Resource Management in Sustainable Cyber-Physical Systems Using Heterogeneous Cloud Computing

• Published in: IEEE transactions on sustainable computing Vol. 3; no. 2; pp. 60 - 72
• Main Authors: Gai, Keke, Qiu, Meikang, Zhao, Hui, Sun, Xiaotong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Capacity planning; Cloud computing; Computational modeling; Computer networks; Cyber-physical systems; Distributed processing; dynamic programming; Heterogeneous cloud computing; Mapping; Optimization; Prediction algorithms; Resource management; Servers; smart switch; Sustainability; task assignment; Workload; Workloads
• ISSN: 2377-3782; 2377-3790
• DOI: 10.1109/TSUSC.2017.2723954

The substantial growth of the distributed computing using heterogeneous computing has enabled great expansions in Cyber Physical Systems (CPS). Combining CPS with heterogeneous cloud computing is an alternative approach for increasing sustainability of the system. However, execution of resource management in cloud systems is still encountering a few challenges, including the bottlenecks of the Web server capacities and task assignments in the heterogeneous cloud. The unstable service demands often result in service delays, which embarrasses the competitiveness of the enterprises. This paper addresses the problem of the task assignment in heterogeneous clouds, which is proved as a NP-hard problem. The proposed approach is called Smart Cloud-based Optimizing Workload (SCOW) Model that uses predictive cloud capacities and considers sustainable factors to assign tasks to heterogeneous clouds. To reach the optimization objective, we propose a few algorithms, which include Workload Resource Minimization Algorithm (WRM), Smart Task Assignment (STA) Algorithm , and Task Mapping Algorithm (TMA). Our experimental evaluations have examined the performance of the proposed scheme.