An Optimized VM Migration to Improve the Hybrid Scheduling in Cloud Computing

Abstract

Cloud computing is a decentralized platform that efficiently allows user applications to utilize various resources. However, it faces challenges in task scheduling (TS) and load balancing (LB). To address these issues, several metaheuristic algorithms have been developed. One such algorithm is a hybrid TS algorithm that uses long short-term memory (LSTM) to determine task runtime reliability, tuna swarm optimization (TSO) to schedule optimal tasks with high expected runtime, and the VIKOR technique to backfill remaining tasks. Despite these efforts, the TS among nodes is unbalanced, leading to overloaded or under-loaded Physical Machines (PMs) and high energy consumption. To tackle these problems, this article proposes a hybrid TSLB algorithm to achieve balanced energy utilization and load fairness among PMs in heterogeneous cloud networks. The TSO algorithm is used to select optimal tasks and virtual machines (VMs) for migration to suitable PMs. The selection of optimal tasks is based on the fitness function of all VMs, while the selection of optimal VMs for migration is determined by the fitness function of all PMs in the network. This approach ensures the best mapping correlation between selected tasks and VMs, resulting in effective load distribution. Simulation results show that the LSTM-TSLBTSO-VIKOR algorithm achieves a makespan of 2700 seconds, a mean resource utilization ratio (RUR) of 0.97, a degree of imbalance (DoI) of 0.03, a throughput of 0.86 tasks/sec, memory usage of 26.9MB, a bandwidth of 187MBps, energy consumption of 95KWh, one VM migration, and a load fairness of 1.23 for 1000 tasks. These results outperform the LSTM-TSTSO-VIKOR, CMODLB, APDPSO, LBPSGORA, and GWOLB algorithms.