Geographic trough filling for internet datacenters
Dan Xu, Xin Liu · 2012
To reduce datacenter energy consumption and cost, current practice has considered demand-proportional resource provisioning schemes, where servers are turned on/off according to the load of requests. Most existing work considers instantaneous (Internet) requests only, which are explicitly or implicitly assumed to be delay-sensitive. On the other hand, in datacenters, there exist a vast amount of delay-tolerant jobs, such as background/maintainance jobs. In this paper, we explicitly differentiate delay-sensitive jobs and delay tolerant jobs. We focus on the problem of using delay-tolerant jobs to fill the extra capacity of datacenters, referred to as trough/valley filling. Giving a higher priority to delay-sensitive jobs, our scheme complements most existing demand-proportional resource provisioning schemes. Our goal is to design an intelligent trough filling mechanism that is energy efficient and also achieves good delay performance. Specifically, we propose a joint dynamic speed scaling and traffic shifting scheme. The scheme does not need statistical information of the system, which is usually difficult to obtain. In the proposed scheme, energy cost saving comes from dynamic speed scaling, statistical multiplexing, electricity price diversity, and service efficiency diversity. In addition, good delay performance is achieved via load shifting and capacity allocation based on queue conditions. We show the efficiency of the proposed scheme by both analysis and simulation.