2014/04/18 by Huangxin Wang, Wang, Huangxin, Jean X. Zhang +3 · 1 citation
Computer Science · #Cloud Computing and Resource Management #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Performance (cs.PF) #cs.PF
paper · pdf · doi:10.48550/arxiv.1404.4865
openalex publication_date 2014/04/18 · arxiv created 2014/04/21 · arxiv updated 2014/04/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we design an analytically and experimentally better online energy and job scheduling algorithm with the objective of maximizing net profit for a service provider in green data centers. We first study the previously known algorithms and conclude that these online algorithms have provable poor performance against their worst-case scenarios. To guarantee an online algorithm's performance in hindsight, we design a randomized algorithm to schedule energy and jobs in the data centers and prove the algorithm's expected competitive ratio in various settings. Our algorithm is theoretical-sound and it outperforms the previously known algorithms in many settings using both real traces and simulated data. An optimal offline algorithm is also implemented as an empirical benchmark.