Cost-effective workflow scheduling approach on cloud under deadline constraint using firefly algorithm
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Cost-effective workflow scheduling approach on cloud under deadline constraint using firefly algorithm Koneti Kalyan Chakravarthi1 · L. Shyamala1 · V. Vaidehi2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Cloud computing, a novel and promising methodology in the distributed computing domain, provides a pay-per-use framework to solve large-scale scientific and business workflow applications. These workflow applications have a constraint that each of them must completed within the limited time (deadline constraint). Therefore, scheduling a workflow with deadline constraints is increasingly becoming a crucial research issue. However, many analytical reviews on scheduling problems reveal that existing solutions fail to provide cost-effective solutions and they do not consider the parameters like CPU performance variation, delay in acquisition and termination of Virtual Machines (VMs). This paper presents a CostEffective Firefly based Algorithm (CEFA) to solve workflow scheduling problems that can occur in an Infrastructure as a Service (IaaS) platform. The proposed CEFA uses a novel method for problem encoding, population initialization and fitness evaluation with an objective to provide cost-effective and optimized workflow execution within the time limit. The performance of the proposed CEFA is compared with the state-of-the-art algorithms such as IaaS Cloud-Partial Critical Path (IC-PCP), Particle Swarm Optimization (PSO), Robustness-Cost-Time (RCT), Robustness-Time-Cost (RTC), and Regressive Whale Optimization (RWO). Our experimental results demonstrate that the proposed CEFA outperforms current state-of-the-art heuristics with the criteria of achieving the deadline constraint and minimizing the cost of execution. Keywords Deadline constraint · Workflow scheduling · Scientific workflows · Firefly
1 Introduction Complex scientific applications like Physics, Bioinformatics, Earth Science, Astronomy and disaster modeling can be represented naturally in the form of workflows [1, 2]. One of the benefits of workflow representation is that the workflow can be reusable, reproducible and even traceable through other workflows [3, 4]. Workflows, depicted as Directed Acyclic Graph (DAG), consists of computational activities interconnected through data-flow and control-flow dependencies [5]. Scientific workflows are partitioned into multiple tasks which require complex data of different sizes and tens of hundreds of processing hours [6]. In the meantime, computing systems where catastrophe can occur will Koneti Kalyan Chakravarthi
[email protected] 1
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
2
Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India
become useless, if the completion of workflow execution takes more than some specified time. These workflow-based applications are highly demanding and challenging for processing huge amounts of data in real-time workflow tasks with the desired cost reduction o
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