Abstract
Objectives: The objective of this model is to design a technique to schedule the workload for the computation of web services in the heterogeneous cloud environment. As the Web Service Composition (WSC) for the execution of scientific workload in a heterogeneous cloud computing environment is a challenging task. Modern workload requires dynamic resource provisioning technique as there exist parallelization among sub-tasks and different task demands different Quality of Service (QoS) requirement. Methods: This study presents an Evolutionary Computing based Web Service Composition (EC-WSC) technique to execute a large-scale scientific workload in a heterogeneous cloud environment. A multi-objective metric for improving energy efficiency and resource utilization is modelled. Then, an improved searching mechanism for the dragonfly evolutionary computing algorithm is modelled. Findings: Experiment outcomes show EC-WSC model attains superior performance in execution time performance analysis and energy efficiency performance analysis when compared with existing resource provisioning models of workload service composition such as Deadline and Budget-Aware Workflow Scheduling (DBAWS)(1), Evolutionary Computing Multi-objective optimization for Hybrid Clouds (EC-MOH)(2), Web Service Composition (WSC)(3), and Evolutionary Multi-Objective Optimization for clouds (EMOC)(4) in terms of heterogeneous computing, workload size, multi-objective optimization, QoS metric, and optimization strategy. Our model EC-WSC has proved to be more efficient in terms of energy efficiency by a reduction of 52.13% and also reduction in execution time by 71% when compared with the WSC(3) existing Web Service Composition model. Novelty: Existing resource provisioning predominantly focused on reducing computation cost and time; however, induces task execution latency and energy overhead. However, EC-WSC is modelled to utilize resources more efficiently and meet task QoS requirements by assuring energy minimization constraints. Keywords: Cloud Computing; Heterogeneous Computing Environment; Multiobjective optimization problem; Resource Provisioning; Workload Scheduling
Highlights
Cloud computing environment offers virtual computing nodes for executing workflow tasks submitted by the user
Experiment is carryout to evaluate the performance of the evolutionary computing-based Web Service Composition Technique and existing web service composition technique(2,3) for executing data-intensive workload on the cloud computing environment
Energy efficiency and execution time are metrics used for validating the performance of different web service composition models
Summary
Cloud computing environment offers virtual computing nodes (i.e., instance) for executing workflow tasks submitted by the user. The user can use several cloud instances in a scalable manner for executing tasks. These instances/services are given to users based on predefined SLA’s. The users are charged based on QoS and SLA defined Considering these benefits, gridbased workflow providers such as Pegasus and ASKALON have started supporting cloud-based workflow execution. It is a challenging task for finding suitable resources (i.e., web service composition mechanism) for executing workload in a multiprocessing framework. The standard web service composition technique hypothesis is that users submit the workload with constraints to the cloud platform. Existing web composition technique for executing workload focused just for minimizing energy; leads to higher service violation
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