DEEP-SaM - Energy-Efficient Provisioning Policies for Computing Environments
GECON '09 Proceedings of the 6th International Workshop on Grid Economics and Business Models
Computer Networks: The International Journal of Computer and Telecommunications Networking
Low-energy automated scheduling of computing resources
Proceedings of the 1st ACM/IEEE workshop on Autonomic computing in economics
Cost-Aware and SLO-Fulfilling Software as a Service
Journal of Grid Computing
Energy-efficient deadline scheduling for heterogeneous systems
Journal of Parallel and Distributed Computing
Whare-map: heterogeneity in "homogeneous" warehouse-scale computers
Proceedings of the 40th Annual International Symposium on Computer Architecture
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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 as soon as it becomes idle,” 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.