Energy-Aware DPSO Algorithm for Workflow Scheduling on Computational Grids

Computational Grids are high performance computing systems used to solve large scale computational demands. Because scheduling workflow applications in a distributed environment is a NP-Complete problem, meta-heuristics are introduced to solve this issue. In this paper, we propose an Energy-Aware algorithm based on Discrete Particle Swarm Optimization (DPSO) called EA-DPSO. Our aim is to minimize the make span (i.e., Overall completion time) of all the tasks within the workflow while optimizing energy consumption. The experimental results show that energy consumption is optimized, while the scheduling in terms of completion time is maintained.

[1]  Albert Y. Zomaya,et al.  CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. (2012) Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/cpe.2839 SPECIAL ISSUE PAPER Energy efficient genetic-based schedulers in comp , 2022 .

[2]  Rajkumar Buyya,et al.  A Taxonomy of Workflow Management Systems for Grid Computing , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[3]  Jean-Marc Pierson,et al.  Multi-objective Scheduling for Heterogeneous Server Systems with Machine Placement , 2014, 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[4]  Thomas F. Wenisch,et al.  PowerNap: eliminating server idle power , 2009, ASPLOS.

[5]  Laurent Lefèvre,et al.  Save Watts in Your Grid: Green Strategies for Energy-Aware Framework in Large Scale Distributed Systems , 2008, 2008 14th IEEE International Conference on Parallel and Distributed Systems.

[6]  Rajkumar Buyya,et al.  Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enabled Clusters , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[7]  Simon,et al.  Resource allocation to conserve energy in distributed computing , 2011, Int. J. Grid Util. Comput..

[8]  Albert Y. Zomaya,et al.  Author manuscript, published in "Journal of Parallel and Distributed Computing (2011)" A Parallel Bi-objective Hybrid Metaheuristic for Energy-aware Scheduling for Cloud Computing Systems , 2011 .

[9]  Lyes Bouali,et al.  A Hybrid Algorithm for DAG Application Scheduling on Computational Grids , 2015, MSPN.

[10]  Andrei Tchernykh,et al.  Adaptive energy efficient scheduling in Peer-to-Peer desktop grids , 2014, Future Gener. Comput. Syst..

[11]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[12]  Albert Y. Zomaya,et al.  Minimizing Energy Consumption for Precedence-Constrained Applications Using Dynamic Voltage Scaling , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[13]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[14]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[15]  Albert Y. Zomaya,et al.  Survey on Grid Resource Allocation Mechanisms , 2014, Journal of Grid Computing.

[16]  Ying Lu,et al.  Efficient Power Management of Heterogeneous Soft Real-Time Clusters , 2008, 2008 Real-Time Systems Symposium.

[17]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[18]  Marek Kisiel-Dorohinicki,et al.  Future Generation Computer Systems ( ) – Future Generation Computer Systems Security, Energy, and Performance-aware Resource Allocation Mechanisms for Computational Grids , 2022 .

[19]  Pascal Bouvry,et al.  Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems , 2013, Journal of Grid Computing.

[20]  Y.-K. Kwok,et al.  Static scheduling algorithms for allocating directed task graphs to multiprocessors , 1999, CSUR.

[21]  Cristian Mateos,et al.  Distributed job scheduling based on Swarm Intelligence: A survey , 2014, Comput. Electr. Eng..

[22]  Ajith Abraham,et al.  A DISCRETE PARTICLE SWARM OPTIMIZATION APPROACH FOR GRID JOB SCHEDULING , 2009 .

[23]  Akshat Verma,et al.  Power-aware dynamic placement of HPC applications , 2008, ICS '08.

[24]  Sanjeev Baskiyar,et al.  Energy aware DAG scheduling on heterogeneous systems , 2010, Cluster Computing.

[25]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[26]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[27]  M. Christobel,et al.  Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids , 2015, TheScientificWorldJournal.