Distributed system seminar talk: Data grouping framework for energy-efficiency in distributed storage systems

My research group and Tevfik's research group meet jointly for a weekly distributed systems. This gives our students a chance to give talks about current project and get feedback for improvement in a friendly setting.

In this week's seminar, Luigi presented his research on building energy-efficient file systems. I was initially skeptical about energy-efficiency as a research topic. Academicians like to work on things that they can quantify and improve, so I was thinking that energy-efficiency in distributed storage was an opportunistic research problem, rather than a real-world problem. Turns out, I couldn't be any more wrong: IT companies spend $10 billions every year on energy consumption (This is 3% of entire expenditure of US!). $3.5 billion of that $10 billion is energy expenditure is due to the storage systems.

Dynamic power management (DPM) is the primary mechanism for energy saving at the storage systems. DPM basically means turn the disk off if you're not using it. An idling disk spends energy because it is still rotating, and this mechanic motion which burns energy. But turning a disc off is not easy. It takes 10s of seconds to stop and start hard disk, and the energy usage spikes at these transition points. This makes the problem into an optimization problem. When is it beneficial to turn the disk off? How can you create gaps long enough to turn off the disk?

The literature discusses the following DPM-enabling techniques for energy-saving in storage systems. Most of these techniques prescribe data access locality improvements.

1) Memory and disk caching: Caching is not only good for providing low-latency but also in some cases good for saving energy. If we can use cache to answer instead of turning on the disk, we can give the disk more time to sleep. But what should be the cache size? If it is too small, data won't fit, this won't provide much/any saving. If it is too large, the cache itself may consume more energy than it saves.

2) Diverting accesses: Data is stored redundantly, so this gives us the opportunity to spin down some redundant disks by diverting the accesses to the already active/hot ones. Unsurprisingly, there is a tradeoff of increased latency in doing so. By limiting concurrency/parallelism you increase latency of replies. (Is energy-efficiency versus latency a fundamental tradeoff in distributed storage?) Maybe, by offering well-drafted SLA agreements to the clients, it is possible to give incentive to the client for trading energy efficiency for slightly increased latency.

3) Popular data clustering: This technique prescribes organizing the disk storage based on the previously observed access locality of data. So if a disk is hot, it is likely to stay hot, and if a disk gets cold, it is likely to stay cold and it can sleep.

I guess there also could be orthogonal techniques if you don't need to serve requests in real-time. For those cases you have the opportunity to batch-schedule accesses.

Luigi is working on a hybrid of these techniques to provide as much energy-efficiency as possible. I wouldn't have thought energy-efficiency for distributed storage could be this interesting. There might even be a couple distributed algorithms problem here that I would enjoy.


Matteo said…
This is interesting. Where can I read something more about your student's work?


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