Multi-Objective Optimization of Deadline and Budget-Aware Workflow Scheduling in Uncertain Clouds

Abstract

Cloud technologies are being used nowadays to cope with the increased computing and storage requirements of services and applications. Nevertheless, decisions about resources to be provisioned and the corresponding scheduling plans are far from being easily made especially because of the variability and uncertainty affecting workload demands as well as technological infrastructure performance. In this paper we address these issues by formulating a multi-objective constrained optimization problem aimed at identifying the optimal scheduling plans for scientific workflows to be deployed in uncertain cloud environments. In particular, we focus on minimizing the expected workflow execution time and monetary cost under probabilistic constraints on deadline and budget. According to the proposed approach, this problem is solved offline, that is, prior to workflow execution, with the intention of allowing cloud users to choose the plan of the Pareto optimal set satisfying their requirements and preferences. The analysis of the combined effects of cloud uncertainty and probabilistic constraints has shown that the solutions of the optimization problem are strongly affected by uncertainty. Hence, to properly provision cloud resources, it is compelling to precisely quantify uncertainty and take explicitly into account its effects in the decision process.

Evolutionary Computing based Web Service Composition Technique for Scheduling of Workload under Cloud Environment

“…Experiment outcomes show that the EC-WCS is very efficient in reducing the energy consumption by 23% when compared with the exiting method EC-MOH-WSC and also meets the QoS requirement of workload application. Experiment outcomes also show EC-WSC model attains superior performance in execution time performance analysis and energy efficiency performance analysis https://www.indjst.org/ (2) , 2019 WSC (3) , 2020 DBAWS (1) , 2021 EMOC (4) when compared with existing resource provisioning models of workload service composition such as EC-MOH (2) , WSC (3) , DBAWS (1) , and 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 a reduction in execution time by 71% when compared with the WSC (3) existing Web Service Composition model.…”

“…Thus, proves that the new improved evolution computing algorithm of the dragonfly algorithm can search better (i.e., optimal) resources in executing the workload. As a result, achieves much better performance than the recently modeled WSC technique (1)(2)(3)(4) . https://www.indjst.org/ (2) , (16)…”

“…The Table 1 describes the comparatives analysis of EC-WSC concerning other WSC models such as EC-MOH (2) , WSC (3) , DBAWS (1) , and EMOC (4) . The following work has been considered for comparative analysis because of the multi-objective optimization strategy adopted.…”

“…Recently, extensive work has been completed on the workload resource provisioning issue in homogeneous processing conditions. In (1) because of the workload issues and resource provision in cloud computing and for the storage requirements, an optimized method using the multi-objective has been proposed. This method mainly focuses to reduce the cost and time required for the execution of the scientific cloud problems.…”

“…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.…”