Implementasi Algoritma Improvised Prioritized Deadline Scheduling Algorithm (IPDSA) pada Grid Environment Menggunakan PVM3

  • Haidar Hendri Setyawan Universitas Sebelas Maret
  • Wisnu Widiarto Universitas Sebelas Maret
  • Ardhi Wijayanto Universitas Sebelas Maret
DOI: https://doi.org/10.29207/resti.v4i5.2457
Keywords: Grid Computing, Resource Scheduling, Two-level Hierarchy Scheduling Model, PVM3, IPDSA

Abstract

Resource Scheduling is one of the most challenging parts of grid computing. A number of algorithms have been designed and developed to create effective resource scheduling. In this research, the algorithms that have been used are the improvised prioritized deadline scheduling algorithm (IPDSA), and the parallel virtual machine version 3 (PVM3) has been used for efficient task execution, with a deadline limit for each task. PVM3 is a software library that optimizes resources flexibly and heterogeneously on a computer. These resources have been connected to various architectures in parallel, so that they can complete tasks well, even though they are very large and complex. This research has implemented the IPDSA resource scheduling algorithm to optimize scheduling and Grid resources in a computer laboratory as a grid environment, where the computers (hosts) are the Grid resource. This research has also developed an IPDSA resource scheduling algorithm by giving priority to each task and implemented using PVM3. The IPDSA resource scheduling algorithm has been successfully implemented using PVM3, with average Tardiness showing a stable value and getting a Non-Delayed Task value above 97.3%, because the resources and tasks that are carried out can be distributed evenly according to the number of hosts used.

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References

Foster, Kesselman, C. and Tuecke, S., 2001, The anatomy of the grid: Enabling scalable virtual organizations, Int. J. High Perform. Comput. Appl., vol. 15, no. 3, pp. 200–222.

Goswami, S. and De Sarkar, A., 2013, A comparative study of load balancing algorithms in computational grid environment, in 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, pp. 99–104.

Matani, A., Naji, H.R. and Motallebi, H., 2020, A Fault-Tolerant Workflow Scheduling Algorithm for Grid with Near-Optimal Redundancy, Journal of Grid Computing, volume 18, pp. 377–394

Patel, D. K., Tripathy, D. and Tripathy, C. R., 2016, Survey of load balancing techniques for grid, J. Netw. Comput. Appl., vol. 65, pp. 103–119

Balasangameshwara, J. and Raju, N., 2012, Performance-driven load balancing with a primary-backup approach for computational grids with low communication cost and replication cost, IEEE Trans. Comput., vol. 62, no. 5, pp. 990–1003

Midya, S., Roy, A., Majumder, K. and Phadikar, S., 2018, Multi-objective optimization technique for resource allocation and task scheduling in vehicular cloud architecture: A hybrid adaptive nature inspired approach, Journal of Network and Computer Applications, Volume 103, 1 February 2018, pp. 58-84, https://doi.org/10.1016/j.jnca.2017.11.016

Topcuoglu, H., Hariri, S., and Wu, M., 2002, Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions Parallel Distribution Systems 13(3), pp. 260–274

Kumar, M., Sharma, S. C., Goel, A. and Singh, S. P., 2019, A comprehensive survey for scheduling techniques in cloud computing, J. Netw. Comput. Appl..

Hao, Y., Liu, G. and Wen, N., 2012, An enhanced load balancing mechanism based on deadline control on GridSim, Future Gener. Comput. Syst., vol. 28, no. 4, pp. 657–665

Xu, Z., Hou, X. and Sun, J., 2003, Ant algorithm-based task scheduling in grid computing, in CCECE 2003-Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No. 03CH37436), vol. 2, pp. 1107–1110.

Chauhan, A., Singh, S., Negi, S. and Verma, S. K., 2016, Algorithm for deadline based task scheduling in heterogeneous grid environment, in 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 219–222.

Geist, A., Beguelin, A., Dongarra, J., Jiang, W., Manchek, R. and Sunderam, V. S., 1994, PVM: Parallel Virtual Machine :a Users’ Guide and Tutorial for Networked Parallel Computing. MIT Press.

Nanthiya, D. and Keerthika, P., 2013, Load balancing GridSim architecture with fault tolerance, in 2013 International Conference on Information Communication and Embedded Systems (ICICES), pp. 425–428

Sampath S., Nanjesh B. R., Sagar, B. B. and Subbaraya, C. K., 2014, Performance optimization of PVM based parallel applications using optimal number of slaves, in 2014 International Conference on Reliability Optimization and Information Technology (ICROIT), Feb. 2014, pp. 388–392, doi: 10.1109/ICROIT.2014.6798360.

Published
2020-10-30
How to Cite
Haidar Hendri Setyawan, Widiarto, W., & Wijayanto, A. (2020). Implementasi Algoritma Improvised Prioritized Deadline Scheduling Algorithm (IPDSA) pada Grid Environment Menggunakan PVM3. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(5), 957-963. https://doi.org/10.29207/resti.v4i5.2457
Issue
Vol 4 No 5 (2020): Oktober 2020
Section
Information Technology Articles

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