New findings from research by Uptime Institute Intelligence reveals that organizations can cut both their cloud carbon emissions and costs by moving workloads to different regions. However, the trade off with this migration is an increase in latency.
Cloud users choose regions based primarily on two factors:
Locating the application close to end users improves the user experience by delivering faster content. Some applications, such as interactive gaming, require very low latency, which is driving cloud providers to invest in new edge locations close to end users. Not all applications, however, need such a quick response and end users can often tolerate a slight increase in latency without material impact to their experience.
Offering cloud-based services in a country usually has implications regarding data protection and this can be partly addressed by keeping data within the same jurisdiction as their end users.
If there are no legal reasons to keep data in a jurisdiction, cloud users can often migrate their workloads to a nearby region and gain reductions in their carbon footprint; this migration can also result in lower costs. Uptime Intelligence collated information from Microsoft Azure, Amazon Web Services (AWS), Google Cloud, the Cloud Carbon Footprint project (which sources data from carbonfootprint.com, the European Environment Agency and the US Environmental Protection Agency) and CloudPing to produce the Cloud Carbon Explorer, which includes three interactive maps.
The maps show the potential cross-region workload migration paths for AWS, Google Cloud and Microsoft Azure. These workload migration paths can reduce carbon footprint without significantly impacting user experience and, in some cases, reduce cost. Users can use the tool to explore suitable compromises of latency, cost and carbon for each application.
We found 38 migration paths between AWS regions that provide both carbon and cost reductions with a latency impact of less than 100 milliseconds (ms). For Google Cloud, there were 39 migrations paths and for Microsoft Azure there were 15 migration paths, all with a latency impact of <100 ms.
Figure 1 shows the tool’s analysis of possible workload migrations from AWS’s Frankfurt data center. The Carbon Cloud Footprint project estimates AWS’s Frankfurt data center to have relatively high grid carbon emissions compared with the rest of Europe. Migration to Stockholm, Milan, Paris or London all provide significant reductions in carbon and cost, with a maximum increase in latency of 30 ms.
Figure 1 Cloud Carbon Explorer: migration paths from AWS’s Frankfurt region
The bubble size in Figure 1 represents the grid carbon emissions, and the thickness of the line represents the latency impact (wider equals slower). For example, clicking on the migration path pointer from Frankfurt to Stockholm shows a potential 98% cut in grid emissions. The color of the line indicates impact to cost, with green representing a cost saving (not shown in this example: yellow lines representing <5% cost increases).
Users can also make carbon and cost reductions when using Microsoft Azure or Google Cloud. For instance, the Cloud Carbon Explorer shows that by moving a virtual machine from Google’s Hong Kong data center to Taiwan, grid emissions drop 14% and the cost of an e2-standard-2 virtual machine decreases by 17%. The trade-off is a slight increase of round-trip latency of 13 ms. In another example, Microsoft Azure users can reduce carbon and cost by migrating their workloads from Iowa (US) to Illinois (US). For a slight increase in latency of 13 ms, the cost of a D2as_v5 virtual machine drops by 12% and grid carbon emissions decrease by 17%.
The Cloud Carbon Explorer provides indicative carbon, cost and latency figures based on various assumptions. A lack of data is a significant problem for users in calculating their cloud carbon footprints. This difficulty in acquiring the appropriate data is the reason Uptime Intelligence have used third-party sources and were not able to evaluate all regions for all cloud providers. In addition, the individual characteristics of specific data centers (such as power usage effectiveness variations) have not been considered due to a lack of comprehensive information. Although the analysis is imperfect, it does demonstrate, however, that there are suitable trade-offs to be made.
Organizations should commit to cutting carbon emissions, partly for regulatory reasons and also because sustainability is high on consumer and corporate agendas. However, moving region isn’t always simple, and there are legal and latency repercussions to consider. As discussed in our report How resiliency drives cloud carbon emissions, before users consider migrating workloads, they should investigate whether the application can be tuned to reduce carbon.
The next step for users to reduce their carbon (and cost) involves the choice of data center. Users need to balance the benefits of carbon reduction (sometimes coupled with a cost reduction) against the impact of a latency increase.
Uptime Intelligence believes the third-party data sources used in the Cloud Carbon Explorer are reliable and fair, but we have not audited them in depth. Users should use the interactive maps to evaluate feasible migrations before performing their own more detailed assessments. Our analysis suggests that these investigations are worthwhile, given the potential savings in both carbon and costs.
https://journal.uptimeinstitute.com/wp-content/uploads/2022/10/Cloud-Carbon-featured.jpg6281200Dr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]https://journal.uptimeinstitute.com/wp-content/uploads/2022/12/uptime-institute-logo-r_240x88_v2023-with-space.pngDr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]2022-10-17 09:00:002022-10-11 14:58:13Sacrifice speed to cut cloud carbon and costs
IT hardware vendors, such as Dell and Hewlett Packard Enterprise (HPE), are pivoting their revenue models away from product sales toward service-based subscriptions. The objective is to make hardware appear more flexible and cloud-like so buyers can retain servers in their choice of data center while receiving the critical benefit of the cloud: scalability.
Much of the value of the public cloud is in its pay-as-you-go consumption model. Scalability is an application’s ability to consume more or fewer resources as needed (see Cloud scalability and resiliency from first principles). Scalability is only possible if the user is billed in arrears based on resources consumed. This model allows IT to address changing business needs without having to provision capacity far in advance. The provider, not the user, is primarily responsible for infrastructure capacity planning.
To provide this flexibility, public cloud providers provision far more capacity than is likely to be needed. This excess allows users to consume at will without needing to reserve capacity in advance. The aggregation of demand from thousands of users allows hyperscaler cloud providers to overprovision capacity and still make a profit.
Server utilization is a critical metric in the cloud business model. The hyperscale provider faces a balancing act: too many servers sitting unused equates to waste, and waste equates to sunk costs. Too few servers and the principal value of the cloud, namely the ability for users to scale spontaneously, falls apart.
Organizations using physical servers in their own or colocation facilities (including for reasons of compliance, data sovereignty or performance) face the same capacity-management problem. But unlike hyperscalers, they neither have the benefit of demand aggregation at scale, nor the budget to buy and host many empty servers on a just-in-case basis.
Server vendors are changing their business models to allow the leasing of hardware on a more flexible basis. In this hardware as a service or consumption-based model, the buyer commits to a monthly minimum capacity of resources, usually expressed in central processing unit (CPU) cores, memory and storage. The provider supplies a server (to the user’s data center or colocation facility) that delivers at a minimum this capacity, but also builds in a buffer of capacity — usually 10% to 20% above the user’s requirement. The organization is billed at the end of each month for its minimum commitment, plus any additional capacity consumed.
The vendor provides a software portal that tracks consumption and capacity. If consumption regularly breaches the user’s committed capacity, the user is alerted and a new server can be ordered and delivered to the organization easily. Server maintenance is included.
Leasing hardware in this way isn’t necessarily cheaper than purchasing upfront, but the key benefits for the user include being able to switch from capital expense to operating expense, plus a lower capacity-management burden. From the vendor’s point of view, as a service models provide a defense against the pull of the public cloud. They also help build a more relationship-led business by proactively responding to customers’ capacity issues.
HPE pioneered this model with GreenLake. It is pivoting to a services-first approach, away from its established reputation as a hardware manufacturer. HPE GreenLake includes computing and storage hardware plus integrations with software such as cloud stacks, container platforms and multicloud portals. The GreenLake portfolio now includes more than 70 products.
Cloud integration is a critical use case. HPE provides the hardware and software providers such as VMware, Microsoft, Red Hat and Google provide the cloud software, enabling pay-as-you-go private clouds. Public cloud integrations also allow organizations to build hybrid clouds that run across public and private venues using the same platform and are charged on a consumption basis.
This new approach appears to be working. In Q2 2022, annualized revenue run rate for HPE’s as a service offerings reached $820M, bringing the total number of customers to over 1,600. The company reports that Q2 2022 orders are up by 107%, compared with Q2 2021.
Dell, too, is pivoting to a services-first approach with its APEX portfolio. As with HPE, APEX will offer hardware owned, managed and maintained by Dell and billed using a subscription model. Launched in 2021, the success of APEX is not yet evident, but Dell is seeing the model as mission-critical to its future. Other major hardware vendors, including Cisco, Hitachi and Lenovo have also introduced as a service models (called Cisco Plus, Hitachi EverFlex and Lenovo TruScale).
Organizations should consider consumption-based servers. There is no (or little) capital investment, the provider takes responsibility for some aspects of capacity planning and maintenance, and physical hardware can be consumed flexibly in a cloud-like model. However, capacity isn’t guaranteed: if unexpected resource demands overrun the capacity buffer, there may be performance issues while more servers are delivered to the site.
Is a consumption-based server cheaper than a purchased one? It depends on several factors, such as the contract term, the server model, the commitment and the utilization. For example, if the user over-commits to a minimum capacity, it may pay more than if it bought a smaller server upfront. Furthermore, the vendor still owns the server at the end of the term. There is no resell or trade-in value, which impacts the buyer’s return on investment. Hardware leasing models could be good news for colocation operators because it removes the capital requirement to run servers in their facilities. The model opens new revenue streams for managed services providers: could partnerships and packages that unify pay-as-you-go data center capacity, hardware and software attract capital-stricken customers?
https://journal.uptimeinstitute.com/wp-content/uploads/2022/09/Server-leasing-models-featured.jpg6281200Dr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]https://journal.uptimeinstitute.com/wp-content/uploads/2022/12/uptime-institute-logo-r_240x88_v2023-with-space.pngDr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]2022-10-12 08:00:002022-09-15 10:59:16New server leasing models promise cloud-like flexibility
Quantum computing promises a revolution in scientific discovery. Quantum computing’s main advantage over digital computing is in quickly solving highly complex problems that require significant time or resources to process. Currently, solutions to many complex problems can be estimated using supercomputers or pools of servers over days or weeks. Other problems are so complex that they cannot be solved in human timescales using today’s technology. Although research is progressing, there is no guarantee that a practically useful quantum computer can ever be realized.
The impact of a fault-tolerant quantum computer on science and engineering would be vast. For example, it could help the global economy reduce global carbon emissions by finding a better process for producing ammonia than the currently used, energy-heavy Haber-Bosch process.
For companies involved in quantum computing, such as IBM, Microsoft, Honeywell, Amazon, D-Wave, Intel and IQM, quantum computing presents a significant new revenue stream. But it threatens to displace existing applications that use supercomputers or server pools to estimate solutions to tough problems.
Data centers, physical servers and applications used for complex processing tasks could be most vulnerable to being displaced by quantum computing, particularly modeling applications in fields such as finance, biochemistry, and engineering. For many applications, however, the cost and complexity of quantum will not guarantee a return on investment. Most quantum computer prototypes today require operating temperatures near absolute zero, huge upfront investments (although many are now accessible remotely as a cloud) and very specialized skills — this is a significant and costly undertaking.
So, what is quantum computing? In a digital computer, chips use transistors that are either switched on or off, representing 1s and 0s. Digital circuits use this mechanism to perform logic and memory functions, ultimately enabling complex IT systems and software. Electrical signals, typically encoding binary information, represent the flow of digital information.
Digital computers are deterministic systems, meaning they need to proceed with calculations step by step and can only mimic randomness in mathematics and the chaotic nature of the real world. They have another limitation: their numerical precision means they can only approximate when modeling nature. Even with high clock speeds and the massive parallelism of processing resources of supercomputers, there are many complex problems classical computers cannot practically attack. There are too many steps to go through or too much simplification required to be practical. Financial modeling, route optimization and protein folding in molecular biology are just a few examples of areas where classical computing hinders progress.
The quantum in quantum computing refers to tiny particles on the very limit of measurement. At the quantum scale, particles act spontaneously and randomly. Particles only assume a known state (i.e., their movement and position) once they are measured. A quantum particle “decides” what it is doing only when observed. Consequently, the results of observations are probabilistic. Unlike the clear, definite states of traditional computers, quantum bits, called qubits in a quantum computer, take multiple probabilistic states (or superpositions) between 0 and 1. The unobserved particle has essentially yet to decide its definite state.
Rather than looking through every possible combination of a problem, quantum computers use the inherent uncertainty in quantum particles and apply physics to solve the problem. Imagine we are trying to find the correct combination from a sequence of 0s and 1s that lies between 000 and 111. We could either go through every possible combination in series using a digital computer, or we could create three qubits and put them into a superposition state where we don’t know what they are doing. We can then perform operations on these qubits using lasers or electromagnetic fields.
Crucially, we manipulate these particles without looking at them — they remain in superposition. Once we have performed our operations, we then measure the particle’s state. The uncertainty between 0 and 1 then “collapses” into the correct combination.
This highly simplified example may introduce more questions than provide answers. The concepts in play at a quantum level are not intuitive, and the math is not simple. The critical point is that a quantum computer allows operations to be performed across a vast range of potential answers in as little as one step. In a digital computer, each possible solution must be investigated separately.
In the real world, however, building a quantum computer large enough to be practical has so far proved elusive. The problem is that controlling and protecting quantum states at such small scales is arduously tricky. Any interference, such as a stray particle, may bump into the qubit particle, changing its state by accident and leading to a loss of coherence. Errors that prevent coherence from lasting long enough to perform valuable and correct calculations are the biggest block to a viable quantum computer. The more qubits, the more difficult these errors are to control. Fault-tolerant quantum computers with hundreds, or even thousands, of useful qubits are the holy grail of quantum computing research.
Even though there is no guarantee that large-scale quantum computing is viable, scientists, government sponsors and investors either want to believe it is possible or don’t want to risk missing out on its daunting possibilities. Perhaps a more prominent indicator is that there are reports that some governments are collecting encrypted data in the hope they can crack it using a quantum computer later. As such, quantum-proof encryption is increasingly being considered in high-security use cases. But even quantum pioneers say we’re at least a decade away from a practical quantum computer.
Even if quantum computing is realized technically, it will probably not be as disruptive as some have forecasted. Practicalities, such as accessing a quantum computer and programming it, are far from simple. Cost will be prohibitive for many applications. As a result, it will not displace digital computers, including supercomputers, either. Instead, quantum computers will augment existing computing infrastructure as a new accelerated computing platform.
https://journal.uptimeinstitute.com/wp-content/uploads/2022/09/Quantum-Computing-featuring.jpg6281200Dr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]https://journal.uptimeinstitute.com/wp-content/uploads/2022/12/uptime-institute-logo-r_240x88_v2023-with-space.pngDr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]2022-10-05 08:00:002022-09-14 14:25:39Quantum computing is not a panacea yet
Although big-name hyperscalers such as Amazon, Google and Microsoft dominate the cloud arena, other companies also believe they have a role to play. Vultr, OVHcloud, Linode, DigitalOcean, Bluehost and Scaleway, for example, don’t offer huge portfolios of cutting-edge products but their products focus on simplicity and low cost for smaller businesses with relatively straightforward requirements.
The hallmark of the cloud is the ability to provision resources remotely and pay by credit card. But these resources are no longer just virtual machines — hyperscalers have evolved to offer vast portfolios, from software containers to artificial intelligence. Microsoft Azure claims to offer 200 products and services, for example, with each product having different variations and sizes in different regions and at different prices. Hyperscaler cloud providers have millions of individual line items for sale. Users must choose the best combination of these products for their requirements and architect them into an application.
Not all users need to build highly scalable applications across international borders utilizing the latest technology. Many companies just want to develop simple web applications using standard tools and are willing to use standard products to deliver their applications, rather than demanding specific capabilities.
With limited portfolios and limited variations, smaller alternative cloud providers can focus on delivering a few products well:
They focus innovation on squeezing costs and maximizing efficiency rather than on developing broad ranges of cutting-edge products.
Most have a handful of locations offering a few services (usually virtual machines, storage and, more recently, containers) in a limited range of configurations (such as a few different sizes of virtual machines).
They can focus on a region or country so that specific local demands for data sovereignty or ownership can be met.
Customer service and support is often more personal with an alternative cloud provider than a hyperscaler.
Hyperscalers are upfront that users must build resiliency in their applications using multiple availability zones (akin to data centers) and cloud services such as load balancers (see Public cloud costs versus resiliency: stateless applications). Alternative cloud providers don’t necessarily offer such capabilities. Users of alternative cloud providers often seek simplicity, without the need to architect decoupled, multivenue applications. The result is that users – almost inadvertently – rely more heavily on alternative cloud providers’ data centers to deliver resiliency than they might in a hyperscaler data center, where the application itself has been designed to be resilient. This dynamic is difficult to quantify but there are examples. Last year, a fire destroyed one of OVHcloud’s Strasbourg (France) data centers. The local fire service said the data center had neither an automatic fire extinguisher system nor an electrical cut-off mechanism. OVHcloud is now facing a class action lawsuit from 140 clients that were impacted, demonstrating the reliance these companies had on OVHcloud.
The challenge with the alternative provider model is that it reduces cloud services to a commodity where price, not innovation or quality, is a differentiator. As a result, alternative providers are under more pressure than hyperscalers to keep prices low. With less diverse portfolios, price pressure on a single service can impact overall margins more than a hyperscaler with other services to offset any losses. OVHcloud’s fire demonstrates that simplicity doesn’t necessarily mean resiliency.
In June 2022, DigitalOcean increased prices on nearly all its services. We believe some of this price increase is due to rising costs. Inflation is high in many economies and supply chain issues are affecting the timely and inexpensive delivery of servers and other equipment. The COVID-19 pandemic has triggered a movement of workers, reducing supply and, anecdotally, raising salaries. An innovation-driven hyperscaler might be able to absorb some of these costs in its margins; it is harder for a cost-differentiated alternative cloud provider.
In the short term, users may question whether they are getting enough value from alternative cloud providers to justify a larger bill (because of price increases). Migrating from an alternative cloud provider can be simpler than from a hyperscaler as they offer simple services with have many commonalities across providers and are, therefore, easier to move.
Hyperscaler services are usually more proprietary, including application programming interfaces coded into the fabric of applications that can be expensive to migrate. In addition, hyperscalers are increasingly opening new data centers in new countries, offsetting some of the alternative cloud providers’ value in locality. As a result, alternative cloud providers are more vulnerable to internal cost increases and buyers demanding lower prices than hyperscalers.
Alternative cloud providers are also vulnerable to a broader demand for cutting-edge technology. In the longer term, will cloud users see IT as something that has to operate cheaply and simply while the business focuses on bigger things, or will they want to pay more for their IT and invest in skills to build more complex yet strategically important applications? Small businesses want to use innovative technologies such as the internet of things, cloud-native development or machine learning to build differentiated and resilient applications that drive the business forward — even if these innovations come at a cost premium.
https://journal.uptimeinstitute.com/wp-content/uploads/2022/08/Cloud-Alternative-featured.jpg6281200Dr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]https://journal.uptimeinstitute.com/wp-content/uploads/2022/12/uptime-institute-logo-r_240x88_v2023-with-space.pngDr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]2022-09-28 08:00:002022-08-29 11:53:33Alternative clouds are vulnerable to demanding buyers
The value, role and safety of nuclear power has strongly divided opinion, both in favor of and against, since the 1950s. This debate has now, in 2022, reached a critical point again as energy security and prices cause increasing concern globally (particularly in geographies such as Europe) and as the climate crisis requires energy producers to shift toward either non- or low-carbon sources — including nuclear.
At the beginning of 2022, Uptime Institute Intelligence forecast that data center operators, in their search for low-carbon, firm (non-intermittent) power sources, would increasingly favor — and even lobby for — nuclear power. The Uptime Institute Global Data Center Survey 2022 shows that data center operators / owners in major data center economies around the world are cautiously in favor of nuclear power. There are, however, significant regional differences (see Figure 1).
Figure 1 Nuclear is needed, say operators in most regions
In both North America and Europe, about three-quarters of data center operators believe nuclear should either play a core long-term role in providing grid power or is necessary for a period of transition. However, Europeans are more wary, with 35% saying nuclear should only play a temporary or transitional role (compared with just 23% in North America).
In Europe, attitudes to nuclear power are complex and politicized. Following the Chernobyl and Fukushima nuclear accidents, green parties in Europe lobbied strongly against nuclear power, with Germany eventually deciding to close all its nuclear power plants. More recently, the Russian invasion of Ukraine has exposed Germany’s over-reliance on energy imports, and many have called for a halt to this nuclear shutdown.
In the US, there is greater skepticism among the general population about climate change being caused by humans, and consequently surveys record lower levels of concern about carbon emissions. Uptime Intelligence’s survey appears to show data center operators in North America also have lower levels of concern about nuclear safety, given their greater willingness for nuclear to play a core role (Figure 1). As the issues of climate change and energy security intensify, this gap in opinion between the US and Europe is likely to close in the years ahead.
In China, not a single respondent thought nuclear power should be phased out — perhaps reflecting both its government’s stance and a strong faith in technology. China, more than most countries, faces major challenges meeting energy requirements and simultaneously reducing carbon emissions.
In Latin America, and Africa and the Middle East, significantly lower proportions of data center operators think nuclear power should play a key role. This may reflect political reality: there is far less nuclear power already in use in those regions, and concerns about political stability and nuclear proliferation (and cost) will likely limit even peaceful nuclear use. In practice, data center operators will not have a major impact on the use (or non-use) of nuclear power. Decisions will primarily be made by grid-scale investors and operators and will be steered by government policy. However, large-scale energy buyers can make investments more feasible — and existing plants more economic — if they choose to class nuclear power as a renewable (zero-carbon) energy source and include nuclear in power purchase agreements. They can also benefit by siting their data centers in regions where nuclear is a major energy source. Early-stage discussions around the use of small modular reactors (SMRs) for large data center campuses (see Data center operators ponder the nuclear option) are, at present, just that — exploratory discussions.
https://journal.uptimeinstitute.com/wp-content/uploads/2022/09/Nuclear-2-featured.jpg6281200Andy Lawrence, Executive Director of Research, Uptime Institute, [email protected]https://journal.uptimeinstitute.com/wp-content/uploads/2022/12/uptime-institute-logo-r_240x88_v2023-with-space.pngAndy Lawrence, Executive Director of Research, Uptime Institute, [email protected]2022-09-21 08:30:002022-09-14 09:51:51Data center operators cautiously support nuclear
The thousands of gallons of diesel that data centers store on-site to fuel backup generators in the event of a grid outage are hard to ignore — and do little to assist operators’ drive toward tougher sustainability goals.
In the past two years, an alternative to diesel made from renewable materials has found support among a small number of data center operators: hydrotreated vegetable oil (HVO). HVO is a second-generation biofuel, chemically distinct from “traditional” biodiesel. It can be manufactured from waste food stocks, raw plant oils, used cooking oils or animal fats; and either blended with petroleum diesel or used in 100% concentrations.
This novel fuel reduces carbon dioxide (CO2) emissions by up to 90%, particulate matter by 10% to 30% and nitrogen oxides by 6% to 15%, when compared with petroleum diesel.
Operators that have deployed, or are testing, 100% HVO in their data centers include colocation provider Kao Data, Ark Data Centres, and Datum Datacentres in the UK, Compass Datacenters in the US, Stack Infrastructure in Canada and Interxion in France. The largest colocation provider Equinix said in its most recent 2021 sustainability report that it was piloting HVO at multiple sites and investigating its supply chain for a “transition away from diesel.” Microsoft is using a blend of petroleum diesel and at least 50% HVO in its cloud data centers in Sweden, which were launched at the end of 2021.
There are several benefits to using 100% HVO in addition to lowered carbon footprint (see Table 1). It can be stored for up to 10 years without major changes to its properties, unlike petroleum diesel, which can only be stored for up to a year. HVO is free from fatty acid methyl ester (FAME), which means it is not susceptible to “diesel bug” (a microbial contamination that grows in diesel) and does not require fuel polishing. HVO also has better cold-flow properties than petroleum diesel that simplify operation in colder climates and delivers slightly more power to the engine due to its high cetane value.
Table 1. Considerations when replacing diesel with hydrotreated vegetable oil
Importantly, HVO serves as a drop-in replacement for diesel: it can be stored in existing tanks, used by most (if not all) existing generators and mixed into existing diesel stocks without changes to parts or processes. Generator vendors that have tested and approved 100% HVO for use with their equipment include Rolls-Royce’s mtu, Cummins, Aggreko, Caterpillar and Kohler; others are likely to follow suit.
However, HVO comes at a price premium: in Europe, it costs about 30% more than traditional diesel fuels.
Another issue is HVO’s currently limited supply. Europe’s first HVO refinery was opened in Finland by oil refiner Neste in 2007; the company remains the world’s largest HVO producer, with four plants in operation.
In the US, there were five commercial HVO plants as of 2020, with a combined capacity of over 590 million gallons per year, according to the US Department of Energy. Most of this capacity was headed to California, due to the economic benefits under the state’s low-carbon fuel standard. For comparison, refineries in the US produced around 69.7 billion gallons of ultra-low-sulfur diesel in 2020.
Production of HVO has been expanding rapidly, owing to its potential as a sustainable aviation fuel. Biofuel broker Greenea expects the number of pure and co-processing HVO plants to triple in the EU, triple in Asia, and rise six-fold in the US between 2020 and 2025.
There is also a potential issue relating to source materials: while HVO can be made from used cooking oils, it can easily be made from palm oil – which can contribute to deforestation and other environmental issues. Large HVO producers are phasing out palm oil from their supply chains; the largest, Neste, plans to achieve this by the end of 2023. Visibility into manufacturing practices and distribution will be a key consideration if HVO is part of data center sustainability strategies.
What is the scale of the potential environmental impact of the use of HVO on data center emissions? While highly resilient data centers are often designed to rely on gensets as their primary source of power, they are rarely used for this purpose in mature data center regions, such as the US and Western Europe, because the electrical grids are relatively stable. Generator testing is, therefore, the main application for HVO in most data centers.
A data center might run each of its generators for only 2 hours of testing per month (24 hours / year). A typical industrial grade, 1-megawatt (MW)-output diesel generator consumes around 70 gallons of fuel per hour at full load. This means a 10 MW facility that has 10 1-MW generators requires up to 16,800 gallons of diesel per year for testing purposes, emitting about 168 metric tons of carbon.
Uptime Institute estimates that for a data center located in Virginia (US) operating at 75% utilization, such a testing regimen would represent just 0.8% of a facility’s Scope 1 and 2 emissions.
The impact on emissions would be greater if the generators were used for longer periods of time. If the same facility used generators to deliver 365 hours of backup power per year, in addition to testing, their contribution to overall Scope 1 and 2 emissions would increase to 8%. In this scenario, a switch to HVO would deliver higher emission reductions.
The relative environmental benefits of switching to HVO would be higher in regions where the grid is most dependent on fossil fuels. However, the impact on the share of emissions produced by generators would be much more pronounced in regions that rely on low-carbon energy sources and have a lower grid emissions factor.
A key attraction of using HVO is that operators do not have to choose one type of fuel over another: since it is a drop-in replacement, HVO can be stored on-site for generator testing and supply contracts for petroleum diesel can be arranged for instances when generators need to be operated for longer than 12 hours at a time. Looking ahead, it remains to be seen whether data center operators are willing to pay a premium for HVO to back up their sustainability claims.
https://journal.uptimeinstitute.com/wp-content/uploads/2022/09/HVO-hydrotreated-vegatable-oil-featured.jpg6281200Max Smolaks, Research Analyst, [email protected]https://journal.uptimeinstitute.com/wp-content/uploads/2022/12/uptime-institute-logo-r_240x88_v2023-with-space.pngMax Smolaks, Research Analyst, [email protected]2022-09-14 08:00:002022-09-12 15:46:56Vegetable oil promises a sustainable alternative to diesel
Sacrifice speed to cut cloud carbon and costs
/in Executive, Operations/by Dr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]New findings from research by Uptime Institute Intelligence reveals that organizations can cut both their cloud carbon emissions and costs by moving workloads to different regions. However, the trade off with this migration is an increase in latency.
Cloud users choose regions based primarily on two factors:
If there are no legal reasons to keep data in a jurisdiction, cloud users can often migrate their workloads to a nearby region and gain reductions in their carbon footprint; this migration can also result in lower costs. Uptime Intelligence collated information from Microsoft Azure, Amazon Web Services (AWS), Google Cloud, the Cloud Carbon Footprint project (which sources data from carbonfootprint.com, the European Environment Agency and the US Environmental Protection Agency) and CloudPing to produce the Cloud Carbon Explorer, which includes three interactive maps.
The maps show the potential cross-region workload migration paths for AWS, Google Cloud and Microsoft Azure. These workload migration paths can reduce carbon footprint without significantly impacting user experience and, in some cases, reduce cost. Users can use the tool to explore suitable compromises of latency, cost and carbon for each application.
We found 38 migration paths between AWS regions that provide both carbon and cost reductions with a latency impact of less than 100 milliseconds (ms). For Google Cloud, there were 39 migrations paths and for Microsoft Azure there were 15 migration paths, all with a latency impact of <100 ms.
Figure 1 shows the tool’s analysis of possible workload migrations from AWS’s Frankfurt data center. The Carbon Cloud Footprint project estimates AWS’s Frankfurt data center to have relatively high grid carbon emissions compared with the rest of Europe. Migration to Stockholm, Milan, Paris or London all provide significant reductions in carbon and cost, with a maximum increase in latency of 30 ms.
The bubble size in Figure 1 represents the grid carbon emissions, and the thickness of the line represents the latency impact (wider equals slower). For example, clicking on the migration path pointer from Frankfurt to Stockholm shows a potential 98% cut in grid emissions. The color of the line indicates impact to cost, with green representing a cost saving (not shown in this example: yellow lines representing <5% cost increases).
Users can also make carbon and cost reductions when using Microsoft Azure or Google Cloud. For instance, the Cloud Carbon Explorer shows that by moving a virtual machine from Google’s Hong Kong data center to Taiwan, grid emissions drop 14% and the cost of an e2-standard-2 virtual machine decreases by 17%. The trade-off is a slight increase of round-trip latency of 13 ms. In another example, Microsoft Azure users can reduce carbon and cost by migrating their workloads from Iowa (US) to Illinois (US). For a slight increase in latency of 13 ms, the cost of a D2as_v5 virtual machine drops by 12% and grid carbon emissions decrease by 17%.
The Cloud Carbon Explorer provides indicative carbon, cost and latency figures based on various assumptions. A lack of data is a significant problem for users in calculating their cloud carbon footprints. This difficulty in acquiring the appropriate data is the reason Uptime Intelligence have used third-party sources and were not able to evaluate all regions for all cloud providers. In addition, the individual characteristics of specific data centers (such as power usage effectiveness variations) have not been considered due to a lack of comprehensive information. Although the analysis is imperfect, it does demonstrate, however, that there are suitable trade-offs to be made.
Organizations should commit to cutting carbon emissions, partly for regulatory reasons and also because sustainability is high on consumer and corporate agendas. However, moving region isn’t always simple, and there are legal and latency repercussions to consider. As discussed in our report How resiliency drives cloud carbon emissions, before users consider migrating workloads, they should investigate whether the application can be tuned to reduce carbon.
The next step for users to reduce their carbon (and cost) involves the choice of data center. Users need to balance the benefits of carbon reduction (sometimes coupled with a cost reduction) against the impact of a latency increase.
Uptime Intelligence believes the third-party data sources used in the Cloud Carbon Explorer are reliable and fair, but we have not audited them in depth. Users should use the interactive maps to evaluate feasible migrations before performing their own more detailed assessments. Our analysis suggests that these investigations are worthwhile, given the potential savings in both carbon and costs.
New server leasing models promise cloud-like flexibility
/in Executive, Operations/by Dr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]IT hardware vendors, such as Dell and Hewlett Packard Enterprise (HPE), are pivoting their revenue models away from product sales toward service-based subscriptions. The objective is to make hardware appear more flexible and cloud-like so buyers can retain servers in their choice of data center while receiving the critical benefit of the cloud: scalability.
Much of the value of the public cloud is in its pay-as-you-go consumption model. Scalability is an application’s ability to consume more or fewer resources as needed (see Cloud scalability and resiliency from first principles). Scalability is only possible if the user is billed in arrears based on resources consumed. This model allows IT to address changing business needs without having to provision capacity far in advance. The provider, not the user, is primarily responsible for infrastructure capacity planning.
To provide this flexibility, public cloud providers provision far more capacity than is likely to be needed. This excess allows users to consume at will without needing to reserve capacity in advance. The aggregation of demand from thousands of users allows hyperscaler cloud providers to overprovision capacity and still make a profit.
Server utilization is a critical metric in the cloud business model. The hyperscale provider faces a balancing act: too many servers sitting unused equates to waste, and waste equates to sunk costs. Too few servers and the principal value of the cloud, namely the ability for users to scale spontaneously, falls apart.
Organizations using physical servers in their own or colocation facilities (including for reasons of compliance, data sovereignty or performance) face the same capacity-management problem. But unlike hyperscalers, they neither have the benefit of demand aggregation at scale, nor the budget to buy and host many empty servers on a just-in-case basis.
Server vendors are changing their business models to allow the leasing of hardware on a more flexible basis. In this hardware as a service or consumption-based model, the buyer commits to a monthly minimum capacity of resources, usually expressed in central processing unit (CPU) cores, memory and storage. The provider supplies a server (to the user’s data center or colocation facility) that delivers at a minimum this capacity, but also builds in a buffer of capacity — usually 10% to 20% above the user’s requirement. The organization is billed at the end of each month for its minimum commitment, plus any additional capacity consumed.
The vendor provides a software portal that tracks consumption and capacity. If consumption regularly breaches the user’s committed capacity, the user is alerted and a new server can be ordered and delivered to the organization easily. Server maintenance is included.
Leasing hardware in this way isn’t necessarily cheaper than purchasing upfront, but the key benefits for the user include being able to switch from capital expense to operating expense, plus a lower capacity-management burden. From the vendor’s point of view, as a service models provide a defense against the pull of the public cloud. They also help build a more relationship-led business by proactively responding to customers’ capacity issues.
HPE pioneered this model with GreenLake. It is pivoting to a services-first approach, away from its established reputation as a hardware manufacturer. HPE GreenLake includes computing and storage hardware plus integrations with software such as cloud stacks, container platforms and multicloud portals. The GreenLake portfolio now includes more than 70 products.
Cloud integration is a critical use case. HPE provides the hardware and software providers such as VMware, Microsoft, Red Hat and Google provide the cloud software, enabling pay-as-you-go private clouds. Public cloud integrations also allow organizations to build hybrid clouds that run across public and private venues using the same platform and are charged on a consumption basis.
This new approach appears to be working. In Q2 2022, annualized revenue run rate for HPE’s as a service offerings reached $820M, bringing the total number of customers to over 1,600. The company reports that Q2 2022 orders are up by 107%, compared with Q2 2021.
Dell, too, is pivoting to a services-first approach with its APEX portfolio. As with HPE, APEX will offer hardware owned, managed and maintained by Dell and billed using a subscription model. Launched in 2021, the success of APEX is not yet evident, but Dell is seeing the model as mission-critical to its future. Other major hardware vendors, including Cisco, Hitachi and Lenovo have also introduced as a service models (called Cisco Plus, Hitachi EverFlex and Lenovo TruScale).
Organizations should consider consumption-based servers. There is no (or little) capital investment, the provider takes responsibility for some aspects of capacity planning and maintenance, and physical hardware can be consumed flexibly in a cloud-like model. However, capacity isn’t guaranteed: if unexpected resource demands overrun the capacity buffer, there may be performance issues while more servers are delivered to the site.
Is a consumption-based server cheaper than a purchased one? It depends on several factors, such as the contract term, the server model, the commitment and the utilization. For example, if the user over-commits to a minimum capacity, it may pay more than if it bought a smaller server upfront. Furthermore, the vendor still owns the server at the end of the term. There is no resell or trade-in value, which impacts the buyer’s return on investment. Hardware leasing models could be good news for colocation operators because it removes the capital requirement to run servers in their facilities. The model opens new revenue streams for managed services providers: could partnerships and packages that unify pay-as-you-go data center capacity, hardware and software attract capital-stricken customers?
Quantum computing is not a panacea yet
/in Design, Executive, Operations/by Dr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]Quantum computing promises a revolution in scientific discovery. Quantum computing’s main advantage over digital computing is in quickly solving highly complex problems that require significant time or resources to process. Currently, solutions to many complex problems can be estimated using supercomputers or pools of servers over days or weeks. Other problems are so complex that they cannot be solved in human timescales using today’s technology. Although research is progressing, there is no guarantee that a practically useful quantum computer can ever be realized.
The impact of a fault-tolerant quantum computer on science and engineering would be vast. For example, it could help the global economy reduce global carbon emissions by finding a better process for producing ammonia than the currently used, energy-heavy Haber-Bosch process.
For companies involved in quantum computing, such as IBM, Microsoft, Honeywell, Amazon, D-Wave, Intel and IQM, quantum computing presents a significant new revenue stream. But it threatens to displace existing applications that use supercomputers or server pools to estimate solutions to tough problems.
Data centers, physical servers and applications used for complex processing tasks could be most vulnerable to being displaced by quantum computing, particularly modeling applications in fields such as finance, biochemistry, and engineering. For many applications, however, the cost and complexity of quantum will not guarantee a return on investment. Most quantum computer prototypes today require operating temperatures near absolute zero, huge upfront investments (although many are now accessible remotely as a cloud) and very specialized skills — this is a significant and costly undertaking.
So, what is quantum computing? In a digital computer, chips use transistors that are either switched on or off, representing 1s and 0s. Digital circuits use this mechanism to perform logic and memory functions, ultimately enabling complex IT systems and software. Electrical signals, typically encoding binary information, represent the flow of digital information.
Digital computers are deterministic systems, meaning they need to proceed with calculations step by step and can only mimic randomness in mathematics and the chaotic nature of the real world. They have another limitation: their numerical precision means they can only approximate when modeling nature. Even with high clock speeds and the massive parallelism of processing resources of supercomputers, there are many complex problems classical computers cannot practically attack. There are too many steps to go through or too much simplification required to be practical. Financial modeling, route optimization and protein folding in molecular biology are just a few examples of areas where classical computing hinders progress.
The quantum in quantum computing refers to tiny particles on the very limit of measurement. At the quantum scale, particles act spontaneously and randomly. Particles only assume a known state (i.e., their movement and position) once they are measured. A quantum particle “decides” what it is doing only when observed. Consequently, the results of observations are probabilistic. Unlike the clear, definite states of traditional computers, quantum bits, called qubits in a quantum computer, take multiple probabilistic states (or superpositions) between 0 and 1. The unobserved particle has essentially yet to decide its definite state.
Rather than looking through every possible combination of a problem, quantum computers use the inherent uncertainty in quantum particles and apply physics to solve the problem. Imagine we are trying to find the correct combination from a sequence of 0s and 1s that lies between 000 and 111. We could either go through every possible combination in series using a digital computer, or we could create three qubits and put them into a superposition state where we don’t know what they are doing. We can then perform operations on these qubits using lasers or electromagnetic fields.
Crucially, we manipulate these particles without looking at them — they remain in superposition. Once we have performed our operations, we then measure the particle’s state. The uncertainty between 0 and 1 then “collapses” into the correct combination.
This highly simplified example may introduce more questions than provide answers. The concepts in play at a quantum level are not intuitive, and the math is not simple. The critical point is that a quantum computer allows operations to be performed across a vast range of potential answers in as little as one step. In a digital computer, each possible solution must be investigated separately.
In the real world, however, building a quantum computer large enough to be practical has so far proved elusive. The problem is that controlling and protecting quantum states at such small scales is arduously tricky. Any interference, such as a stray particle, may bump into the qubit particle, changing its state by accident and leading to a loss of coherence. Errors that prevent coherence from lasting long enough to perform valuable and correct calculations are the biggest block to a viable quantum computer. The more qubits, the more difficult these errors are to control. Fault-tolerant quantum computers with hundreds, or even thousands, of useful qubits are the holy grail of quantum computing research.
Even though there is no guarantee that large-scale quantum computing is viable, scientists, government sponsors and investors either want to believe it is possible or don’t want to risk missing out on its daunting possibilities. Perhaps a more prominent indicator is that there are reports that some governments are collecting encrypted data in the hope they can crack it using a quantum computer later. As such, quantum-proof encryption is increasingly being considered in high-security use cases. But even quantum pioneers say we’re at least a decade away from a practical quantum computer.
Even if quantum computing is realized technically, it will probably not be as disruptive as some have forecasted. Practicalities, such as accessing a quantum computer and programming it, are far from simple. Cost will be prohibitive for many applications. As a result, it will not displace digital computers, including supercomputers, either. Instead, quantum computers will augment existing computing infrastructure as a new accelerated computing platform.
Alternative clouds are vulnerable to demanding buyers
/in Executive, Operations/by Dr. Owen Rogers, Research Director for Cloud Computing, Uptime Institute, [email protected]Although big-name hyperscalers such as Amazon, Google and Microsoft dominate the cloud arena, other companies also believe they have a role to play. Vultr, OVHcloud, Linode, DigitalOcean, Bluehost and Scaleway, for example, don’t offer huge portfolios of cutting-edge products but their products focus on simplicity and low cost for smaller businesses with relatively straightforward requirements.
The hallmark of the cloud is the ability to provision resources remotely and pay by credit card. But these resources are no longer just virtual machines — hyperscalers have evolved to offer vast portfolios, from software containers to artificial intelligence. Microsoft Azure claims to offer 200 products and services, for example, with each product having different variations and sizes in different regions and at different prices. Hyperscaler cloud providers have millions of individual line items for sale. Users must choose the best combination of these products for their requirements and architect them into an application.
Not all users need to build highly scalable applications across international borders utilizing the latest technology. Many companies just want to develop simple web applications using standard tools and are willing to use standard products to deliver their applications, rather than demanding specific capabilities.
With limited portfolios and limited variations, smaller alternative cloud providers can focus on delivering a few products well:
Hyperscalers are upfront that users must build resiliency in their applications using multiple availability zones (akin to data centers) and cloud services such as load balancers (see Public cloud costs versus resiliency: stateless applications). Alternative cloud providers don’t necessarily offer such capabilities. Users of alternative cloud providers often seek simplicity, without the need to architect decoupled, multivenue applications. The result is that users – almost inadvertently – rely more heavily on alternative cloud providers’ data centers to deliver resiliency than they might in a hyperscaler data center, where the application itself has been designed to be resilient. This dynamic is difficult to quantify but there are examples. Last year, a fire destroyed one of OVHcloud’s Strasbourg (France) data centers. The local fire service said the data center had neither an automatic fire extinguisher system nor an electrical cut-off mechanism. OVHcloud is now facing a class action lawsuit from 140 clients that were impacted, demonstrating the reliance these companies had on OVHcloud.
The challenge with the alternative provider model is that it reduces cloud services to a commodity where price, not innovation or quality, is a differentiator. As a result, alternative providers are under more pressure than hyperscalers to keep prices low. With less diverse portfolios, price pressure on a single service can impact overall margins more than a hyperscaler with other services to offset any losses. OVHcloud’s fire demonstrates that simplicity doesn’t necessarily mean resiliency.
In June 2022, DigitalOcean increased prices on nearly all its services. We believe some of this price increase is due to rising costs. Inflation is high in many economies and supply chain issues are affecting the timely and inexpensive delivery of servers and other equipment. The COVID-19 pandemic has triggered a movement of workers, reducing supply and, anecdotally, raising salaries. An innovation-driven hyperscaler might be able to absorb some of these costs in its margins; it is harder for a cost-differentiated alternative cloud provider.
In the short term, users may question whether they are getting enough value from alternative cloud providers to justify a larger bill (because of price increases). Migrating from an alternative cloud provider can be simpler than from a hyperscaler as they offer simple services with have many commonalities across providers and are, therefore, easier to move.
Hyperscaler services are usually more proprietary, including application programming interfaces coded into the fabric of applications that can be expensive to migrate. In addition, hyperscalers are increasingly opening new data centers in new countries, offsetting some of the alternative cloud providers’ value in locality. As a result, alternative cloud providers are more vulnerable to internal cost increases and buyers demanding lower prices than hyperscalers.
Alternative cloud providers are also vulnerable to a broader demand for cutting-edge technology. In the longer term, will cloud users see IT as something that has to operate cheaply and simply while the business focuses on bigger things, or will they want to pay more for their IT and invest in skills to build more complex yet strategically important applications? Small businesses want to use innovative technologies such as the internet of things, cloud-native development or machine learning to build differentiated and resilient applications that drive the business forward — even if these innovations come at a cost premium.
Data center operators cautiously support nuclear
/in Executive, Operations/by Andy Lawrence, Executive Director of Research, Uptime Institute, [email protected]The value, role and safety of nuclear power has strongly divided opinion, both in favor of and against, since the 1950s. This debate has now, in 2022, reached a critical point again as energy security and prices cause increasing concern globally (particularly in geographies such as Europe) and as the climate crisis requires energy producers to shift toward either non- or low-carbon sources — including nuclear.
At the beginning of 2022, Uptime Institute Intelligence forecast that data center operators, in their search for low-carbon, firm (non-intermittent) power sources, would increasingly favor — and even lobby for — nuclear power. The Uptime Institute Global Data Center Survey 2022 shows that data center operators / owners in major data center economies around the world are cautiously in favor of nuclear power. There are, however, significant regional differences (see Figure 1).
In both North America and Europe, about three-quarters of data center operators believe nuclear should either play a core long-term role in providing grid power or is necessary for a period of transition. However, Europeans are more wary, with 35% saying nuclear should only play a temporary or transitional role (compared with just 23% in North America).
In Europe, attitudes to nuclear power are complex and politicized. Following the Chernobyl and Fukushima nuclear accidents, green parties in Europe lobbied strongly against nuclear power, with Germany eventually deciding to close all its nuclear power plants. More recently, the Russian invasion of Ukraine has exposed Germany’s over-reliance on energy imports, and many have called for a halt to this nuclear shutdown.
In the US, there is greater skepticism among the general population about climate change being caused by humans, and consequently surveys record lower levels of concern about carbon emissions. Uptime Intelligence’s survey appears to show data center operators in North America also have lower levels of concern about nuclear safety, given their greater willingness for nuclear to play a core role (Figure 1). As the issues of climate change and energy security intensify, this gap in opinion between the US and Europe is likely to close in the years ahead.
In China, not a single respondent thought nuclear power should be phased out — perhaps reflecting both its government’s stance and a strong faith in technology. China, more than most countries, faces major challenges meeting energy requirements and simultaneously reducing carbon emissions.
In Latin America, and Africa and the Middle East, significantly lower proportions of data center operators think nuclear power should play a key role. This may reflect political reality: there is far less nuclear power already in use in those regions, and concerns about political stability and nuclear proliferation (and cost) will likely limit even peaceful nuclear use. In practice, data center operators will not have a major impact on the use (or non-use) of nuclear power. Decisions will primarily be made by grid-scale investors and operators and will be steered by government policy. However, large-scale energy buyers can make investments more feasible — and existing plants more economic — if they choose to class nuclear power as a renewable (zero-carbon) energy source and include nuclear in power purchase agreements. They can also benefit by siting their data centers in regions where nuclear is a major energy source. Early-stage discussions around the use of small modular reactors (SMRs) for large data center campuses (see Data center operators ponder the nuclear option) are, at present, just that — exploratory discussions.
Vegetable oil promises a sustainable alternative to diesel
/in Design, Operations/by Max Smolaks, Research Analyst, [email protected]The thousands of gallons of diesel that data centers store on-site to fuel backup generators in the event of a grid outage are hard to ignore — and do little to assist operators’ drive toward tougher sustainability goals.
In the past two years, an alternative to diesel made from renewable materials has found support among a small number of data center operators: hydrotreated vegetable oil (HVO). HVO is a second-generation biofuel, chemically distinct from “traditional” biodiesel. It can be manufactured from waste food stocks, raw plant oils, used cooking oils or animal fats; and either blended with petroleum diesel or used in 100% concentrations.
This novel fuel reduces carbon dioxide (CO2) emissions by up to 90%, particulate matter by 10% to 30% and nitrogen oxides by 6% to 15%, when compared with petroleum diesel.
Operators that have deployed, or are testing, 100% HVO in their data centers include colocation provider Kao Data, Ark Data Centres, and Datum Datacentres in the UK, Compass Datacenters in the US, Stack Infrastructure in Canada and Interxion in France. The largest colocation provider Equinix said in its most recent 2021 sustainability report that it was piloting HVO at multiple sites and investigating its supply chain for a “transition away from diesel.” Microsoft is using a blend of petroleum diesel and at least 50% HVO in its cloud data centers in Sweden, which were launched at the end of 2021.
There are several benefits to using 100% HVO in addition to lowered carbon footprint (see Table 1). It can be stored for up to 10 years without major changes to its properties, unlike petroleum diesel, which can only be stored for up to a year. HVO is free from fatty acid methyl ester (FAME), which means it is not susceptible to “diesel bug” (a microbial contamination that grows in diesel) and does not require fuel polishing. HVO also has better cold-flow properties than petroleum diesel that simplify operation in colder climates and delivers slightly more power to the engine due to its high cetane value.
Importantly, HVO serves as a drop-in replacement for diesel: it can be stored in existing tanks, used by most (if not all) existing generators and mixed into existing diesel stocks without changes to parts or processes. Generator vendors that have tested and approved 100% HVO for use with their equipment include Rolls-Royce’s mtu, Cummins, Aggreko, Caterpillar and Kohler; others are likely to follow suit.
However, HVO comes at a price premium: in Europe, it costs about 30% more than traditional diesel fuels.
Another issue is HVO’s currently limited supply. Europe’s first HVO refinery was opened in Finland by oil refiner Neste in 2007; the company remains the world’s largest HVO producer, with four plants in operation.
In the US, there were five commercial HVO plants as of 2020, with a combined capacity of over 590 million gallons per year, according to the US Department of Energy. Most of this capacity was headed to California, due to the economic benefits under the state’s low-carbon fuel standard. For comparison, refineries in the US produced around 69.7 billion gallons of ultra-low-sulfur diesel in 2020.
Production of HVO has been expanding rapidly, owing to its potential as a sustainable aviation fuel. Biofuel broker Greenea expects the number of pure and co-processing HVO plants to triple in the EU, triple in Asia, and rise six-fold in the US between 2020 and 2025.
There is also a potential issue relating to source materials: while HVO can be made from used cooking oils, it can easily be made from palm oil – which can contribute to deforestation and other environmental issues. Large HVO producers are phasing out palm oil from their supply chains; the largest, Neste, plans to achieve this by the end of 2023. Visibility into manufacturing practices and distribution will be a key consideration if HVO is part of data center sustainability strategies.
What is the scale of the potential environmental impact of the use of HVO on data center emissions? While highly resilient data centers are often designed to rely on gensets as their primary source of power, they are rarely used for this purpose in mature data center regions, such as the US and Western Europe, because the electrical grids are relatively stable. Generator testing is, therefore, the main application for HVO in most data centers.
A data center might run each of its generators for only 2 hours of testing per month (24 hours / year). A typical industrial grade, 1-megawatt (MW)-output diesel generator consumes around 70 gallons of fuel per hour at full load. This means a 10 MW facility that has 10 1-MW generators requires up to 16,800 gallons of diesel per year for testing purposes, emitting about 168 metric tons of carbon.
Uptime Institute estimates that for a data center located in Virginia (US) operating at 75% utilization, such a testing regimen would represent just 0.8% of a facility’s Scope 1 and 2 emissions.
The impact on emissions would be greater if the generators were used for longer periods of time. If the same facility used generators to deliver 365 hours of backup power per year, in addition to testing, their contribution to overall Scope 1 and 2 emissions would increase to 8%. In this scenario, a switch to HVO would deliver higher emission reductions.
The relative environmental benefits of switching to HVO would be higher in regions where the grid is most dependent on fossil fuels. However, the impact on the share of emissions produced by generators would be much more pronounced in regions that rely on low-carbon energy sources and have a lower grid emissions factor.
A key attraction of using HVO is that operators do not have to choose one type of fuel over another: since it is a drop-in replacement, HVO can be stored on-site for generator testing and supply contracts for petroleum diesel can be arranged for instances when generators need to be operated for longer than 12 hours at a time. Looking ahead, it remains to be seen whether data center operators are willing to pay a premium for HVO to back up their sustainability claims.