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Cost-aware Scheduling for Heterogeneous Enterprise Machines (CASH'EM)
Burge, Jennifer; Ranganathan, Partha; Wiener, Janet L.
Keyword(s): power scheduling; data centers; autonomic computing
Abstract: Data centers contain heterogeneous sets of machines. Some machines are faster and some - often the same ones - consume more energy and cost more to operate. The data center coordinator must decide how to allocate these machines to multiple applications of potentially many customers, each of which has different requirements. Given a stream of customer requests for machines, how does the data center provider decide which machines to give to whom and when? We propose new algorithms for a cost-aware provider to maximize its profit as it makes admission and scheduling decisions for the customer requests. We show that it matters which machines are assigned to each customer, especially when the data center is undersaturated. (Most data centers are.) Our new algorithms do best when they try to anticipate the “riskiness” of their decisions, that is, the likelihood that even higher-value requests will arrive later. We also show that turning unused machines off, rather than leaving them idle, even using simple heuristics like “turn off a machine that has been idle for ten minutes,” can save a lot of money. Finally, we show that having heterogeneity in the data center is, in fact, beneficial. We demonstrate that the same set of customers can be satisfied at a lower cost and a higher profit in a heterogeneous data center rather than in a data center comprised solely of the newest, fastest, machines.
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