Abstract
Recent developments in cloud computing have made it a powerful solution for executing large-scale scientific problems. The complexity of scientific workflows demands efficient utilization of cloud resources to satisfy user requirements. Scheduling of scientific workflows in a cloud environment is a challenge for researchers. The problem is considered as NP-hard. Some constraints such as a heterogeneous environment, dependencies between tasks, quality of service and user deadlines make it difficult for the scheduler to fully utilize available resources. The problem has been extensively studied in the literature. Different researchers have targeted different parameters. This article presents a multi-objective scheduling algorithm for scheduling scientific workflows in cloud computing. The solution is based on genetic algorithm that targets makespan, monetary cost, and load balance. The proposed algorithm first finds the best solution for each parameter. Based on these solutions, the algorithm finds the superbest solution for all parameters. The proposed algorithm is evaluated with benchmark datasets and comparative results with the standard genetic algorithm, particle swarm optimization, and specialized scheduler are presented. The results show that the proposed algorithm achieves an improvement in makespan and reduces the cost with a well load balanced system.
Highlights
Cloud computing is the new paradigm of computing in which, instead of local storage, data are stored and accessed over the Internet
The proposed algorithm first finds the best solution for each parameter, that is, makespan, cost, and load balance
The proposed algorithm is based on three parameters, that is, makespan, monetary cost, and load balance
Summary
Cloud computing is the new paradigm of computing in which, instead of local storage, data are stored and accessed over the Internet. Cloud resources such as storage, processing, and network services are provided on demand to the cloud users.[1] Instead of developing in-house infrastructure, service providers provide services to the users by pay per service and negotiation between cloud users and service providers.[2] Cloud resources are virtualized, which enables both the customization of the platform and the isolation of application from the underlying hardware.[3,4] These resources are accessed from anywhere and applications can be deployed as per user needs.[5] Cloud may be public, private, or hybrid.
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