Abstract
With the increase in deployment of scientific workflow applications on an IaaS cloud computing environment, the distribution of workflow tasks to particular cloud instances to decrease runtime and cost has emerged as an important challenge. The cloud workflow scheduling is a well-known NP-hard problem. In this paper, we propose a new approach for multi-objective workflow scheduling in IaaS clouds offering a limited amount of instances and a flexible combination of instance types, and present a hybrid algorithm combining genetic algorithm, artificial bee colony optimization and decoding heuristic for scheduling workflow tasks over the available cloud resources while trying to optimize the workflow makespan and cost simultaneously. The proposed algorithm is evaluated for real-world scientific applications by a simulation process. The simulation results show that our proposed scheduling algorithm performs better than the current state-of-the-art algorithms. We validate the results by the Wilcoxon signed-rank test.
Highlights
Scientific workflow is a widely-used model to describe various scientific computing problems in areas such as bioinformatics, astronomy, and physics
We extend HGAABC for coping with commercial Infrastructure as a Service (IaaS) cloud computing systems providing a limited amount of instances and a flexible combination of instance types
A scientific workflow application is modeled as a directed acyclic graph (DAG), defined by a two-tuple W(T, E), where T is the set of vertices standing for n different tasks of the workflow, and E is the set of directed edges between the vertices standing for dependencies
Summary
Scientific workflow is a widely-used model to describe various scientific computing problems in areas such as bioinformatics, astronomy, and physics. With the emergence of cloud computing, there are many new challenges that must be addressed in order to efficiently schedule the large scale scientific workflow application in cloud environment. Many meta-heuristic algorithms, such as genetic algorithm (GA) [1] particle swarm optimization (PSO) [2], and artificial bee colony algorithm (ABC) [3] were proposed to solve the scheduling problem of the workflow tasks in cloud environments. Y. Gao et al.: Hybrid Algorithm for Multi-Objective Scientific Workflow Scheduling in IaaS Cloud exploration ability. To overcome the disadvantages of the two algorithms, this paper proposes a hybrid approach based on GA and ABC for scheduling scientific workflows in IaaS clouds with pay-per-use pricing model. The performance of the proposed algorithm is evaluated against other algorithms to prove its effectiveness in solving the workflow scheduling problem in IaaS clouds.
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