ABSTRACT Cloud computing is becoming an increasingly admired paradigm that delivers highperformance computing resources over the Internet to solve the large-scale scientific problems, but still it has various challenges that need to be addressed to execute scientific workflows. The existing research mainly focused on minimizing finishing time (makespan) or minimization of cost while meeting the quality of service requirements. However, most of them do not consider essential characteristic of cloud and major issues, such as virtual machines (VMs) performance variation and acquisition delay. In this paper, we propose a meta-heuristic cost effective genetic algorithm that minimizes the execution cost of the workflow while meeting the deadline in cloud computing environment. We develop novel schemes for encoding, population initialization, crossover, and mutations operators of genetic algorithm. Our proposal considers all the essential characteristics of the cloud as well as VM performance variation and acquisition delay. Performance evaluation on some well-known scientific workflows, such as Montage, LIGO, CyberShake, and Epigenomics of different size exhibits that our proposed algorithm performs better than the current state-of-the-art algorithms. INDEX TERMS Cloud computing, scientific workflows, resource provisioning, scheduling, quality of service (QoS).
In 2016, J. meena et al proposed a meta -heuristic cost effective genetic algorithm (CEGA) REF that reduces the execution cost of the workflow while meeting the deadline in cloud computing.
Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint
IEEE Access
IEEE Access
Computer Science