Budget aware scheduling algorithm for workflow applications in IaaS clouds
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Budget aware scheduling algorithm for workflow applications in IaaS clouds K. Kalyan Chakravarthi1 • L. Shyamala1 • V. Vaidehi2 Received: 25 November 2019 / Revised: 16 February 2020 / Accepted: 9 March 2020 Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Cloud computing, a novel and promising model of Service-oriented computing, provides a pay-per-use framework to solve large-scale scientific and business workflow applications. Workflow scheduling in cloud is challenging due to dynamic nature of the cloud, particularly, on demand provisioning, elasticity, heterogeneous resource types, static & dynamic pricing models and virtualization. An example of workflow scheduling is mapping workflow tasks to cloud computing resources. Additionally, these workflow applications have a runtime constraint—the most typical being the cost of the computation and the time that computation requires to complete. Therefore, the focus is on two criteria: makespan and cost. This paper presents an algorithm called NBWS (Normalization based Budget constraint Workflow Scheduling) which generates a workflow schedule which minimizes the schedule length while satisfying the budget constraint. The algorithm undergoes a process of min–max normalization tailed by computing expect reasonable budget ðerbÞ for dispatching the workflow tasks into one of the virtual machines. To minimize the execution time, NBWS algorithm maps the workflow tasks to resources which are having the earliest finish time within the allocated budget. The experimental results demonstrate that NBWS outperforms current state-of-the-art heuristics with respect to budget constraint and minimizing the makespan. Keywords Scheduling Scientific workflows Budget-constraint IaaS Quality of service DAG
1 Introduction Distributed computing [1–4] provides a good platform for various workflow applications, for big data analysis in insurance [5], demand estimation of water resources [6], age invariant face recognition [7], Efficient business process consolidation [8], and video analytics [9]. In this big data era, workflows require large numbers of networked, high-performance, distributed resources to process and retrieve information from the ever-increasing data [10]. Although most of the workflow applications in the past have utilized dedicated HPCs (High Performance Computing platforms), there are many compelling reasons for & K. Kalyan Chakravarthi [email protected] 1
School of Computer Science and Engineering, VIT, Chennai, India
2
Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India
continual migration of scientific computing into commercial clouds: the elasticity offers a variety of cost-effective computing platforms [11–14], the on demand model is more suitable for the sporadic computing requirements of researchers [15], and finally, instead of purchasing and maintaining the dedicated High Performance Computing equipment, cloud provides computing as a utility-based IT resour
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