caa a22 4500 606159118 CHVBK 20210128100623.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00521-014-1804-9 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1804-9 A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing [Elektronische Daten] [Keng-Mao Cho, Pang-Wei Tsai, Chun-Wei Tsai, Chu-Sing Yang] Virtual machine (VM) scheduling with load balancing in cloud computing aims to assign VMs to suitable servers and balance the resource usage among all of the servers. In an infrastructure-as-a-service framework, there will be dynamic input requests, where the system is in charge of creating VMs without considering what types of tasks run on them. Therefore, scheduling that focuses only on fixed task sets or that requires detailed task information is not suitable for this system. This paper combines ant colony optimization and particle swarm optimization to solve the VM scheduling problem, with the result being known as ant colony optimization with particle swarm (ACOPS). ACOPS uses historical information to predict the workload of new input requests to adapt to dynamic environments without additional task information. ACOPS also rejects requests that cannot be satisfied before scheduling to reduce the computing time of the scheduling procedure. Experimental results indicate that the proposed algorithm can keep the load balance in a dynamic environment and outperform other approaches. The Natural Computing Applications Forum, 2014 Scheduling nationallicence Load balance nationallicence Cloud computing nationallicence Ant colony optimization nationallicence Particle swarm optimization nationallicence Cho Keng-Mao Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, 70101, Tainan, Taiwan, ROC aut Tsai Pang-Wei Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, 70101, Tainan, Taiwan, ROC aut Tsai Chun-Wei Department of Computer Science and Information Engineering, National Ilan University, 26047, Yilan, Taiwan, ROC aut Yang Chu-Sing Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, 70101, Tainan, Taiwan, ROC aut Neural Computing and Applications Springer London 26/6(2015-08-01), 1297-1309 0941-0643 26:6<1297 2015 26 521 https://doi.org/10.1007/s00521-014-1804-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1804-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Cho Keng-Mao Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, 70101, Tainan, Taiwan, ROC aut NATIONALLICENCE 700 1- Tsai Pang-Wei Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, 70101, Tainan, Taiwan, ROC aut NATIONALLICENCE 700 1- Tsai Chun-Wei Department of Computer Science and Information Engineering, National Ilan University, 26047, Yilan, Taiwan, ROC aut NATIONALLICENCE 700 1- Yang Chu-Sing Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, 70101, Tainan, Taiwan, ROC aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/6(2015-08-01), 1297-1309 0941-0643 26:6<1297 2015 26 521