David Mytton

Paper notes – Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

Published in Data Center Energy, Environment, IT Energy, Paper notes. Tags: , , .

Paper notes – Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

Paper

Zheng, J., Chien, A.A., and Suh, S. (10/2020). Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers. Joule 4, 2208–2222. https://dx.doi.org/10.1016/j.joule.2020.08.001 

Notes

  • Curtailment and long-distance transmission of renewable energy is a major challenge for the transition of the energy system to 100% clean energy. It’s one of the details that many environmentalists forget (or don’t know about) when advocating for rapid decarbonization of the energy system particularly if they only focus on solar and wind. That, and the challenges of variability and energy storage.
  • I try and catch myself whenever I say “renewables” because instead we should be talking about “clean energy” i.e. wind, solar, hydro, and nuclear, with gas as a transition fuel.
  • Researching strategies for dealing with these challenges is therefore an important area of work. Understanding how the grid is used and making that smarter is going to be an important lever for managing future energy systems.
  • Demand response often comes up in the context of data centers and IT workloads because unlike other large energy users – typically heavy industry – IT workloads have a high degree of temporal and spatial flexibility. At least theoretically.
  • This paper shows that moving workloads between data centers between different regions in the US could enable significant carbon reduction and absorb energy that would otherwise have been curtailed.
  • This is particularly relevant because the paper cites an interesting statistic about the density of data center capacity within small regions of the US: “In the second half of 2018, North Virginia absorbed over a third of the world’s new data center capacity, with an addition of 270 megawatts (MW) of data center power” (source).

However, the paper makes several major assumptions which I see in almost every academic paper on data center demand response or carbon related workload migration:

Assumption: Data centers are underutilized and provisioned for peak capacity, which is a waste. That capacity could be used for workloads migrated from other regions.

Comment: Traditional data centers are notoriously underutilized. In the days of buying hardware for colo it was necessary to overprovision for peak capacity because of the lead times for deploying new equipment. The move to cloud computing means this is less of a problem for the customer because of the flexibility of cloud services, and for the cloud provider because they can allocate resources more efficiently. 

However, redundancy and reliability is a major reason for deploying additional capacity. Data centers should be efficient, but they must be reliable. Being able to fail over when there is a system failure is part of the architecture of well designed applications. This is why you typically see systems utilized to no more than 50% of capacity because you need to be able to switch to the idle infrastructure in an outage.

Pushing past 50% is possible, and the big cloud providers anecdotally say they do this, but running your system at 100% is only sensible if you don’t require redundancy.

Assumption: Workloads are flexible and can be distributed geographically.

Comment: Distributed computing is hard! There’s a reason why most applications deployed to the cloud are only redundant across zones – adding even a small amount of latency introduces difficult computer science problems like storage consistency and database leadership coordination. Multi-zone is generally sufficient (and highly recommended) for most applications. Distributing static assets is straightforward, which is why CDNs are trivial for developers to use (although cache purging is still tricky). As soon as you need to sync state across geographies then it gets more difficult.

In the cloud there is often the assumption that all regions are equally capable, but that is not true. For example, AWS treats each region as (almost) entirely independent from any other. New products capabilities are rolled out to different regions at different times and of course there are pricing differences between regions as well.

Some geographic regions are too small to move between! The paper uses the US as an example, but the US is unusual in terms of its large geographic size yet it is generally considered a “single market” from a legal perspective. That’s not true in Europe where different countries have different data protection laws. AWS has a single region in Germany, for example, so it may not be legally possible to migrate workloads to France or the UK to take advantage of lower carbon intensities.

Lots of innovation is happening in the distributed computing space. Products like Cloudflare’s durable objects, R2 (object storage) and D1 (edge SQL database), and Google Cloud Spanner are trying to solve these issues. 

Assumption: Delay-tolerant workloads like big data analysis, image processing and scientific computation are good candidates for migration.

Comment: The main problem with migrating these workloads is the cost of moving the data. All the cloud providers charge for internal network transfer, which is usually cheaper between zones but expensive between regions. These workloads are the kind that operate on huge volumes of data, which would likely make it prohibitively expensive to move. It would also take a long time.

This means that the workloads that are more fault tolerant and can be operated at higher utilization are also the workloads that are more difficult to move because of the cost and network transfer times.

Assumption: There are “advanced algorithms and automation mechanisms in place to enable the load migration”.

Comment: Outside of the managed database products like Google Cloud Spanner, I’ve yet to see any evidence of these types of automated mechanisms. Migration within zones, such as the ability to migrate VMs for maintenance, is already possible, so perhaps this is a case of extending those capabilities across regions – but then you start to encounter the same issues of latency, data transfer cost, and consistency.

Spot instances which price capacity based on market signals are a good example of introducing incentives to run tasks in particular locations at more optimal times, but they don’t do anything for the migration itself.

As noted above, there is a lot of innovation happening in distributed computing, so I expect this to get better and improve over time.

Conclusions

This is a good paper that makes valid points about the possibilities of migrating flexible IT workloads, however it makes classic assumptions I see in most papers that discuss this topic. The big assumptions are that the data centers are fungible entities where the operator has complete control, the goal is to optimize for efficiency, and it is trivial to move huge volumes of data cheaply. 

We are starting to see this with the big tech companies who own many data centers. Google is an example used in the paper, but it’s telling that it is the only example. Google is only doing it for a small number of workloads. That shows how difficult it is.

This paper is good inspiration for future research direction. I think we’ve seen enough evidence of the benefits of such capabilities that time and effort should be directed towards building the technologies needed to achieve these migrations. And if the cloud providers could reduce their data transfer fees, that would be nice as well.