Whether those goals are best met with one cloud, a hybrid model, or a multicloud model will depend on your unique situation, dependencies, budget, and available resources. We’ll cover the difference between multicloud and hybrid cloud so you can make an informed next step.

Public Cloud vs Private Cloud?

Hybrid environments combine public and private clouds. And in the case of hybrid IT, it can also include non-cloud environments. Generally, the choice between public and private cloud will come down to how much control businesses want over resources compared to the amount of flexibility they need.

Public cloud providers, such as AWS and Azure, rent out resources to companies in predetermined amounts at a discount, or on a model where you pay for what you use. Businesses have the flexibility to scale up or down their resources on-demand. However, they must navigate and configure the security settings and tools provided by the public cloud provider to ensure optimal security.

Private cloud can run on-premises or offsite with a data center provider. Organizations have significantly more control over configurations and security settings in a private cloud environment. However, scaling resources can be more challenging, and the infrastructure is often more expensive compared to public cloud options. This control and security, combined with the challenges of scalability and cost, make hybrid cloud solutions an attractive option for many businesses.

What is the Difference Between Multicloud and Hybrid Cloud Computing?

In cloud computing, we often hear the terms “multicloud” and hybrid cloud. While both terms sound similar, there are a few key differences organizations tend to overlook. Understanding the differences between these two cloud approaches is essential for organizations that are striving to ensure cloud optimization and meet business needs.

Architecture

A hybrid cloud is the combination of cloud and on-premises infrastructure in a unified framework. It could include public cloud (Microsoft Azure, AWS, etc.) and private cloud infrastructure. Hybrid cloud adoption has increased over the past few years due to its many benefits, which we’ll be covering shortly.

Multicloud computing is the use of multiple public cloud platforms to support business functions. Multicloud deployments can be part of an overall hybrid cloud environment. A hybrid cloud strategy may include multiple clouds, but a multicloud strategy isn’t necessarily hybrid.

Intercloud Workloads

In a multicloud environment, workloads are deployed across different public clouds and often require additional processes and tools for interoperability. Similarly, hybrid cloud environments can include these workloads but also involve movement between cloud and on-premises infrastructures. This flexibility is often necessary for legacy systems with numerous dependencies that cannot be easily migrated to the cloud.

Vendor Lock-in

Vendor lock-in happens when a business feels overly reliant on one cloud provider and finds it difficult to switch to a new provider without significant investment and resources to do so. While both formats may introduce vendor lock-in, this may be more common in hybrid cloud environments where businesses are only using one public cloud provider. In a multicloud configuration, organizations may have more flexibility to move workloads to different public cloud environments.

Pricing

This flexibility in options within a multicloud environment can lead to more competitive pricing for businesses. Public cloud resources can be purchased in discounted packages for predictable workloads, while pay-as-you-go pricing is available for variable workloads.

Availability

With hybrid cloud, availability depends on both the public cloud provider and the on-premises infrastructure in use. In contrast, a multicloud environment can offer higher availability since data and workloads are distributed across multiple public clouds, reducing the risk of downtime.

Data Storage

Data storage has some similarities and differences between cloud environments. In hybrid cloud storage, on-premises storage (private cloud) is combined with public cloud resources. This provides greater control for sensitive data stored on the private cloud, but also requires tools to move data between environments that may be harder to set up compared to multicloud environments. Hybrid cloud can be ideal for businesses that have a mix of sensitive and non-sensitive data, and for those that want greater control over their core infrastructure.

With multicloud storage, data is stored across public cloud providers, which offers greater flexibility and scalability. Although multicloud storage can also be complex to manage, it reduces the risk of vendor lock-in by providing businesses the option to choose between different public cloud providers based on their specific needs and cost considerations. Multicloud is well-suited for businesses that want more scalability and flexibility, and don’t have as many data residency regulation concerns.

Security

In comparing multicloud and hybrid cloud environments, security plays a crucial role. Hybrid cloud setups allow organizations to implement tailored security measures across both public and on-premises infrastructures, providing greater control over sensitive data. In contrast, multicloud environments, which rely on multiple public cloud providers, often have less room for customization. While this can present challenges for specific compliance needs, many public cloud providers still meet essential standards such as GDPR and HIPAA. Ultimately, the choice between the two depends on an organization’s specific security requirements and regulatory obligations.

Flexibility

In terms of flexibility, hybrid cloud environments offer organizations the ability to seamlessly integrate on-premises and public cloud resources. This allows businesses to choose where to host specific workloads based on factors like cost, performance, and compliance. On the other hand, multicloud environments provide flexibility through the use of multiple public cloud providers, enabling organizations to select the best services from each provider.

While both approaches enhance adaptability, hybrid clouds excel in integrating legacy systems, whereas multicloud setups offer diverse options and avoid vendor lock-in, allowing businesses to respond more dynamically to changing needs.

How is Hybrid Cloud Similar to Multicloud?

Despite these differences, hybrid cloud and multicloud share many similarities. They can both be solid frameworks to store sensitive data when configured well, but they can come with common challenges, such as cloud complexity.

Infrastructure Security

Both hybrid and multicloud environments operate on a shared responsibility model, where the level of infrastructure security responsibility may vary. Cloud providers are responsible for securing the underlying infrastructure, while customers must secure their applications, data, and access controls within that infrastructure.

Key responsibilities for businesses include identity and access management (IAM), data encryption, and vulnerability management. Users should have access only to the resources necessary for their roles, whether in public or private clouds. Data must be protected both at rest and in transit, so organizations need to implement proper encryption measures. Regularly scanning for vulnerabilities and applying patches is essential to mitigate risks associated with security weaknesses, including zero-day attacks. By actively managing these responsibilities, organizations can enhance their overall security posture in any cloud environment.

Storing Sensitive Data

Even though public cloud providers offer fewer security customizations for businesses, both hybrid and multicloud environments can be suitable for storing sensitive data. Hybrid cloud gives organizations the power to place their most sensitive information on private infrastructure, whereas multicloud infrastructure allows for redundancy across multiple public cloud providers, mitigating risks from outages and data breaches.

Managing Data

In both multicloud and hybrid cloud, businesses must determine how to manage data across different platforms without compromising accessibility or performance. Hybrid clouds require tools and processes to facilitate data movement between public and private environments. While multicloud setups can simplify data management by leveraging multiple public clouds, they may still necessitate additional configuration to ensure effective data movement between those clouds.

Regulatory Compliance

Different businesses and industries are subject to different regulatory requirements, such as HIPAA, GDPR, CCPA, and PCI-DSS. Most public cloud providers are certified to meet common compliance standards, but if you have very specific needs, you may need to talk with the provider to confirm they can meet your compliance capabilities. Hybrid cloud offers more control over regulatory compliance, allowing businesses to store sensitive data on-premises or in an offsite private cloud.

Cloud Complexity

Cloud complexity is an issue for hybrid and multicloud environments, but what is being managed is where the difference resides. Hybrid cloud involves managing public and private cloud infrastructure. Multicloud involves managing different public cloud provider platforms, APIs, and security settings.

Can a Hybrid Cloud be a Multicloud?

A hybrid cloud can incorporate multicloud elements if it includes multiple cloud environments, such as a combination of public and private clouds. However, multicloud specifically refers to the use of multiple public cloud services from different providers, so it is not accurate to consider all multiclouds as hybrid clouds. While a hybrid cloud may include public clouds, it is distinguished by the integration of on-premises or private cloud resources.

Why Do Companies Use Multicloud?

Companies use multicloud to escape vendor lock-in and improve flexibility and performance across cloud environments. This isn’t a great fit for companies that have legacy frameworks they can’t easily move to the cloud. However, for businesses looking to innovate, multicloud can be a great option.

Why Do Companies Use Hybrid Cloud?

Companies tend to use hybrid cloud when they are either not completely ready to move all of their workloads to the cloud, or when moving some workloads would require more effort than it is worth, but they still want to leverage the benefits of the cloud. Hybrid cloud can serve as a happy medium or a long-term solution for digital transformation in a company, allowing for more innovation and flexibility compared to on-premises frameworks.

Find the Right Cloud Strategy For You with Cloud Experts

Choosing between hybrid cloud and multicloud hinges on your unique business needs. Data sensitivity, scalability, compliance requirements, and budgetary limitations will determine the optimal solution. Need guidance in figuring out what configuration will work best for you? TierPoint’s cloud experts can help you choose the right mix of cloud platforms that will help you reach and exceed your digital transformation goals while keeping your financial constraints and regulatory requirements in mind.

Part of adopting the cloud is convincing your leadership that it’s time to modernize your IT infrastructure. The drivers could be network performance, on-premises data center costs, and more. Read our complimentary eBook to learn how to have those conversations.

We’ll talk about what should be part of modern data center architecture, as well as key considerations for businesses looking to move to a more effective data center.

Key Considerations for Data Center Design

When making decisions about a data center design, organizations should think about scalability, flexibility, power consumption, availability, redundancy, and security of their infrastructure.

Scalability and Flexibility

The design of a data center should include anticipation of future growth. Ensure there is enough space, power, and cooling capacity for additional servers and racks. Modular designs and adaptable layouts can improve flexibility and scalability, and high-density computing can make the most of your square footage.

Power and Cooling Efficiency

Powering equipment and keeping it cool can be a resource-intensive exercise. However, there are ways businesses can optimize and reduce their power consumption, making it more sustainable. By switching to energy-efficient equipment, leveraging renewable energy sources, and implementing strategies such as hot aisle containment to maintain a barrier around hot air exhaust, businesses can improve their power and cooling efficiency.

High Availability and Redundancy

When a data center has high availability and redundancy, the facility ensures continuous operation regardless of interruptions.

Backup generators, redundant power supplies, and copies of critical systems can mean that data centers are only down for a few minutes per year at the most.

Security and Physical Protection

Physical and digital security is vital in data centers. The facility should have access control systems to allow only necessary people into certain parts of the building or applications. Security cameras, fire suppression systems, and intrusion detection tools can help safeguard data and equipment.

What Should Be Included Within a Data Center Design?

When building a data center, the anatomy of the design should incorporate the aforementioned considerations and designed with geography, data sensitivity, performance, and availability in mind.

Building Structure

Every region is prone to certain natural disasters, such as hurricanes, floods, earthquakes, and tornados. A facility’s structure should be reinforced to withstand whatever mother nature brings, especially if it’s more expected in a certain region.

Access Controls and Physical Security

Physical access to data center resources should be restricted and tightly controlled. This can include protocols around access for sensitive areas of a building, use of two-factor authentication, biometric screening, and video surveillance that covers all doors and windows.

Virtual Security

When designing a data center, it’s crucial to include virtual security measures as part of a comprehensive cybersecurity plan. Effective cybersecurity measures are essential to protect data centers from threats and ensure data integrity, and can include:

• Firewalls
• Encryption
• Regular security audits
• Virtual private networks (VPNs)
• Intrusion detection and prevention systems (IDS/IPS)
• Security information and event management (SIEM)

Climate Control and Cooling

Heat, humidity, and static electricity can wreak havoc on data center equipment. Redundant environmental systems can enable continuous operations. Cooling methods also make a big difference in the performance of your equipment. Air cooling blows air on and around equipment, whereas liquid cooling circulates cool liquid to equipment and around the building to absorb heat. After that, the liquid is sent through radiators or cooling towers, providing an efficient way to cool key components.

Building Management Systems

Building management systems can give data center operators a high-level view of all factors of facility health, including HVAC, power loads, and voltage levels. Management systems can also monitor the status of emergency power systems such as uninterruptible power supplies (UPS) and generators.

Power

Diverse and redundant power sources can greatly reduce the chance of power outages affecting the availability of servers. Power distribution units (PDUs) do more than deliver power in a data center. They can also be used to track power consumption and identify voltage fluctuations that may indicate equipment issues.

Data centers can also include UPS as a first line of defense against short-term spikes or drops in power that can greatly hinder availability or damage equipment. Redundant UPS systems offer even higher availability.

Backup generators can be added to provide continuous power during utility power outage events. Facilities can also have additional fuel onsite to keep generators running longer.

Redundancy and Failover

Redundancy and failover add extra safeguards to a data center to boost availability. Duplicating critical components, such as hardware, network connections, and power, improves redundancy. Failover details the process where data centers switch automatically to a backup system when a primary system fails. This can be done by having both systems run simultaneously (active/active), or by having a backup system in place to start when the primary one fails (active/passive).

Environmental Monitoring

Data centers should be monitoring onsite operations as well as the environment. Onsite operations monitoring provides 24x7x365 visibility into possible security threats and elements critical to data center infrastructure performance. Environmental monitoring includes sensors for temperature, humidity, airflow, and power consumption. Detecting environmental issues early can reduce the likelihood of equipment failure.

Cabling, Connectivity, and Networking

Having one or two carriers to choose from can mean businesses may have to sacrifice availability or performance. When data centers are carrier-neutral and offer multiple connectivity options with different carriers, organizations enjoy higher availability, lower latency, greater choice, and improved disaster recovery.

Hybrid and Multi-Cloud Architectures

Is there a need to connect to on-premises infrastructure or form connections between multiple cloud environments? Data center design should consider the interconnectivity needed between different architectures and work to integrate as effectively as possible.

Business Continuity Workspace

Sometimes, a natural disaster or outage can lead to displacement, leaving employees looking for a safe place to work. Data centers can also include business continuity workspaces, allowing employees to set up shop during the recovery process. For example, TierPoint’s data centers have workspace sites that can accommodate up to 800 people.

Modern Data Center Design Strategies

To accommodate larger workloads and meet new demands, modern data centers are being designed with more scalability and agility built in.

Modular and Containerized Designs

Modular data center designs start with pre-fabricated modules that contain IT equipment, power, and cooling. Because the design is modular, it’s easy to add or remove pieces as needed, which makes scaling easy.

Similar to modular design, IT infrastructure can be kept within a containerized unit for rapid deployment. These are not as customizable as modular designs, but if time is of the essence, containerized designs can be the better choice.

High-Density Computing Solutions

High-density computing solutions can fit more computing power into smaller spaces using technology such as blade servers and GPU-accelerated systems. With blade servers, multiple server modules reside in one chassis, sharing power and cooling resources. The shared nature of the system reduces the physical blueprint without compromising on processing power.

Graphics processing units (GPUs) offer significantly higher processing power compared to central processing units (CPUs) and can be a better fit for machine learning and artificial intelligence tasks. High-density data centers are necessary to house GPUs effectively.

Choosing the Right Data Center for Your Needs

The data center design that is right for your business will depend on what data and applications you want in the data center, your tolerance for downtime, natural disasters common to your geographic area, and more. TierPoint’s 40 world-class data centers offer coast-to-coast connection, carrier-neutral connectivity, and hybrid flexibility to suit your business needs.

What Are AI Workloads?

AI workloads can be used to train, execute, and maintain artificial intelligence models. Different types of workloads are used to accomplish different tasks:

• Predictive analytics and forecasting: Customer behavior, maintenance needs, and sales trends can be predicted by training AI models on historical data.
• Natural Language Processing (NLP): Many users are now familiar with NLP – chatbots and virtual assistants use NLP to understand inputs and generate outputs that take after human language.
• Anomaly detection: By training AI on common patterns, this technology can identify unusual events in data sets. This can be used for fraud detection, catching possible cybercrime activity, or pinpointing equipment malfunctions.
• Image or video recognition: Similarly, AI can be used to identify objects, activities, and scenes in images and videos. This technology can be used by healthcare to analyze imaging, or by security systems to recognize faces.
• Recommendation algorithm: AI models can understand which products and services people may need by analyzing past browsing and purchase behaviors.

Even in uncertain economic times, it’s expected that AI workloads will continue to be important. About one-third of respondents on Flexera’s State of Tech Spend 2023 said they expect their AI budgets to increase significantly.

The Data, Compute, and Storage Requirements of AI Workloads

Because AI workloads are capable of so much, their computational requirements are much greater. They require complex computations, massive datasets, and a need to store and scale that greatly surpasses the needs of traditional workloads.

Training AI models requires massive datasets that have millions, or even billions, of data points. This calls for significant computational power. Central processing units (CPUs) typically handle one task at a time. AI workloads rely on parallel processing to break operations into chunks that can be handled simultaneously for faster computations. Graphical processing units (GPUs) excel at parallel processing and are necessary to accelerate AI workloads. The GPU market is on the rise and is expected to more than quadruple by 2029.

In the training phase, AI models need significant resources; however, these needs fluctuate depending on future applications. Storage needs can also ebb and flow. High-performance storage solutions, such as solid-state drives (SSDs), as well as cost-effective object storage, are important for short-term access and long-term archiving of immense amounts of data. 

5 Challenges of Managing AI Workloads

Because of these requirements and more, managing AI workloads in data centers can be difficult if the facility isn’t ready to meet the need. Networking, processing, and scalability features need to be in place for AI workloads to be functional.

Network Requirements

Because AI workloads tend to transfer large amounts of data between storage systems and compute resources, businesses need a solution that offers low latency and high bandwidth. Traditional data centers can be too sluggish to accommodate AI operations.

High Computational Power Needs

As previously mentioned, GPUs and specialized AI accelerators (TPUs) can aid in parallel processing and support AI workloads in a way that traditional data centers with CPUs cannot. More complexity also enters the picture when more diverse hardware resources are added into the mix.   

Real-Time Processing Demands

Real-time processing is already becoming essential for certain AI applications, including autonomous vehicles and fraud detection systems. When it comes to driving, even a split second of delay can lead to catastrophic results. Effective real-time processing requires powerful hardware, efficient data pipelines, and optimized software frameworks.

Massive Data Processing Requirements

Data centers need to be able to process the data used by AI models and meet storage, cleaning, and pre-processing requirements. What happens to the data throughout its lifecycle? Data centers need to manage the archival, deletion, and anonymizing of data as well. All touchpoints along the data’s lifecycle add layers of complexity to the process.

Scalability and Flexibility Constraints

Traditional data centers don’t tend to offer as much flexibility or scalability, making it more difficult for businesses to change resources to meet fluctuating needs. Training can require significant resources, while deployment may vary in its demands. Rigidity can slow down or stop the effectiveness of AI workloads.

Can High-Density Computing Support and Optimize AI Workloads? 

High-density computing (HDC) is a good fit for organizations looking to support and optimize AI workloads. As the name suggests, HDC can fit more processing power into a smaller footprint, leading to the following benefits.

Stronger Compute Density

A stronger compute density equals stronger processing power in a limited space, which can enable AI workloads to handle massive data sets and complex algorithms necessary for both training and execution.

Decreased Latency

When resources are packed more tightly together, travel distances are also decreased between components. In HDC environments, latency goes down due to the minimization of travel time. Latency is hugely important for real-time applications that require instantaneous responses.

Better Scalability

Because more resources can be packed into one rack, HDC is also great for scalability. New computing units can be added to existing racks and meet increased processing needs. Scaling down is just as easy.

Improved Resource Utilization

High-density computing offers a smaller space solution for businesses employing AI workloads, and the smaller footprint also promotes better resource utilization. Hardware is used more efficiently and organizations enjoy less wasted space. Data center power density can also be improved.

More Specialized Configurations

Different AI workloads have distinct needs. For example – latency may be more important in one workload, and scalability may be more important in another. HDC allows businesses to create highly customized configurations that meet the needs of specific AI workloads. This could look like high numbers of GPUs or more AI accelerators.

What Other Techniques Can Be Used to Better Support AI Workloads?

While high-density computing expands your ability to handle demanding AI workloads, some other approaches can be used to support and foster AI projects.

Integrate High-Performance Computing Solutions  

Processing power is vital for AI workloads, and good computing density is just the start. High-performance computing (HPC) solutions should also be incorporated. This could include high core count CPUs, GPUs, and AI accelerators called tensor processing units (TPUs). CPUs are good for raw processing, GPUs work best with parallel processing, and TPUs are perfect for machine learning tasks.

Optimize Data Storage and Management

AI models require huge datasets for training, so optimizing storage and management is important to keep operations efficient after deployment. Solid-state drives (SSDs) have fast read/write speeds, so their performance can be optimal for frequently accessed data. Object storage can archive less frequently accessed data.

Implement Efficient Networking

Technologies like Ethernet fabrics can offer higher bandwidth and lower latency than traditional data center networks. Moving between storage, compute resources, and even edge devices at high speeds is essential for AI workloads. Businesses may also consider adding network segmentation and traffic prioritization to direct data flow more efficiently and optimize networking.

Leverage Parallelization and Distributed Computing

Parallelization breaks AI tasks into subtasks and assigns them to multiple computing units. This can accelerate workloads by multiplying efforts. Containerization can also enhance this process further by packaging subtasks with their dependencies, simplifying deployment and enabling consistent execution.

Use Efficient Cooling Systems

Any computing generates heat, but high-performance computing and AI workloads generate significantly more heat than traditional workloads. Effective cooling systems can help you maintain optimal temperatures, reducing the likelihood of equipment breakdown or malfunction. Closed-loop liquid cooling can offer energy-efficient heat dissipation that keeps up with demanding computing.

Incorporate Cloud Solutions

Cloud computing adds the flexibility and scalability required for modern workloads. Businesses can access cloud GPUs on-demand from cloud providers for workloads that need greater-than-average processing power. This can be a more cost-effective alternative to maintaining your own GPU infrastructure in a data center.

Unlock the Full Potential of Your AI Workloads

Don’t let technological limitations handcuff your AI workload potential. By employing high-density computing, optimized data storage solutions, effective cooling systems, and more, your organization can take advantage of current AI capabilities and prepare for future developments.

If you feel limited by your current data center situation, TierPoint’s High-Density Colocation services could be your next move. These facilities are designed with AI in mind, ready to accommodate your high-performance workloads.

Learn more about our data center services and business applications of AI and machine learning.

What is Data Center Sustainability?

Data center sustainability encompasses different practices and approaches facilities can take to reduce the impact of data centers. When considering and implementing sustainable business practices, data centers also need to keep security and reliability concerns in the mix.

Why is Data Center Sustainability Important?

Data center sustainability is important for several reasons. Data centers consume a lot of energy every year, and this number is on the rise due to the demands from AI services. Overall data center consumption in the U.S. is expected to reach 35 Gigawatts by 2030, more than doubling since 2022. Focusing on sustainability at data centers can help reduce their environmental impact, save money, conserve resources, and position a business as more environmentally conscious. In some cases, it may be important to focus on sustainability for regulatory reasons as well.

Key Aspects of a Strong Data Center Sustainability Approach

Data sustainability can best be achieved through a multi-pronged approach of innovative optimizations and energy-efficient techniques.

Innovative Cooling Techniques

Liquid cooling offers a more efficient way to cool your equipment compared to traditional air cooling. Liquids are better conductors of heat, so they can absorb heat more effectively, similar to water pipes compared to fans. Liquid cooling can also bring coolant directly to the source of the heat, which may be components like processors or graphics cards. The more precise the delivery, the more effective the cooling, and the less energy is wasted.

Energy Efficiency

While data centers can optimize energy use via liquid cooling, they can also implement energy-efficient servers, renewable energy sources, and smart power management strategies to save even more.

Resource Optimization and Waste Reduction

Other methods of resource optimization and waste reduction can include:

• Consolidation of underutilized servers via virtualization technologies
• Regulating power usage of servers based on current workloads
• Separating hot air exhaust and cold air intake with a hot aisle/cold aisle containment strategy
• Optimizing storage using tiers, prioritizing faster storage for the most frequently accessed data
• Use heat from the data center to heat the buildings or provide hot water for the facility
• Managing e-waste for decommissioned equipment responsibly

Monitoring and Measurement

It’s easier to improve your sustainability when you have ongoing monitoring in the data center of:

• Power consumption
• Water usage
• Humidity
• Other environmental factors

Knowing which resources are being wasted (i.e. water due to a leakage), impacting or using the most energy can help you chart a course for future sustainability initiatives.

Data Center Design

Hot aisle/cold aisle containment is one design decision you may choose to make to improve sustainability via data center design. Adding natural ventilation, using energy-efficient building materials, and optimizing airflow in server racks can all contribute to a more sustainable data center.

Internal Culture of Sustainability

One way to ensure a data center will continue to become more sustainable is through fostering a culture of sustainability throughout the organization. This can include employee training, brainstorming on sustainability initiatives, and reiterating the shared responsibility at the business to lessen environmental impact.

How Do You Measure Data Center Sustainability?

More than one metric is necessary to get a clear view of data center sustainability.

Power usage effectiveness (PUE) is measured by dividing total facility energy consumption by IT equipment energy consumption. Lower PUEs, closer to 1, means the data center is more efficient.

Similar to PUE, carbon usage effectiveness (CUE) evaluates data center energy efficiency by measuring carbon emissions that are generated from one unit of IT energy. Water usage effectiveness (WUE) measures the efficiency of water used for cooling purposes.

Other metrics data centers might use include renewable energy use, a percentage of energy consumption coming from renewable sources, and material use, such as the responsible management of electronic waste from decommissioned equipment. The metrics a data center chooses to track may depend on the efficiencies they’re hoping to make.

Can AI Be Used to Help Improve Data Center Sustainability?

Artificial intelligence (AI) can be used to improve data center sustainability. How? By employing the right AI tools, businesses can work to reduce their data center footprint.

Real-Time Monitoring and Analysis

AI can amplify manual efforts by continuously monitoring sensor data for temperature, power consumption, and other environmental changes. This can help spot inefficiencies and address issues quicker.

Predictive Cooling and Power Management

AI can predict cooling needs and adjust cooling systems proactively by learning from historical patterns and combining that with real-time usage and outside sources such as weather forecasts. AI can also predict future power demands and change consumption based on how workloads normally fluctuate.

Proactive Maintenance

Equipment that needs minor maintenance is much better than equipment that needs an overhaul. AI tools can take sensor data on equipment to predict potential equipment failures. By enabling preventative maintenance, data centers experience less downtime and less energy waste.

Server Provisioning and Virtualization

Automating provisioning and right-sizing servers with AI means that servers meet workload demands but don’t exceed them. When done effectively, energy consumption goes down and there’s less of a need for additional virtualization.

Workload and Data Placement Optimization

AI is much more effective at identifying the most efficient servers for certain workloads and consolidating lightly used servers on the fly. Proper workload distribution and data placement also minimize energy consumption.

Data-Driven Decision Making and Continuous Improvement

Because AI can analyze vast amounts of data in a fraction of the time it would take for a human to do the same thing, data-driven decision-making can be far more precise. Data centers are empowered with a richer view of their environment and can make continuous improvements toward more sustainable configurations.

Building a More Sustainable IT Strategy

Whether you are operating out of your own data center, or you’re looking to make your equipment in a facility you rent more energy-efficient, there are steps you can take to improve your sustainability today. If you need help building a more sustainable IT strategy, you can contact our team of experts today.

What is Data Center Modernization?

Data center modernization involves updating the data center infrastructure and processes to make it more secure, agile, and efficient in an ever-evolving digital landscape. Digital transformation is a top priority for IT leaders – 74% of Flexera 2023 Tech Spend Pulse respondents said it was a priority IT initiative, only topped by cloud/cloud migration (75%) and cybersecurity (76%).

Why is Modernization Crucial?

Modernized data centers are designed to adapt to the future advancements and trends IT leaders have on their priority lists. When data centers are up-to-date, they can help organizations stay ahead of the curve and minimize unnecessary technical debt.

Instead of being added after the fact, modernized data centers start with flexibility and scalability in mind. Data storage, processing, and artificial intelligence/machine learning (AI/ML) needs can be more easily accommodated with a modernized data center.

Benefits of Data Center Modernization 

Organizations can benefit from modernizing their data centers in ways similar to how individuals can benefit from upgrading their tech. Staying focused on the future can improve security measures and subsequent compliance, provide better support for more modern workloads, and improve operations, to name a few perks.

Enhanced Security and Compliance

Newer data center setups can offer more advanced security features, including automated security measures, to provide better safeguards for data and systems. Businesses can stay one step ahead of attackers with intrusion detection, automated patching, and encryption incorporated into modernized infrastructure.

Older data centers can have disparate parts that may not all meet industry regulations, whereas more modern infrastructure is often easier to keep compliant with data privacy laws and other regulations.

Better Support for Complex and HPC Workloads

Workloads are getting bigger and more complex. High-performance computing market revenue is predicted to reach $40.39 billion by 2026, up from just under $30 billion in 2021. New hardware equipped with faster processors and increased memory capacity is becoming more prevalent, as are more efficient storage solutions. Modernized data centers are necessary to handle complex tasks such as artificial intelligence, HPC, and big data analytics.

Increased Business Agility and Innovation

While traditional data centers tend to be more static in their infrastructure, modern data centers are more scalable and flexible with the help of cloud integration and virtualized resources. Instead of waiting to spin up new physical resources, a modernized data center can scale to meet demand quickly, cutting down on friction between need and fulfillment.

Elevated Performance and Reliability

Users in all industries expect predictable, reliable performance from the services they use. Businesses need to meet and exceed expectations around performance and reliability, and this is best facilitated by modernized data centers that leverage cutting-edge technologies. When end users receive a more consistent experience, satisfaction and retention rates improve.

Improved Operational Efficiency

Many tasks in data centers can now be automated, freeing up time for IT team members to focus on more strategic projects. By automating where you can, you can reduce the time spent on certain tasks, reduce the likelihood of human error, and boost operational efficiency.

Reduced Operational Costs

One way businesses can reduce operational costs through modernization projects is through automation, but there are other cost savings organizations can see in the long term. Modern data center architectures can be more energy-efficient and resource-optimized. Plus, they tend to require less maintenance because they contain newer components. While modernization can include some upfront costs, over time, it can lead to lower operational expenses.

Easier Integration with Cloud and Hybrid Environments

Cloud environments and modernized data centers are meant to work together seamlessly. 89% of organizations use multicloud environments, with 73% employing hybrid cloud environments.

To leverage on-premises and cloud-based resources and integrate them most effectively, a modernized data center is key.

Is It Time to Modernize Your Data Center?

If your business is facing one or more of these problems, it may be time to update your data center to take advantage of the benefits modernization can bring.

• Performance hiccups: Slow processing speeds, frequent system crashes, and slower-than-normal applications can mean your infrastructure isn’t able to keep up with your current (and future) data processing needs.
• Scalability stoppages: If your business experiences a surge in demand, are you able to meet the need? Are you paying too much for underutilized resources? Limitations in scalability can indicate a need for modernized data centers.
• Security worries: Legacy systems can be more prone to cyberattacks. As cybercrime becomes more sophisticated, it’s important to adopt a modernized solution that comes with automated patching and other advanced security features.
• Maintenance costs: While maintenance will always be a regular cost for data centers, frequent maintenance of old systems can become a pricy endeavor. If you feel like you’re always fixing something, it may be time to modernize.
• Compliance challenges: Whether your organization is looking to maintain compliance with industry standards or meet requirements for cyber insurance, modern data centers tend to have more controls in place and greater capacity to meet regulatory requirements compared to legacy facilities. If you find it difficult to comply, no matter what you update, an overhaul may be necessary.
• Integration struggles: Do you feel like you can’t use cloud resources to their greatest potential because you aren’t able to integrate your data center with the cloud? Modernized data centers are built for integration, making hybrid environments easier to achieve.

Is Your Data Center Holding You Back? 

Don’t let your current data center stand in the way of future innovation. Work with an infrastructure that works for you. TierPoint’s data center services are here for you when it’s time to modernize. We offer tighter security, strong uptime and availability, and an easier path to multicloud connectivity.

What is Sustainable High Performance Computing? 

Sustainable HPC aims to deliver massive computational power synonymous with HPC systems while keeping an eye on the environmental impact of these systems. Instead of focusing solely on performance, sustainable HPC takes a multifaceted approach to the lifespan of the system, from the design of the infrastructure to how resources are used, and ultimately disposed of or recycled at the end of life.

7 Ways to Advance Sustainable High Performance Computing

While sustainability and high-performance computing may not be entirely compatible, there are opportunities businesses can take to become more energy-efficient.

Leverage Environmental Monitoring

With environmental monitoring, businesses collect and look at all physical aspects of a data center environment, including humidity, temperature, airflow, and power consumption. This data can be used to identify opportunities for more energy-efficient configurations.

Energy-Efficient Hardware and Cooling Systems

Businesses can minimize energy consumption using energy-efficient hardware components and advanced cooling technologies. Liquid cooling in data centers is more efficient than air cooling because water absorbs and transfers heat more efficiently. This means components can remain cooler at higher loads using water or coolants instead of air. Exchanging hardware for more energy-efficient equipment can be as simple as buying something newer.

Renewable and Alternative Energy Integration

Depending on the data center your organization is using, the facility may integrate renewable energy sources into its power infrastructure, such as solar or wind power. These shifts can add up to big differences over time.  In addition, some data centers are taking advantage of alternative energy sources like on-site fuel cells, which have much lower emissions than traditional sources of energy.

Heat Reuse and Waste Heat Management

Because HPC involves a lot of operating power, it also generates a lot of heat. Businesses can implement innovative solutions for reusing heat generated by HPC systems, including district heating or greenhouse climate control.

With district heating networks, captured heat is used to warm nearby buildings. This can lower your carbon footprint and reliance on fossil fuels. Hot water can be sent from a data center to other homes and buildings through insulated pipes.

Greenhouse climate control involves taking heat generated from HPC systems and sharing it with greenhouses to maintain ideal temperature and humidity levels for the plants inside. These are win-win solutions for data centers and surrounding communities. 

Workload Optimization and Resource Management

Organizations can also minimize data center energy waste by efficiently utilizing resources and optimizing HPC workloads. Workload scheduling, load balancing, virtualization, and containerization can all aid in consolidating resources.

Scheduling tools can assign tasks to computing resources based on when the most efficient time would be to execute them. By waiting for other demanding tasks to be completed, scheduling HPC workloads can avoid unnecessary competition for resources. Load balancing takes workloads and distributes them evenly across available resources. By doing this, no one server is overloaded with work, and no servers are left idle.

Virtualization allows multiple virtual machines (VMs) to be run on one server. Each VM operates as its own device with an operating system and applications. This makes allocating resources to each workload more effective, and enables greater scalability for servers based on demand. Containerization can package and deploy applications using the shared operating system of a host machine, taking virtualization even further. Server utilization and resource allocation can be even more well-defined with containerization.

Cloud-Based HPC Solutions

Cloud-based HPC solutions can bring on-demand resource scaling, and potentially lower energy consumption, to your data center compared to on-premises deployment. These cloud-based services can allow a team to burst processing power without provisioning additional physical equipment. Teams won’t have to maintain underused hardware or worry about the energy costs associated with idle resources.

Responsible Disposal, Recycling, and Reuse of HPC Materials

The materials used in HPC systems can be hazardous if not disposed of properly. So, data centers should consider the system’s complete lifecycle and how to extend the lifespan of certain components.

Some manufacturers offer take back programs for electronic components, which can make recycling easier. Businesses may also want to consider partnering with specific electronics recyclers to form a regular practice around responsible disposal and recycling of HPC materials.

Upgrading certain components can be a cost-effective and resource-efficient method to extend your system’s lifespan. Refurbishing retired components or upgrading memory or storage can bring new life to your HPC data center, without replacing more resource-intensive parts of the environment.

Can Artificial Intelligence and Machine Learning Help Optimize Sustainable HPC?

Artificial intelligence and machine learning (AI/ML) workloads are often part of HPC systems. These technologies require a massive amount of resources, but they could also be used to reimagine data center sustainability.

By analyzing sensor data and system logs, AI and ML models can predict potential hardware failures or performance degradation in HPC systems. This enables proactive data center maintenance and preventive actions, reducing downtime and extending the lifespan of infrastructure.

AI/ML enhancements could also be used to monitor resources and identify usage trends to discover new efficiency opportunities. AI/ML tools can help integrate previously disparate workloads, finding efficiencies that decrease overall resource usage.

On the software level, AI/ML can help with data management and developer tools, plus allow for more efficient queueing. It’s easy to think of AI/ML as drains of energy, but they can be implemented in ways that are more sustainable. For example, one way businesses may be able to accomplish this is through the scaling of AI/ML workloads on high-bandwidth, low-latency GPUs that run jobs more quickly compared to CPUs, allowing for greater resource allocation for other compute tasks.

Embracing the Future of Sustainable High Performance Computing

As you begin to research and identify opportunities to help make your HPC initiatives more sustainable, TierPoint is here to help. Our team is familiar with finding a balance between delivering excellent performance while doing what is possible to help minimize the environmental impact of these workloads. Reach out today to learn more.

In the meantime, download our whitepaper to discover additional ways AI/ML can be used to improve business processes and operations.

It’s clear based on these trends that data centers are preparing for massive data use, but capacity planning is also important for individual businesses. Here’s what your organization should know about data capacity planning and how to prepare for your future data needs.

What is Data Center Capacity Planning?

Data centers need to be able to meet both current and future demands. Data center capacity planning keeps the future in mind by strategically managing and planning for future needs of data center infrastructure.

How is Data Center Capacity Measured?

Data center capacity can’t be calculated with just one measurement. Multiple figures are combined to determine capacity, including space, power, cooling, computing resources, and network connectivity.

What Are the Capacity Components of Data Centers?

When each of these capacity components is well-understood, they can be used in combination to pinpoint your business needs.

Power

Servers, storage systems, and network equipment all need a constant and reliable power supply to function properly. Data centers have a primary power source and typically more than one source of backup power, such as uninterruptible power supplies (UPS) and generators. Looking at power in terms of data center capacity also involves how the power is distributed throughout the facility.

Cooling

Robust cooling systems are also necessary to keep data center components cool and working without breaking. Air conditioning systems, airflow management, and liquid cooling can all be used to keep temperatures low in the building and with specific equipment.

Data Storage

Data can be stored in massive amounts in data centers via hard disk drives (HDDs), solid-state drives (SSDs), and tape libraries. HDDs are used for long-term storage, whereas SSDs are typically used for data that requires quick access. Tape libraries can be added for archival records.

Network Connectivity

The network connectivity in a data center determines the speed at which information is exchanged. Facilities need high-bandwidth connections to allow for communications between servers and storage systems, as well as external network connections that go to private networks or the internet.

Physical Space

Making the most of a data center means making the most of the space, including with optimized server racks and hot aisle/cold aisle containment. The more businesses optimize their footprint, the more room they have to expand in the future.

Disaster Recovery

Disaster recovery isn’t necessarily a traditional capacity component. However, it is important for improving data center resilience, and businesses need to ensure they have sufficient resources to support DR initiatives. This can include power and cooling redundancies, backup sites, and data replication measures.

Why Does Data Center Capacity Planning Matter?

Capacity planning is important largely due to the exponential growth of data and subsequent demands placed on data center infrastructure as a result of this growth. Total capacity for data centers is set to at least double in most regions, with the U.S. development pipeline set to increase capacity 2.5 times over to meet these demands. Surges in data requirements mean a need for more processing power, network bandwidth, and storage capacity.

High-density colocation facilities are also becoming more popular. These facilities concentrate power and cooling in concentrated spaces to support HPC (high performance computing) demands. This places a greater emphasis on the importance of capacity planning to ensure power and cooling distribution are implemented efficiently with an eye on growth.

Underestimating capacity needs can lead to risks, including:

• Downtime
• Performance issues
• Increased costs from hastily provisioned resources

Overprovisioning also leads to problems, such as:

• Wasted resources
• Energy overconsumption
• Reduced scalability
• Agility issues

Key Considerations for Data Center Capacity Planning

Capacity planning isn’t solely about additional capacity in all circumstances. The clearer your picture is on current usage and what capacity you’ll need to support future strategic business initiatives, the more you can balance the performance and costs of your system and focus your attention on emerging technologies that can meet your workload demands.

Understanding Required Capacity

Determinations of capacity can be done at a rack level, row level, and room level:

• Rack-level capacity: How much power, cooling, and space will you need on a single-rack basis?
• Row-level capacity: What does this look like when expanded out to a row of servers?
• Room-level capacity: What does the overall infrastructure look like, and what will it require?

Current and Future Strategic Business Initiatives

Your overall goals and IT strategy should be aligned with your capacity planning. Application roadmaps that include planned deployments and upgrades can help you calculate what might impact data center resource requirements in the near future.

Business Growth Projections and Scalability Requirements

Capacity planning needs to start with the current state of the business and factor in growth projections and scalability needs for the next several years. This can include data storage growth, user base expansion, and additional power, compute, and cooling resources necessary to facilitate growth.

Finding a Balance Between Performance and Costs

Maximizing performance is important for a great user experience and retention rates; however, most businesses are not working with unlimited budgets. Performance needs to be considered alongside costs. Chances are, some workloads will have more vital performance needs than others.

Emerging Technologies and Workload Demands

Emerging technologies often have much greater capacity demands compared to current workloads. Factor in the potential of emerging technologies, such as AI and ML, and what they might mean for your processing power and storage needs in the coming years. Containerized applications and Internet of Things (IoT) devices can also introduce new resource demands.

Capacity Planning Methodologies and Best Practices

When it comes time to engage in data center capacity planning, there are a few approaches you can take and best practices you should consider following.

Top-Down vs. Bottom-Up Capacity Planning Approaches

You may decide to start with either a top-down or a bottom-up capacity planning approach. With top-down, you start with the organization’s overarching IT strategy and business goals and plan the necessary resources from there. What do you project your users, data storage needs, and application usage will look like in the years to come?

A bottom-up approach starts by looking at the existing data center resources, taking stock of all equipment and utilization rates, and planning to eliminate bottlenecks. Employing both approaches will be the most helpful at giving you a comprehensive picture of what’s now and what’s next.

Leveraging Data Center Infrastructure Management (DCIM) Tools

Data center infrastructure management (DCIM) tools can streamline capacity planning by:

• Visualizing your data center layout, allowing you to see where capacity exists for future projects
• Tracking and managing your inventory of current assets
• Monitoring utilization in real-time of power, cooling, and network usage
• Simulating the impact of new equipment deployment on capacity utilization at the data center

Data center managers can use this information to make better plans for the present and future of your capacity needs.

Establishing Capacity Planning Governance and Processes

Because it’s impossible to predict the future with complete accuracy, it’s important to have checks and balances in place for potential overages and point people for the capacity planning process. Develop a governance structure that defines capacity planning roles and responsibilities, sets thresholds and triggers for capacity, and sets an intention for regular policy review.

Utilizing Predictive Analytics and Forecasting

Predictive analytics can use historical data to predict future growth for your resource usage. Even if your business doesn’t have historical data, predictive analytics can build on industry business trends to make informed predictions about future capacity needs. This places you in a proactive, instead of reactive, position.

Performing Continuous Monitoring and Optimization of Plans

Continuous monitoring and adjustments are necessary to optimize data center capacity planning. You can flag problems early and adjust as you go along to make your capacity plans even more effective over time.

Implementing Effective Data Center Capacity Planning Strategies

It’s easier said than done to implement effective capacity planning strategies in a data center. Working with a partner who’s seen it all and planned for it all can make a huge difference. Learn more about TierPoint’s data center services and how we can get you set up for now and later.

What is Network Optimization?

The goal of network optimization is to allow data to flow smoothly and efficiently across your network. When businesses engage in network optimization, they are applying processes to improve the reliability and performance of their networks to meet the growing and changing demands of applications and users alike.

Types of Network Optimization

There are three main types of network optimization – technical, strategic, and operational.

• Technical optimization: Hardware upgrades, traffic shaping, and bandwidth allocation are all technical aspects of a network that could be improved.
• Strategic optimization: What are you trying to achieve? By applying strategic optimization, you start with your goals in mind and work backwards to design your network to meet these objectives. These might include goals for network performance, business needs related to network traffic, and security and efficiency goals that could be solved with network segmentation.
• Operational optimization: Sometimes, it’s less about the hardware and more about the tools and techniques used to streamline tasks and automations. Performance monitoring, automation scripts, and configuration management can all be part of operational optimization that improves network health and reduces the manual tasks IT staff need to perform to keep the network running smoothly.

Why is it Important to Optimize Your Network?

Your network serves as your traffic command center for users and internal teams. A well-optimized network can greatly improve the user experience and the productivity of your workforce.

An optimized network can improve productivity and user experience. Without it, users can struggle with slow loading times, frequent outages, or application delays that can result in frustration or even abandoning the tools altogether.

Network performance optimization can also increase efficiency through techniques like traffic shaping and network segmentation. The end result is a more streamlined operation, boosting the value from your existing network infrastructure and improving performance. Efficiencies also reduce costs by increasing productivity, reducing downtime, and making the most of current resources.

Often, when businesses evaluate their network performance, optimization processes also call for revisiting access controls and security protocols. Well-optimized networks are often more secure than ones that haven’t been assessed in a while.

Optimizing networks can also enable scalability and allow for future-proofing. Networks can accommodate increasing workloads and bandwidth requirements when they’re optimized.

Is it Time to Perform Network Optimization?

Are you relatively happy with your network performance? Are issues starting to creep in? Here’s how you can determine whether it’s time to perform network optimization measures.

Are You Experiencing Network Performance Issues?

You may experience one or several symptoms indicative of a need to optimize your networks. Application slowdowns, lagging user experience, increased latency, data transfer delays, and network congestion can all be symptoms of a bigger problem. These can be signs of bandwidth limitations, network congestion, or less-than-ideal resource allocation.

Are Your Workloads Growing?

As your workload sizes increase, bandwidth demands will also grow. High-performance computing, such as artificial intelligence and machine learning (AI/ML), are more demanding workloads that require substantial bandwidth. If you are starting to incorporate these technologies into your infrastructure, you need to ensure that your network can meet increased demands.

Tips and Strategies for Network Optimization

Network optimization can be done by employing one or more strategies to improve performance, increase efficiencies, and decrease roadblocks and latency.

Use Network Performance Monitoring Tools

To ensure optimal network performance and proactively identify potential issues, leveraging dedicated network performance monitoring tools is essential. These tools provide real-time visibility into your network’s health, allowing you to pinpoint bottlenecks or underlying problems that could be degrading the end-user experience.

By tapping into hypervisor or hyperscaler integrated reporting metrics, as well as log analytics and agent-based monitoring systems, you can gain comprehensive insights into your network’s behavior. Additionally, vendor-integrated monitoring solutions and protocols like syslog and NetFlow can offer granular data on network traffic patterns and resource utilization. With these powerful monitoring and optimization tools at your disposal, you can stay ahead of performance challenges and ensure your network consistently delivers the level of service your business demands.

Perform Traffic Prioritization and Shaping

Not all traffic needs to be at the front of the queue. Quality of service (QoS) prioritizes applications based on importance, controlling traffic and ensuring the urgent workloads operate at their peak. This could mean that financial transactions are prioritized over a task such as bulk data transfers. Traffic shaping can build on QoS by aligning bandwidth allocation to current priorities.

Implement Network Segmentation

One large network can pose a greater security risk than one that is segmented. Dividing your network into smaller segments can also improve performance. You may consider separating guest traffic from internal workloads, or separate high-bandwidth activities from the rest of your processes.

Enable Load Balancing

Having all traffic relegated to one area can place a strain on your network. Consider enabling load balancing instead. You can distribute network traffic across several connections or servers to avoid overloading individual resources. This can optimize application performance and provide a better end-user experience.

Research and Leverage SD-WAN Solutions

Software-defined Wide Area Networks (SD-WAN) can connect local area networks (LANs) using software-defined networking (SDN) principles. These then become wide-area networks (WAN), where networks are connected across geographically dispersed locations.

With SD-WAN, businesses can apply intelligent traffic routing, as well as application awareness, to their networks and streamline their WAN connectivity, improving performance. Approximately 43% of respondents to LiveAction’s 2024 Network Performance Monitoring Trends Report are managing either WAN or SD-WAN networks.

Deploy Software-Defined Networking

With SDN, the control plane is centralized, allowing for more programmatic configuration and automation of networks. While this involves additional configuration, SDN can make networks more flexible and simple to manage and optimize.

Leverage Content Delivery Networks

Content delivery networks (CDNs) can improve performance for geographically distributed users or applications that may be content-heavy. CDNs store content, not just in one place, but on dispersed servers, to reduce latency for users and improve their experience when accessing data.

Update Your Network Design and Hardware

When network hardware is outdated, it can create a drag on performance. Upgrade old hardware and revisit your network design to better align with network needs. Network modernization can also optimize your data flow. A choice that was once efficient may prove inefficient over time as more gets added to the network.

Adopt Network Automation and Orchestration Techniques

The more you are able to automate manual tasks, the more time you will free up for your IT teams, allowing them to focus on more strategic, interesting initiatives. Automation can also reduce the risk of manual errors and improve the efficiency of operating your network. According to LiveAction, many organizations are still overly reliant on manual processes and could stand to benefit from automation technology.

Building a Continuous Network Performance Monitoring Plan

As a best practice to keep your network performance running properly, businesses should build an ongoing plan to monitor and optimize their networks. If you’re not sure what to include in your plan, or you’re looking for a partner to work alongside your team, TierPoint is here to help. Learn more about our IT Network Services and chat with our experts today.

Below, we discuss what a data center fabric is, its components and types, and things to consider before using this approach in your business.

What is a Data Center Fabric?

A data center fabric consists of high-speed network connections that link storage devices and servers within a data center. The design of a data center fabric is meant to be scalable and flexible to overcome limitations and challenges associated with traditional data center network architectures.

How Does a Data Center Fabric Work?

Data can travel between different parts of the data center efficiently using switches, mesh-like connections, and a unified network. Switches connect together to route the network, mesh-like connections create multiple data paths between devices, and altogether, the pieces work as part of a unified network.

Different Types of Data Center Fabric Architectures

With increasing demand for fabric switches driving the market, there are a few different types of data center fabric implementations that you may choose, which all come with important advantages and considerations.

Spine-Leaf Topologies

Spine Leaf architecture generally uses two layers of switches. Leaf switches connect to storage devices and servers at the edge of the network and are sometimes called ToR (top-of-rack) switches. Spine switches interconnect the leaf switches, creating the mesh. This form of topology is generally easy to manage, simple to scale, and good for high-performance architecture.

Traditional Three-Tier Architecture vs. Modern Fabric Architectures

As the name would suggest, a traditional three-tier architecture is an older network design with three layers. The access layer is where switches are connected to devices, such as servers, directly. The aggregation layer takes the aggregate traffic from the access layer and switches it. The core layer routes traffic between different subnets and serves as a central hub.

Comparatively, modern fabric architecture has more interconnected switches instead of a core layer. This allows for lower latency for east-west traffic, improved flexibility, and better scalability. Modern approaches remove common bottleneck issues and improve management.

Cloud-Ready Fabrics for Hybrid and Multicloud Environments

Modern data fabrics are not only easier to configure, they’re also cloud-ready for hybrid and multicloud environments. Cloud computing trends, such as AI-as-a-Service, real-time cloud, and the Internet of Things (IoT) are making cloud environments even more popular.

Fabrics can use technologies such as virtual extensible LAN (VXLAN) encapsulation and Ethernet VPN (EVPN) to make it easier for servers to be associated with each other in a data center without a physical connection, simplifying the management of multicloud and hybrid deployments.

Components of Data Center Fabric

To operate efficiently, data center fabric architectures rely on the following components.

Network Virtualization and Software-Defined Networking

While traditional networks can be rigid, network virtualization offers a rearrangeable maze for organizations to create multiple virtual networks on top of the same consistent physical network infrastructure. Software-defined networking (SDN) separates the control plane from the data plane, so the directions for the network and the flow of data can be managed with more flexibility.

These components are used together in data center fabrics to provision resources as needed, isolate network segments for certain workloads, and automate configuration tasks.

Fabric Switching and Routing Technologies

Switches are some of the most important pieces of data center fabrics because they allow for the multiple data paths that make fabrics great for performance. Even in moments of congestion or switch failure, fabric switching allows for lossless forwarding. Fabric routing determines which path, depending on available switches and latency concerns, will be the best to move data across the fabric.

Converged Infrastructure and Hyperconvergence

Converged infrastructure combines data center equipment into a single system, instead of having servers, storage, and networking arranged separately. These pre-built arrangements can be taken further via hyperconvergence, integrating the functionality of a combined system into software that runs on a standard server.

Understanding the Benefits of a Data Center Fabric

When organizations implement a data center fabric, they can enjoy simplified management, better performance and efficiency, and greater agility for scaling workloads later on. Networks can be reconfigured to meet changing needs quickly. High-bandwidth connectivity and multiple data paths decrease latency issues significantly. Data fabric architecture is also cloud-ready and allows for greater isolation of workloads, improving security.

Challenges and Considerations in Data Center Fabric Adoption

Before adopting a data center fabric configuration, businesses should consider the following:

• Integration with legacy systems: Existing data center infrastructure might not be immediately compatible with legacy frameworks. Doing so properly may require careful planning.
• Security/compliance concerns: While data center fabrics can improve security through isolation, they can also increase the number of potential attack surfaces. Carefully configure and engage in ongoing monitoring to ensure your environment is compliant with relevant security standards.
• Skill development: Teams need to be well-versed in software-defined networking, network virtualization, and fabric-specific protocols to create an effective framework. This may call for additional training or bringing in outside expertise
• Design considerations: Optimal performance depends on understanding traffic patterns, redundancy needs, and future workload growth projections. Design a fabric with the present and future in mind.

Is a Data Center Fabric the Right Choice?

Whether a data center fabric is the right choice for your business can depend on multiple factors. However, the following are signs that it may be time to consider adoption.

Greater Need for Scalability and Rapid Deployment

A fabric needs to support quick provisioning and scaling of new applications. A significant benefit that businesses get out of data center fabrics is the ability to scale new applications and virtualized workloads. AI/ML technologies put intense demands on computing, often requiring GPUs over CPUs. Scalability is necessary to support new technologies and enable further growth.

Increasing Requirements for High-Speed Data Transfer and Low Latency

Cloud computing, big data analytics, and real-time apps have little to no tolerance for latency. When the technology you’re using and the tools you’re building call for high performance, data center fabrics can deliver the speed you need.

Growing Security Concerns

On a global scale, ransomware is the “most immediate threat” we face, and small businesses are at just as much of a risk as larger ones. Data center fabrics can enhance network security and protect against these threats with features such as micro-segmentation.

Interest in Embracing More Cloud Benefits

Businesses that are ready to reap more benefits from the private cloud can enjoy seamless integration through fabrics. Cloud environments, themselves, allow for greater innovation, agility, and scalability with cost-effective pricing structures.

Ready to Elevate Your IT Infrastructure?

When it’s time to upgrade, implementing a data center fabric can feel like a lot to take on. TierPoint can help you elevate your infrastructure through our cloud consulting and IT advisory services. We’ll take you from a traditional structure to one that allows for maximum flexibility and innovation. Contact us to learn more.

FAQs

What is Data Center Maintenance?

Data center maintenance includes proactive and reactive practices that repair, monitor, inspect, and service all systems that keep a data center running. The goal of data center maintenance is to maximize uptime, extend the lifespan of your data center equipment, and optimize the performance of all data center components.

Why is Data Center Maintenance Important?

Data center maintenance is important for multiple reasons. Regular maintenance, regardless of strategy, can help identify and prevent issues that can lead to system failure. Power outages, equipment failure, security vulnerabilities, and even dust and dirt can bring business to a halt.

Businesses that invest in data center maintenance can experience improved uptime, reduced operational costs, and improved security. Preventative measures can also help greatly reduce the likelihood of major outages.

Types of Data Center Maintenance

There are three main types of more proactive data center maintenance – preventive, reliability-centered, and predictive. Corrective maintenance also exists, which is concerned with fixing equipment that has already broken.

Preventive Maintenance

Preventive maintenance involves routine tasks performed regularly whether the equipment needs a repair or not. While it can help prevent most problems, it can also be overkill and cost more than a company is willing to spend.

Reliability-Centered Maintenance

For a more nuanced strategy, businesses may opt for reliability-centered maintenance. With this approach, companies prioritize their critical systems and plan maintenance tasks accordingly. Systems that are less vital to business operations are not tended to as often as a result.

Predictive Maintenance

Like reliability-centered maintenance, predictive maintenance (typically implemented using a tool like predictive AI) focuses on the most urgent priorities, often determined by sensors and data analysis that identifies current conditions and potential failures.

8 Data Center Maintenance Best Practices

Choosing one data center maintenance practice and sticking to it is better than not attempting to make any changes at all, but the more you can incorporate, the more prepared your business will be to repair and proactively fix equipment in your data center.

Ensure Uptime by Creating Redundancies

By implementing redundant systems, businesses can improve their uptime and make maintenance easier. Consider adding redundant components such as additional power supplies, cooling systems, and network connections.

Keep Indoor Climates Stable

Equipment that must withstand fluctuations in temperature and humidity will experience more wear over time. The more you can keep indoor climates stable, the less frequently you will have to replace equipment. One of the ways you can ensure a more stable temperature inside is by using data center environmental monitoring tools to keep an accurate eye on:

• Humidity
• Temperature
• Airflow

Create Stronger Testing Protocols

Some systems may only be truly put to the test in emergencies, such as power generators, backup systems, and fire suppression equipment. It’s important to regularly test these systems to ensure that they will perform as expected during actual emergencies.

Implement at Least One Type of Data Center Maintenance

Depending on your budget, the number of critical systems you have, and the level of uptime you want to guarantee, consider implementing at least one form of data center maintenance. For example, sensors that allow for predictive maintenance can work well alongside a tiered approach via reliability-centered maintenance.

Hire Adequate Staff for Operating and Maintaining Data Centers 

The smooth functioning of your data center is directly connected to the staff you have available to operate and maintain the facility. One of the leading causes of problems related to downtime stems from human error and poor management practices. To combat this, businesses should either hire people who are well-versed in data center maintenance or consider outsourcing the work to an external team of experts.

Keep a Clean Environment

One of the simplest maintenance tips can also be highly effective. Dust and debris can overheat and wear down equipment. Keeping a tidy environment by regularly dusting, sweeping, and performing other cleaning tasks can help extend the life of your components.

Practice Good Data Hygiene

Good hygiene should also extend to your data. Keeping more data than you need adds an unnecessary load to your facility equipment. Implementing secure data storage can safeguard valuable information and prevent digital breakdowns.

Maintain Emergency Preparedness

Data center maintenance can’t protect you from everything. You also need to have preparedness measures in place for unforeseen emergencies, such as power outages, cyberattacks, and fires. Create a disaster recovery plan and test it at least once a year. Add physical security measures, such as video surveillance, access provisioning, and key management to reduce the potential impact of bad actors.

Make Implementing Data Center Best Practices Easier with an Expert

Even when steps to improve data center maintenance are laid out in a list, it can be difficult to decide which item to start with or find time to manage more than one initiative at the same time. By working with a data center expert, organizations can enjoy the benefits of preventative maintenance measures and free up their time to focus on other areas of their business. Learn more about TierPoint’s data center services by scheduling a consultation.