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Tag: data center

  • Five Ways AMD’s ZT Systems Acquisition May Enhance AI Sustainability

    Five Ways AMD’s ZT Systems Acquisition May Enhance AI Sustainability

    AMD’s recent acquisition of ZT Systems for $1.9 billion opens new doors for enhancing sustainability in the data center and AI markets. Here’s how this strategic move could advance AMD’s environmental goals, along with some potential challenges:

    1. Improved Energy Efficiency

    ZT Systems specializes in energy-efficient hyperscale server solutions. By combining their expertise with AMD’s high-performance processors, data center energy consumption could potentially decrease. Fundamentally, ZT Systems makes servers that use less energy in big data centers. AMD makes computer chips that are powerful but rely on less energy. When you put AMD’s chips in ZT Systems’ servers, data centers can do more work with fewer servers. This aligns with AMD’s goal of achieving a 30x increase in energy efficiency of AI and high-performance computing processors by 2025.

    2. Greener Supply Chains

    AMD has been actively working with suppliers to reduce environmental impact. Integrating ZT Systems’ practices may further strengthen these initiatives, potentially leading to more sustainable manufacturing processes across a broader network. The challenge lies in harmonizing supply chain practices across different regions, which may have varying environmental regulations and standards. For instance, one region may have strict carbon emission limits, while another is more lenient, requiring companies to manage different regulations. This complexity makes it harder to establish a consistent, eco-friendly supply chain strategy.

    3. Smaller Data Center Footprints

    ZT Systems’ rack-scale solutions could help AMD optimize AI infrastructure deployment. This may result in fewer physical servers needed, reducing both energy use and the measurable size of data centers. With less space required to house the servers, data centers  can be smaller overall. Real estate is expensive so reducing costs associated with building and maintaining larger facilities is a positive. However, headwinds may arise with the transition to more space-saving solutions. It might require a larger initial investment and infrastructure changes.

    4. Advanced Cooling Technologies

    Effective cooling is crucial for data center efficiency. ZT Systems brings expertise in advanced cooling solutions, which could help AMD further reduce energy consumption in its data center offerings. The challenge here is ensuring these cooling solutions are adaptable to different data center environments and climates. However, ensuring these cooling solutions are adaptable to different data center environments and climates can be a challenge. For example, data centers located in hot and humid climates may require more advanced cooling systems to maintain optimal temperatures. Traditional air-cooled systems account for up to 40% of a data center’s energy consumption.

    5. Accelerated Renewable Energy Adoption

    AMD and ZT Systems’ combined expertise could accelerate renewable energy adoption in data centers, reducing fossil fuel dependence and carbon footprints. Their integrated solutions offer better power management for variable renewable sources. Data centers could optimize workloads to match energy availability, using solar power during the day and wind power at night. While renewable energy costs and availability vary by location, this approach enables more efficient use of intermittent energy supplies.

    By leveraging ZT Systems’ capabilities, AMD is poised to make significant strides in delivering environmentally responsible technology solutions. This acquisition not only strengthens AMD’s market position but also reinforces its commitment to sustainability in the tech industry, despite the challenges that may arise.

  • Data Management: Cloud Technology or Data Centers?

    Data Management: Cloud Technology or Data Centers?

    Traditionally, larger enterprises have relied on in-house server hardware equipment for their data management needs, optimized to run high-performing applications with a certain degree of assured security and reliability. 

    However, the high costs and technical expertise required to manage these solutions effectively often render them inaccessible to startups and emerging businesses. But, as the old saying goes, necessity is the mother of invention. 

    With the growing demand for cost-effective, efficient data management solutions, the tech industry has witnessed the advent of cloud technology and data center colocation rental services. These alternatives pave the way for startups and small businesses to handle large volumes of data without straining their budgets.

    This article will compare and contrast cloud technology and data center services to help business leaders make well-informed decisions about their data management needs and the solutions that can help them.

    Understanding Data Center Colocation Services

    Data center colocation services offer an enticing solution for companies that require robust data management capabilities without incurring high capital expenditure for securing storage hardware, along with storing and powering them. 

    Essentially, this solution is like renting a space for your data in a specialized facility. These data centers provide a secure environment with the necessary power, cooling, and physical security for your server, storage, and networking equipment.

    In opting for colocation services, companies can leverage state-of-the-art data center infrastructure without the burden of high upfront costs and ongoing maintenance responsibilities associated with owning a private data center. This also allows businesses to focus more on their core competencies rather than the intricacies of data center management.

    However, data centers come with their own set of unique challenges. For instance, they often require a certain degree of technical expertise on the part of the business to manage their servers. This means hiring or training staff to handle server management, increasing the overall cost of this solution.

    Additionally, data centers’ effectiveness can sometimes be influenced by physical proximity. The closer your operations are to the data center, the better the performance can be. This geographical factor can pose problems for businesses operating in multiple locations or managing data across different data centers.

    The Power of Cloud Technology

    In contrast to relying on data centers, cloud technology offers businesses of all kinds and sizes immense scalability and flexibility in terms of data management solutions. 

    As a service that delivers computing infrastructure, software, or storage as a service via a network connection, typically the Internet, cloud technology eliminates the need for physical proximity to servers. It is quickly scalable, both upwards and downwards, as per business needs, ensuring that you only pay for what you use.

    Moreover, the ability of cloud technology to integrate various services can simplify business operations and enhance customer engagement. For instance, many businesses are now offering cloud-based text-to-pay systems that allow customers to pay from any device, providing ease of transaction while simultaneously gathering customer data in the cloud.

    Cloud services can also provide robust disaster recovery and business continuity solutions. However, businesses often cite concerns about data security and privacy in the cloud. Although most cloud service providers implement best-in-class security protocols, the very nature of the cloud—being an off-premises solution—necessitates a high level of trust in the provider.

    AI and Data Management Solutions

    If your business relies heavily on AI applications, you must understand how your data management solution supports this need. AI requires high-quality, well-managed data to function effectively. Here, cloud technology often presents an edge over traditional data centers.

    Many cloud service providers offer built-in tools and services designed to better facilitate AI implementation, including pre-trained machine learning models and AI platforms as a service. These resources can dramatically reduce the time, costs, and complexity that come with implementing AI, particularly for small to mid-sized businesses without extensive in-house AI expertise.

    On the other hand, while data centers might not have these built-in tools, they offer robust performance capabilities for AI workloads and often provide more control over the data, which is vital for sensitive AI applications.

    Keep in mind you can turn to other server-based solutions for storing data as well. For example, if your application relies on a significant amount of PDF content to store data and function effectively, hosting those files on a server such as a server-side PDF SDK will allow you to process those documents on the server via an API. This will then allow you to autoamte your document processing and convert large volumes of your PDF files. 

    Web Hosting Requirements

    Your current web hosting requirements can also significantly impact the choice between data center services and cloud technology. Shared hosting services may be sufficient for businesses with low-traffic websites or applications, and if you don’t need to store a lot of data, but as your traffic increases and the website grows, so do your hosting requirements. 

    Cloud hosting solutions offer excellent scalability, allowing your website to grow with your business. As traffic surges, cloud servers can easily handle the increase, maintaining website performance and user experience.

    Conversely, with a dedicated data center, you’re generally limited to the capacity of the server you rent. To increase your capacity, you may need to physically upgrade the hardware, a process that can result in downtime and potential loss of revenue.

    Considering Cost Implications

    The overall cost of your chosen solution will be a key factor when deciding between data center colocation services and cloud technology. Each service has unique cost structures that can vary between providers, and understanding these can significantly impact your company’s financial health and resource allocation.

    With data center colocation services, the costs are generally more predictable. You pay a fixed amount based on your rented server space and the bandwidth you use. This stability can be advantageous for budgeting purposes, especially for businesses with steady data usage and processing needs. However, you may also incur additional expenses for any necessary hardware upgrades, maintenance, or increased bandwidth usage.

    What’s more, there’s the indirect cost of employing or training staff to manage the servers, plus the potential costs of transportation to and from the data center, particularly for businesses not located near their rented server space. These costs can add up and should be factored into the total cost of operating within a data center.

    Cloud services, conversely, operate on a pay-as-you-go model. This solution can be much more cost-effective for businesses with shifting data needs since you can readily scale up or down based on demand, ultimately only paying for the resources you actually use.

    Choosing the Right Path for Your Organization

    Unfortunately, there’s no one-size-fits-all solution for effective data management. Each business has unique needs, requirements, and considerations that necessitate a carefully tailored approach. When choosing between cloud technology and data center colocation services, business leaders must carefully consider several key factors to determine the right solution for them.

    If scalability, flexibility, and AI integration rank high in your data management strategy, cloud technology presents an excellent solution with its on-demand resources and array of built-in tools. On the other hand, data center colocation services provide robust performance capabilities, greater control over data, and predictable costs—features that may appeal to businesses with more consistent data management needs.

    Understanding your current web hosting requirements and the potential cost implications of each solution are also crucial elements to consider in your decision-making process. The demand for efficient, secure, and cost-effective data management solutions will only continue to grow as data storage needs expand rapidly.

  • Android Apps Must Let Users Delete Data ¦ RISC-V in the Data Center

    Android Apps Must Let Users Delete Data ¦ RISC-V in the Data Center

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Google forces apps to make deleting users’ data easier, and the RISC-V drumbeat grows louder. (more…)

  • Linux Tweak Brings Big Speedup ¦ DCs in SPAAACE (Redux) ¦ Atlassian Fires 500

    Linux Tweak Brings Big Speedup ¦ DCs in SPAAACE (Redux) ¦ Atlassian Fires 500

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Intel optimizes Linux multithreaded networking, data centers in space (again), and more DevOps toolmaker layoffs. (more…)

  • Voice.ai ‘Stole’ Code ¦ AWS Gets Filthier

    Voice.ai ‘Stole’ Code ¦ AWS Gets Filthier

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Alleged theft of GPL code, and Amazon will run its data centers on gas. (more…)

  • Energy Efficiency and the Data Center

    Energy Efficiency and the Data Center

    The IT industry has historically been a leader in the ongoing race to reduce CO2 emissions. Reducing emissions is good for the environment and for the corporate bottom line. The data center is well-known as a voracious data consumer. But the data center consumption findings keep pace with the trend. Even in the face of remarkable growth in data center capacity and performance, their energy consumption has increased only a few percentage points since the 2010s. This almost-flat rate of energy consumption is primarily the result of the shift to cloud computing as well as the shift to solar and wind energy, where it makes sense.

    Investments in renewable energy sources have helped tamp down criticism over the overall IT carbon footprint. Knowing that it takes a tremendous amount of energy to power a data center, owners and operators are always looking for ways to improve efficiency and reduce carbon expenditure. Today, there are newer and better ways for organizations to deliver the performance to properly power modern applications while keeping in mind the energy efficiency and carbon reduction the world requires.

    Minimizing Energy Use and CO2 Production

    How did we get here? Decades of semiconductor process miniaturization in concurrence with living out Moore’s Law has culminated in ongoing improvements in performance, storage capacity and device density. Today’s mobile phones are a great example of that ongoing progress. A secondary factor in the mobile phone revolution is a processor architecture optimized for efficiency and flexibility. This ARM architecture has been used to power almost all devices since the first iPhone. Over the years, ARM has also become a realistic platform for servers; often a better choice than an x86 system.

    Energy efficiency is one area where ARM systems win out over conventional server architecture. But it was not until recently that ARM processors could deliver the required performance for the data center. Today’s systems, based on Armv8 designs, have brought into play x86-class capabilities in a more energy-efficient way. The advent of the ARM-based AWS Graviton processors, which by some estimates have already captured 20% of the instance share on AWS, demonstrates that ARM can run production workloads while delivering what AWS calls “up to 40% better price performance over comparable current-generation x86-based instances for a wide variety of workloads.”

    To illustrate the impact on carbon production when a data center shifts from x86-based architecture to ARM servers, an analysis of the top elements contributing to data center energy use was undertaken. The energy savings was calculated and translated to reduced CO2 emissions based on the fuel mix used to power the electric grid in various locations. The results prove that an ARM-powered data center reduces CO2 production by 74%, equivalent to almost half a million barrels of oil.

    Power Consumption Reduced by Efficient Servers

    The premise is simple—innovative server design significantly reduces data center energy use. In this model, a server’s power draw and physical size are key factors. The energy needed for other data center subsystems was extrapolated by using ratios derived from experience and validated by estimates from other data center operators.

    Data center energy use can be divided into five categories of equipment and infrastructure: Servers, network communications, power supplies, conversion and UPS, cooling systems and storage. Servers and cooling comprise about 75% of the total.
    The calculations were based on the energy used by a medium-sized data center with 750 racks of conventional 1U servers (42 servers/rack; around 31,500 in total). Assumptions used included servers comprising two-thirds of the floor space and that the equipment space accounts for 65% of the total, translating to a 62,000 square-foot data center. For comparison, facilities operated by the major cloud vendors can be a million square feet or more. Knowing server power consumption and the ratios for the equipment categories, algebra can be used to calculate the energy consumption for each.

    The data center in this hypothetical example has, as we said, 31,500 servers. Using the 912.5W power draw of a Dell R640 as typical of 1U x86 servers, the total server usage in the ARM data center would be 251,795 MWh/year. Using this formula, the amount of energy saved by using ARM servers can be calculated. Each server requires about one-quarter the electricity of a standard server. In the formula, the assumption is that the ARM server installation would require half the equipment racks (375 instead of 750) and 52% of the power-per-system, comprising a data center using only 26% as much electricity (152,409 MWh/year) as one based on x86 systems.

    The math shows that switching one medium-sized data center to ARM servers can produce annual savings equivalent to 45,459 fewer cars on the road or 486,749 fewer barrels of oil used.

    The value in CO2 reduction is widely understood for preserving the environment. It is also advantageous from an economic standpoint. In fact, there are many technical and economic reasons to adopt energy-efficient ARM-powered servers in the next-generation cloud data center.

    To hear more about cloud-native topics, join the Cloud Native Computing Foundation and the cloud-native community at KubeCon+CloudNativeCon North America 2021 – October 11-15, 2021

  • Are You Wasting Money in the Cloud?

    Are You Wasting Money in the Cloud?

    I’m not the first person to point out the issues with Andreessen Horowitz’s blog post outlining why Dropbox is repatriating from the public cloud to data centers and saving an enormous amount of money. But while others have brought up valid points about how Dropbox is not representative of most companies and that the value in public cloud is in speed, not cost savings, I wanted to address a different angle: How do you, as an IT leader, know if or when you should move your cloud workloads back to the data center? How do you evaluate the ROI on the cloud in general?

    I want to talk through some of the factors that companies should be aware of when they’re thinking about whether or not it would be appropriate to repatriate a workload—and perhaps give some IT leaders peace of mind that staying in the cloud is OK.

    How Mature is your App?

    The earlier you are in the application’s life cycle, the more critical access to cloud infrastructure is. Early in the application’s life cycle, you need to get things out the door and test them with real people. You don’t, at this point, actually know how users will interact with your application or how the application will perform under the stresses of actual use. You also don’t know how much it will cost to run.

    At the beginning, quick iteration is also very important. There will undoubtedly be things in the initial release that go wrong—you need a way to quickly adjust course and improve.

    As the application matures, everything slows down. Your information about the application accumulates, but quick iteration becomes increasingly less important. It’s not until the workload is fairly mature that you can begin to understand how much it costs to run in the cloud and can compare that with how much it would cost to run on-premises.

    At the early life cycle stages, using a public cloud provider is extremely valuable because it allows you to get the application into users’ hands quickly and to iterate fast. As the application matures and changes are made less frequently, moving it back to a data center could make sense—at least in some cases.

    Internal Skill Sets

    Running an application in the cloud involves a different skill set than managing a data center. If you don’t have the internal skills needed to staff your data center, that can add not just cost, but will also increase the risk that something goes wrong.

    If you’re considering moving from cloud to a data center, taking a critical look at your internal skill sets is essential. Without a skilled team to manage the data center, you’re better off staying in the cloud even if it’s expensive. And yes, you can hire the skill sets you need, but that requires both time and money.

    Criticality

    If you have a workload that you never want to go down—because it is mission-critical, because it’s revenue-producing—you should probably keep it in the cloud. The cloud is generally more reliable than individual data centers, and the repatriation itself could easily cause disruptions.

    In addition, the cost of running mission-critical applications is less important simply because they are so essential to the business. If the application is burning money but is also your only source of revenue—unless it’s burning more money than it earns, it probably makes sense to keep things as-is.

    Looking at the Exit

    Of course, depending on your application’s architecture, repatriating it from the cloud to a data center could be either impossible or prohibitively expensive. If the application relies on the cloud provider’s proprietary services—databases, for example, or Lambda functions—moving it off of the cloud is going to be extremely hard. Even if it turns out that moving from the cloud to the data center is possible, it could easily cost so much that it wipes out any potential savings from repatriation.

    This isn’t to say that you should never think about leaving the cloud—in fact, if you think moving cloud workloads back on-premises is something you’d like to do at some point, you should consider it during the architecture phase. The decisions you make at that point will determine how challenging it would be to move off the cloud in the future—or even to change clouds if you want to. Using Lambda is going to make moving off the cloud very difficult, for example, whereas using containers will make it comparatively easier.

    When we talk about the costs of public clouds versus data centers, we’re trying to compare things that are fundamentally different. First of all, public cloud costs are notoriously difficult to understand, whereas understanding the costs for on-premises is relatively easy. Sure, there is overhead—building, staff members, etc — but it’s not that different from calculating any other cost in a business and an accountant could make sense of it in 10 minutes with Excel. Good luck doing that with AWS.

    That’s the core problem with cloud costs—humans have a hard time trusting what they don’t understand. But cloud has never been about reducing costs. And regardless, the cheapest option is not always the best option.

  • Using Chaos Engineering to Build Resilient Systems

    Using Chaos Engineering to Build Resilient Systems

    Over the past decade, chaos engineering has become one of the most popular approaches in DevOps. It’s uniquely adapted to complex cloud-based systems and has the potential to succeed where more conventional approaches may not.

    Chaos Engineering Explained

    Traditionally, DevOps teams worked with systems hosted on single servers. They would analyze logs with a fine-toothed comb, looking for potential anomalies. They implemented automated tests to ensure the system worked to spec.  They employed root cause analysis to debug applications.

    Modern systems use cloud-based distributed architectures and are much too complex for this approach to be successful; they contain hundreds, and sometimes thousands of instances.  In many cases, their behavior can be chaotic, making it nearly impossible to predict.

    Because of this, modern systems need to be designed for resiliency from the outset. Chaos engineering was developed to foster resilient system design by deliberately anticipating failure modes.

    Two Steps to Chaos

    Here’s how it works. After characterizing a state of “health” for their system, DevOps teams perform a chaos experiment. Just like physics or chemistry experiments, chaos experiments involve setting up two versions of a system: the experimental version and the control.

    The experimental system is then subjected to a barrage of virtual kicks and punches which could range from injecting faults to disrupting a data center. DevOps teams then pore through the resulting logging data, attempting to understand how the chaos experiment affected the state of the system.

    Chaos engineering has two major differences from conventional DevOps practice. First, while conventional forms of testing supply a controlled input and test for a predefined output, chaos experiments are far more open-ended. They’re designed from the outset to expect the unexpected, requiring analysis of the whole system to give novel insights.

    Second, they are performed on live systems. This makes chaos engineering controversial, as its experiments have the potential to affect real users. Still, it makes it much more powerful than alternative approaches. Chaos engineering encourages resilient design by taking genuine risks with systems in the wild.

    Why Companies Embrace Chaos Engineering

    Chaos engineering has been around for over a decade. In that time, it’s been adopted by several of the biggest names in tech. Netflix, the company that pioneered the approach, is still the most well-known for their use of fault injection.

    In 2008, Netflix was moving from an in-house data center to the cloud. They knew that, as they expanded, their application would undergo massive horizontal scaling.  Because any one of its thousands of instances could fail at any moment, it needed to be resilient.

    They created the first chaos experiment, Chaos Monkey. This worked by randomly turning off instances in Netflix’s live application to check it was fault-tolerant. This practice sounds dangerous, but it encouraged Netflix engineers to design resilient system architectures.

    Chaos Monkey sounds bananas, but it worked. In its first five years of operation, there was only one failure which was promptly fixed by an on-call engineer. That failure was caused by Chaos Monkey itself.

    Facebook is another big company to adopt chaos engineering. They created Project Storm in the wake of 2012’s Hurricane Sandy.  Although the hurricane itself didn’t affect Facebook, it served as a wake-up call. A storm hitting one of Facebook’s data centers could wreak havoc.

    In 2014, Facebook took the step of turning off traffic to one of their data centers. Although no end users were affected, traffic loading went haywire on all sorts of different subsystems.

    Once Facebook had successfully identified this weakness in their system’s resilience, they could preemptively design traffic management software that would be resilient in the face of a live data center shutdown.

    As more and more companies use cloud computing and hyperscale data centers, chaos engineering is likely to become an essential feature of DevOps strategy.

    Applying Chaos Engineering In Your Company

    Chaos engineering is the ideal approach for complex, distributed systems. Chances are, your DevOps team either runs their application in the cloud or is planning to migrate to it.

    Serverless computing, in the guise of systems like Kubernetes and Heroku, is popular with an increasing number of organizations. Now it’s common for systems to contain hundreds or even thousands of instances, hosted in massive data centers.

    So far, we’ve only seen chaos engineering from 10,000 feet. It’s time to look at how you can make it work on the ground.

    First, we need to understand the roles observability and monitoring play in chaos engineering.  To successfully implement chaos experiments, there needs to be a high level of system visibility.

    Dealing with Data

    Marius Moscovici outlines four strategies for extracting valuable insights from data. He discusses the importance of detecting anomalies in your data stream, something a chaos engineer needs to be good at.

    Whether it’s the haywire traffic in Facebook’s Project Storm or the deactivation of Netflix instances, chaos experiments rely on the ability to diagnose unusual patterns in data.

    1. Profiling Normal System Behavior

    In order to detect anomalies, you need to have a good idea of what your system normally does. In their book on chaos engineering, Netflix used the analogy of human vital signs. Just as doctors know that 98.6°F is a normal human body temperature, their IT counterparts need to characterize a “normal” state of “health” for their DevOps system.

    Many log analysis and observability tools in the market offer machine learning capabilities to help achieve this.

    2. Detecting Anomalies Across The Board

    Anomaly detection should be integrated into every step of your data analysis. You need to be able to detect anomalies across the board, not just in places you happen to be looking at. Because chaos engineering, unlike more conventional forms of testing, is designed to reveal new and unexpected information it’s vitally important to have a completely open data strategy.

    3. Data Scientists On the Lookout

    Always be on the lookout for potentially anomalous behavior. Chaos engineering will require your company to take its DevOps strategy to a new level, both in terms of your team’s mindset and the sophistication of their logging tools.

    Because chaos engineering requires you to analyze all available data without making assumptions, it’s necessary to leverage machine learning to extract patterns from your logs.  Chaos engineering requires a scientific mindset and an appreciation of the power of data.

    4. Create Alerts

    Finally, make sure that anomalous behavior is being flagged and that the team is aware when it occurs. If possible, dynamic alerts should be used to avoid false positives and alert fatigue which is common with static thresholds.

    Engineering with Chaos

    More and more organizations are migrating to the cloud and building highly distributed application infrastructures. DevOps teams are being forced to confront chaos and complexity on a daily basis.

    While the complexity of modern IT systems has necessitated chaos engineering, it has also enabled it. In a chaotic world, chaos engineering brings the order of an engineering approach.

  • ‘Old Man Yells At Cloud’

    ‘Old Man Yells At Cloud’

    These days, software programmers are increasingly estranged from hardware. Most millennials and Generation Z developers have never had to mount physical servers. They have never been lulled by the gentle hum of a server room chugging along in synchronous harmony. Today, you don’t even necessarily have to build your own PC or worry too much about disk space.

    Instead, cloud-native software developers conveniently offload the server burden to cloud service providers (CSPs). Hosting in a cloud, like AWS or Azure, for example, makes a lot of sense — it allows programmers to hone in on their own specializations. Elastic computing models are cost-effective for startups, and CSPs typically offer a vertical stack with helpful integrated platform-as-a-service (PaaS) and software-as-a-service (SaaS) offerings. However, what are we sacrificing?

    Well, first, research shows the cloud model becomes less cost-effective as a business scales. Second, as more companies transform into technology platforms, cloud usage could better enable their competition. Third, more direct access to bare metal could be an attractive option for certain service providers to increase ownership and control workload optimizations.

    These arguments are bolstered by large companies such as Dropbox, Facebook, Walmart, and others that chose to cut their cloud allegiances in favor of building their own data centers or adopting colocation (in other words, running your own hardware in someone else’s data center, also known as “colo”).

    I recently met with Zach Smith, managing director, bare metal, Equinix, to understand the current state of hardware-as-a-service and the future of hybrid server models. He believes that in the coming years, improved interfaces that access low-level infrastructure will arm IT with better consumer choice that brings them closer to the metal. Yet, to enable this, the hardware world will need to take some lessons from the upper stack regarding transparency, open source community, programmability and improved developer experiences.

    Sidestepping the Cloud

    Public clouds arose as a convenient one-stop-shop to host entire software application environments. As a result, “we have a generation of developers who will never physically touch that hardware,” Smith said.

    The cloud infrastructure services market was worth $39.8 billion in Q4 2020, estimates according to estimates from Statista. Having witnessed the “movement toward verticalization of hyperscale clouds,” Smith now sees the infrastructure-as-a-service (IaaS) market as an industry of high margins and escalating fees. This is echoed by others who seek to reduce rising cloud expenses.

    Though cloud computing usage is growing considerably, the data center isn’t dead. Instead, computing models are morphing and becoming hybridized. “By 2025, 85% of infrastructure strategies will integrate on-premises, colocation, cloud and edge delivery options, compared with 20% in 2020,” predicts David Cappuccio, distinguished VP analyst, in a Gartner report.

    As hybrid multi-cloud approaches emerge, Smith notices a resurgence of attention being paid to infrastructure. Some massive service providers are starting to construct their own data center estates; others, such as Box, now own and lease servers from data center providers.

    Benefits of Bare Metal

    As mentioned above, there are a few reasons to move back to the metal. First, it may simply be more cost-effective for large deployments. In his well-researched article, Antoine Beaupré compared monthly recurring costs for colocation versus an AWS bill, and found the break-even point rested at $100,000 per month, or 150 Amazon m4.2xlarge instances. His research also found that I/O-heavy applications are more costly in the cloud than via colocation.

    “While many services simply launch in the cloud without the organizations behind them considering other options, large content-hosting services have actually moved back to their own data centers,” Beaupré said.

    In addition to cost savings, automating colocation with a programmable layer could also offer more fine-grained control over computing optimizations. “I believe what we’re creating is an operating model for a rapidly changing hardware substrate,” said Smith. He described how bare metal opens up a neutral infrastructure substrate to control physical networking aspects such as cross-connects, IP transits and peering — a benefit Smith half-jokingly called ‘NetDevSecOps.’

    Another argument is that investing in hardware could, ironically, bring sustainability benefits. A report conducted by ITRenew estimates that, of the total embodied energy required within a server’s entire lifespan, 77% is used to manufacture the hardware itself. In contrast, the operational energy used over a server’s lifetime only accounts for 22%.

    Thus, there is an environmental benefit to reversing the trend of a make-dispose economy continually producing new servers. Instead, hardware ownership could encourage a more circular economy. “A circular economy aims to end our take-make-dispose economy, which is a very linear approach, and instead, keep products at their highest possible value for as long as possible,” according to the ITRenew report.

    Learning From the Upper Stack

    How can we build a fundamental non-opinionated infrastructure platform? According to Smith, the industry needs “hardware and networks that are programmable and usable by a developer generation.” To Smith, changing firewalls at a kernel level, altering how the runtime works or controlling the IP stack, should all be operational via API commands.

    Yet, hardware accessibility is still rather opaque and, historically, not as developer-friendly. “How can we take abstraction and community-driven ideas from up the stack?” Smith asks. To enable these changes, he believes the hardware world should model itself on the upper stack in a few key areas:

    1. Transparency: The hardware world is not as easy to comprehend. “When you get to the physical side of switches and routers, it’s still arcane,” Smith said. Increased transparency in functionality and ownership will be required to turn servers into something all developers can operate.
    2. Open-source community: In the same way the software world is built on strong developer communities, hardware will require an open community to normalize things like firmware, boot order and support for differences between drives.
    3. Programmability: “Self-service and programmability are requirements for most things these days — there’s no difference with physical infrastructure,” says Smith. Take the rise of the API economy — today’s mantra is, essentially, if something is possible in a UI, it should be possible through an API. Similarly, if it can be done in a server, it should be controllable with an API.
    4. Quality developer experience: Self-service, programmable infrastructure is just the beginning — interfaces to operate infrastructure require good developer experience. Smith feels that hardware requires an improved buying experience and better life cycle management capabilities. This could be enabled by open source tools like Tinkerbell, a provisioning and workflow engine supported by Linux Foundation.

    Back to the Metal — A Platformer’s Dream?

    All this is not to say the public cloud isn’t valuable – it absolutely is. The cloud has been necessary and has proven resilient enough to support online business and digital life as we know it throughout the pandemic and for years prior. “In 2020 and beyond, most organizations are going to be transforming into a completely cloud-based environment,” said Werner Vogel, CTO, Amazon, during an online AWS summit.

    But, even if most organizations are in the cloud, that doesn’t mean all of their assets will be. Service providers, especially, may cling to hybrid models for more control, to avoid supporting the competition and, perhaps, even to save money. Private data centers could also increase security. In an era where raw computing power is becoming a new asset (quite literally, in the case of cryptocurrency), the server rack is starting to look sexy again.

    “The magic happens when software and hardware work together,” said Smith. But, giving access to physical technology to a generation that has never touched it will require some ingenuity. As Smith says, this could be done by “automating colo” through more usable, standardized interfaces for hardware management.

    “We will see a future in next 10 years where operated software is separated from cloud infrastructure,” Smith said. That’s a bold statement, as such a dramatic change would, in effect, usurp the current cloud provider IaaS market share, altering the distribution and economic model associated with global software.

    While this dream may seem far-fetched, if more platforms decide to cut out the cloud middleman and go it alone, it could materialize.

  • Earth Day: The Green Side of Computing

    Earth Day: The Green Side of Computing

    When we think of climate change and the existential threat of global warming, our minds often conjure images of dirty factories pumping out carbon dioxide or fuel-guzzling cars sitting idle with their engines rumbling. We think of ice sheets tumbling into the ocean and sea levels rising, and we know that grand, sweeping changes are needed. However, while it’s always good to have one eye on the elephant in the room, it’s equally important to step back and look at the picture as a whole.

    Changing Climate Perceptions

    Have you ever thought about your own impact on the environment when settling down to stream something on Netflix? Or how your Zoom meeting tomorrow might contribute to the climate emergency? A recent study found that, despite a record drop in global emissions in 2020 due to the pandemic, the switch to remote working and at-home entertainment still presented a significant environmental risk. One hour of streaming or video conferencing, for instance, can emit 150 – 1,000 grams of carbon dioxide, so perhaps you should think twice about switching on your camera during your next virtual meeting. That’s because everything you do online is facilitated by a data center somewhere in the world. That data center will need to be powered, cooled and maintained in order to operate effectively. The majority of countries reported around a 20% rise in internet traffic from the beginning of the pandemic, meaning those data centers have to work even harder.

    Now, let’s apply this to the world of business. Do organizations consider their cloud activity or data center usage when assessing their environmental footprint? If the latest figures are anything to go by, simply turning down the thermostat by a few degrees may not be enough. Today, the technology business sector consumes roughly 7% of the world’s energy. By 2025, Greenpeace estimates that figure could skyrocket to more than 20%, mainly due to innovations in cloud computing designed to drive efficiency, automation and, of course, facilitate remote working.

    Data center workloads increased by 500% between 2010 and 2020 as more of the working world moved online. In the U.S. alone, data centers use roughly 660 billion liters of water per year to generate electricity and cool their facilities. In recent years, greenhouse gas emissions from data centers have eclipsed that of the entire global airline industry. That means your company’s use of technology could contribute more to global warming than a transatlantic flight. It’s clear that our perceptions around climate change and our relationship to the environment need to broaden considerably.

    Turning the Tide

    Fortunately, it seems the tide is turning. There are initiatives popping up all around the world to try and counter the impact our data centers are having on the environment. The European Union, for instance, has launched the 2030 Climate and Energy Framework – which aims to reduce greenhouse gas emissions by 40% by 2030, with all sectors doing their part. While governments have traditionally focussed on the more obvious issues, such as the burning of fossil fuels by cars, it seems their scope is now broadening in the knowledge that every action – large or small – helps. If we can solve the problem of polluting data centers now, before our dependence on them grows exponentially, we can prevent the next big ‘elephant in the room’ from emerging. Google knows this, and vowed to power all of its data centers using carbon-free energy by 2030. Other companies, like Equinix, are even further along with their green initiatives to protect and power a more sustainable digital world. In 2019, over 90% of Equinix’s global data center energy consumption was covered by renewable energy sources, pushing it one step closer to its long-term goal of using 100% clean and renewable energy.

    Computing has always had an impact on the environment, but it was so negligible that it barely registered. Those impacts are still negligible when looked at in isolation, but when multiplied by billions, they quickly add up. By changing the way we think about cloud computing and accepting it as a net contributor to the climate crisis, we can start to steer our future to safer shores. Making sure you work with data centers who share your passion for a green future is a great first step.