This paper discusses reducing energy costs of operating a data center. This technique relies on temporal and spatial fluctuations in electricity costs as well as use of dynamic request routing across widely distributed machines. The notion of energy elasticity determines how much money can actually be saved by changing the load on a machine; machines with greater energy proportionality can save more money when they're less used than those machines that continue to burn lots of power when idle.
The authors choose to focus on real-time electricity pricing instead of future, predicted markets, though RT makes up about 10% of the market share. Routing to different geographic areas to obtain the best price relies on low correlation between hourly prices in different regions, which appears to be the case between different Regional Transmission Organizations (RTOs). Part of the difference in cost between two locations at once is the fact that different time zones reach their peak cost at non-peak hours in other locations.
An inherent tradeoff in cutting cost of electricity by routing it elsewhere geographically is an increase in bandwidth costs, which the authors accounted for in part of their study. They did find that this cut the amount of energy costs that could be saved, but they still managed to cut total operating costs. Further reductions could be made through enrolling in demand reduction programs, but that would require delay-insensitive workloads within the data center, and it's not obvious whether that tradeoff would end up being monetarily beneficial.
Regardless of the assumptions the authors must make to simulate their ideas, I think the idea of geographic routing based on hourly electricity costs is a great idea (to the extent that performance doesn't suffer greatly), particularly if the work might be moved around anyway due to node failure or data placement.
No comments:
Post a Comment