Computational Technology Resources - CCP

Keywords: cloud computing, workflow scheduling, optimization, QoS, energy.

Summary

Cloud computing is viewed as a major logical step in the evolution of the internet as a source of remote computing services. With many companies now hosting infrastructure tools and services, more and more businesses are using cloud computing, which helps user applications dynamically provision as many compute resources at specified locations as and when required. These distributed applications come from domains such as high-energy physics, bioinformatics, image processing, etc., which is a complex duty. In order to efficiently schedule the workflow application onto these cloud computing environments, workflow scheduling algorithms are used which may be classified into two types:

Best-effort based scheduling: attempts to minimize the execution time ignoring other factors such as cost for access to resources and levels of satisfaction of users quality of service (QoS); and
QoS based scheduling: attempts to improve performance under QoS constraints, for example, minimizing the time under budget constraints or cost minimization under time constraints.

Several algorithms have already been proposed for the first category, but the second one has been less studied.

In this paper, we investigate the problem of scheduling workflow applications on cloud computing infrastructures. The cloud workflow scheduling is a complex optimization problem which requires considering various scheduling criteria. Traditional research mainly focuses on optimizing the time and cost without paying much attention to energy consumption. We propose a new approach based on a hybrid particle swarm heuristic to optimize the scheduling performance by:

formulating a model for task-resource mapping to minimize the overall energy consumption using the dynamic voltage scaling (DVS) technique; and
designing a heuristic that uses hybrid PSO to solve task resource mapping based on the proposed model.

Our approach is validated by simulating a complex workflow application.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 101 PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, GRID AND CLOUD COMPUTING FOR ENGINEERING Edited by: Paper 29 A Particle Swarm Optimization for Workflow Scheduling based on Energy-Awareness in a Cloud Computing Environment K. Sellami¹, M. Ahmed-Nacer², D. Dris¹ and P.F. Tiako³ ¹Applied Mathematics Laboratory, A/Mira University of Bejaia Route de Targua Ouzemour, Bejaia, Algeria ²LSI Laboratory, USTHB University, El Alia, Bab Ezzouar, Algiers ³Langston University & CITDR, Tiako University, Oklahoma, United States of America doi:10.4203/ccp.101.29 purchase the full-text of this paper Full Bibliographic Reference for this paper K. Sellami, M. Ahmed-Nacer, D. Dris, P.F. Tiako, "A Particle Swarm Optimization for Workflow Scheduling based on Energy-Awareness in a Cloud Computing Environment", in , (Editors), "Proceedings of the Third International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 29, 2013. doi:10.4203/ccp.101.29 Keywords: cloud computing, workflow scheduling, optimization, QoS, energy. Summary Cloud computing is viewed as a major logical step in the evolution of the internet as a source of remote computing services. With many companies now hosting infrastructure tools and services, more and more businesses are using cloud computing, which helps user applications dynamically provision as many compute resources at specified locations as and when required. These distributed applications come from domains such as high-energy physics, bioinformatics, image processing, etc., which is a complex duty. In order to efficiently schedule the workflow application onto these cloud computing environments, workflow scheduling algorithms are used which may be classified into two types: Best-effort based scheduling: attempts to minimize the execution time ignoring other factors such as cost for access to resources and levels of satisfaction of users quality of service (QoS); and QoS based scheduling: attempts to improve performance under QoS constraints, for example, minimizing the time under budget constraints or cost minimization under time constraints. Several algorithms have already been proposed for the first category, but the second one has been less studied. In this paper, we investigate the problem of scheduling workflow applications on cloud computing infrastructures. The cloud workflow scheduling is a complex optimization problem which requires considering various scheduling criteria. Traditional research mainly focuses on optimizing the time and cost without paying much attention to energy consumption. We propose a new approach based on a hybrid particle swarm heuristic to optimize the scheduling performance by: formulating a model for task-resource mapping to minimize the overall energy consumption using the dynamic voltage scaling (DVS) technique; and designing a heuristic that uses hybrid PSO to solve task resource mapping based on the proposed model. Our approach is validated by simulating a complex workflow application. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £40 +P&P)
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