GRP-HEFT: A Budget-Constrained Resource Provisioning Scheme for Workflow Scheduling in IaaS Clouds

In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budge...

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Vydané v:IEEE transactions on parallel and distributed systems Ročník 31; číslo 6; s. 1239 - 1254
Hlavní autori: Faragardi, Hamid Reza, Saleh Sedghpour, Mohammad Reza, Fazliahmadi, Saber, Fahringer, Thomas, Rasouli, Nayereh
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Ant colony optimization
budget-constrained scheduling
Budgets
Cloud computing
Constraint modelling
Genetic algorithms
Greedy algorithms
Multiple objective analysis
Particle swarm optimization
Production scheduling
Provisioning
Resource allocation
resource provisioning
Schedules
Scheduling
Scheduling algorithms
Task analysis
workflow scheduling
Workflow software
ISSN:1045-9219, 1558-2183
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Shrnutí:In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (MultiObjective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2019.2961098