Budget-based resource provisioning and scheduling algorithm for scientific workflows on IaaS cloud

The deployment of cloud computing, specifically Infrastructure as a Service (IaaS) clouds, have become an interested topic in recent years for the execution of compute-intensive scientific workflows. These platforms deliver on-demand connectivity to those infrastructure needed for workflow execution, providing customers to pay only for the service they utilize. As a result schedulers are forced to meet a quid-pro-quo among two main QoS criteria: cost and time. The maximum of this research work has been on making scheduling algorithms with the goal of reducing infrastructure costs as fulfilling a user-specified deadline. Few algorithms, on the other hand, have considered the problem of reducing workflow execution time while staying within a budget. This work consider on the latter scenario. We offer a Budget-based resource Provisioning and Scheduling (BPS) algorithm for scientific workflows used in IaaS service. This proposal was developed to face challenges specifically to clouds like resource performance variation, resource heterogeneity, infinite on-demand connectivity, and pay-as-you-go type (i.e. per-minute pricing). It is efficient of responding to the cloud dynamics, and is powerful in creating suitable solutions that fulfill a user-specified budget and reduce the makespan of the leveraged environment. At last, the experimental events confirms that it runs a workflow efficiently with respect to achieving budget of 94% and minimizing makespan of 29% than the state-of-the-art budget-aware algorithms.

Automated summary

The deployment of IaaS clouds for compute-intensive scientific workflows has become a popular topic in recent years. This study proposes a Budget-based resource Provisioning and Scheduling (BPS) algorithm that can efficiently respond to cloud dynamics and reduce workflow execution time while staying within a specified budget. Experimental results show that the BPS algorithm achieves a budget completion rate of 94% and reduces makespan by 29% compared to state-of-the-art budget-aware algorithms.