This paper considers a cost minimization problem for data centers with N servers and randomly arriving service requests. A central router decides which server to use for each new request. Each server has three types of states (active, idle, setup) with different costs and time durations. The servers operate asynchronously over their own states and can choose one of multiple sleep modes when idle. We develop an online distributed control algorithm so that each server makes its own decisions  and the overall time average cost is near optimal with probability 1. The algorithm does not need probability information for the arrival rate or job sizes. Next, an improved algorithm that uses a single queue is developed via a "virtualization" technique . The improvement is shown to provide the same (near optimal) costs while significantly reducing delay, as shown in simulations .
This paper considers a cost minimization problem for data centers with N servers and randomly arriving service requests. A central router decides which server to use for each new request. Each server has three types of states (active, idle, setup) with different costs and time durations. The servers operate asynchronously over their own states and can choose one of multiple sleep modes when idle. We develop an online distributed control algorithm so that each server makes its own decisions , the request queues are bounded and the overall time average cost is near optimal with probability 1. The algorithm does not need probability information for the arrival rate or job sizes. Next, an improved algorithm that uses a single queue is developed via a "virtualization" technique which is shown to provide the same (near optimal) costs . Simulation experiments on a real data center traffic trace demonstrate the efficiency of our algorithm compared to other existing algorithms .
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, the request queues are bounded
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. The improvement
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while significantly reducing delay, as shown in simulations
. Simulation experiments on a real data center traffic trace demonstrate the efficiency of our algorithm compared to other existing algorithms