Hsu, Y.-C.Y.-C.HsuLiu, P.P.LiuPANGFENG LIU2020-05-042020-05-042011https://scholars.lib.ntu.edu.tw/handle/123456789/489252This paper describes the important issue of energy conservation for data centers. We consider the problem of provisioning physical servers to a sequence of jobs, and reducing the total energy consumption. The performance metric is the wasted energy - the over-provisioned computing power provided by the physical servers, but exceeding the requirement of the jobs. We propose three new strategies for allocating servers to a sequence of jobs - a largest machine first heuristic, a best fit method, and a mixed method. We prove that both the largest machine first heuristic and the mixed method will only incur at most 2 in over-provisioned energy. That is, the ratio between the over-provisioned energy and the total provisioned energy is bounded by 2/n(1 + δ), where n is the number of jobs, and 1 + δ is the ratio between the maximum and minimum execution time of jobs. We also derive a tight bound of i on the ratio of wasted energy if the ratio δ could be arbitrarily large. We also conduct experiments to compare the three algorithms in practice. The experiment results indicate that all three algorithms waste very little energy in over-provision. The mixed method outperforms the best fit method, which outperforms the largest machine first method. © 2011 IEEE.[SDGs]SDG7Best fit; Computing power; Data centers; Execution time; Job sequences; Mixed method; New strategy; Performance metrices; Tight bound; Total energy consumption; Algorithms; Cloud computing; Energy conservation; Energy conversion; Energy utilization; Experiments; Scheduling; Heuristic methodsconference paper10.1109/CSC.2011.6138524https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863166670&doi=10.1109%2fCSC.2011.6138524&partnerID=40&md5=d84f9c821c71895e94210b80f3faf715