Multi objective task scheduling algorithm based on SLA and processing time suitable for cloud environment

摘要

Cloud computing is a new paradigm which provides subscription-oriented services. Scheduling the tasks in cloud computing environments is a multi-goal optimization problem, which is NP hard. To exaggerate task scheduling performance and reduce the overall Makespan of the task allocation in clouds, this paper proposes two scheduling algorithms named as TBTS (Threshold based Task scheduling algorithm) and SLA-LB (Service level agreement-based Load Balancing) algorithm. TBTS is two-phase scheduling algorithm which schedules the tasks in a batch. It supports task scheduling in virtual machines with distinct configuration. Furthermore, in TBTS algorithm, threshold data generated based on the ETC (Expected Time to Complete) matrix. Virtual machines which execute tasks with the estimated execution time lesser than threshold value are allocated to the particular task. SLA-LB algorithm is a online model which schedules the task dynamically, based on the requirement of clients, like deadline and budget as the two criteria. Prediction based scheduling is implemented in TBTS to increase the system utilization and to improve the load balancing among the machines by allocation of the minimum configuration machine to the task, based on predicted robust threshold value. SLA-LB uses the level of agreement and finds the required system to reduce the Makespan and increase the cloud-utilization. Simulation of proposed algorithms is performed with benchmark datasets (Braun, 2015) and synthetic datasets are generated with random functions. The proposed TBTS and SLA-LB final values of the proposed algorithms are analyzed with assorted scheduling models, namely SLA-MCT, FCFS, EXIST, LBMM, Lbmaxmin, MINMIN and MAXMIN algorithms. Performance metrics such as Makespan, penalty, gain cost and also the VM utilization factor of proposed algorithm compared with existing algorithms. The comparison analysis among various existing algorithms with TBTS and SLA-LB algorithms show that the proposed methods outperform existing algorithms, even in the scalability situation of the dataset and virtual machines.