Job Scheduling Algorithms on Grid Computing: State-of- the Art

Yousif, Adil; Nor, Sulaiman Mohd; Abdualla, Abdul Hanan; Bashir, Mohammed; University-Sudan, Shendi

doi:10.14257/ijgdc.2015.8.6.13

Cited by 11 publications

(4 citation statements)

References 65 publications

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“…As an example of independent task scheduling algorithms: Opportunistic Load Balancing (OLB) algorithm [5,6], Minimum Execution Time (MET) algorithm [7], Minimum Completion Time (MCT) algorithm [8], Min-min algorithm [9], Max-min algorithm [10,11,12], Suffrage algorithm [13,14]. 98 | P a g e www.ijacsa.thesai.org Dependent task scheduling algorithms use the Directed Acyclic Graph (DAG) to represent dependency between tasks.…”

Section: Related Workmentioning

confidence: 99%

WorkStealing Algorithm for Load Balancing in Grid Computing

Hossam¹,

Abdel-Galil²,

Belal³

2021

IJACSA

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Grid computing is a computer network in which many resources and services are shared for performing a specific task. The term grid appeared in the mid-1990s and due to the computational capabilities, efficiency and scalability provided by the shared resources, it is used nowadays in many areas, including business, e-libraries, e-learning, military applications, medicine, physics, and genetics. In this paper, we propose WorkStealing-Grid Cost Dependency Matrix (WS-GCDM) which schedule DAG tasks according to their data transfer cost, dependency between tasks and load of the available resources. WS-GCDM algorithm is an enhanced version from GCDM algorithm. WS-GCDM algorithm balances load between all the available resources in grid system unlike GCDM which uses specific number of resources regardless how many resources are available. WS-GCDM introduces better makespan than GCDM algorithm and enhances system performance from 13% up to 17% when we experiment algorithms using DAG with dependent tasks.

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Section: Related Workmentioning

confidence: 99%

WorkStealing Algorithm for Load Balancing in Grid Computing

Hossam¹,

Abdel-Galil²,

Belal³

2021

IJACSA

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“…They have largely taken a narrow path that either focus on specific topics or area of applications of scheduling algorithms. These studies ( Almansour and Allah, 2019 ; Arora and Banyal, 2022 ; Arunarani et al., 2019 ; Ghafari et al., 2022 ; Kumar et al., 2019 ; Sana and Li, 2021 ) focused on the extensive review of scheduling techniques in cloud computing ( Agrawal et al., 2021 ; Davis and Burns, 2011 ; Olofintuyi et al., 2020 ), focused on the review of operating system scheduling algorithms while other studies such as ( Maipan-Uku et al., 2017 ; Prajapati and Shah, 2014 ; Yousif et al., 2015 ) focused on scheduling in grid computing. A general overview of scheduling algorithms is necessary at this time to understand the trends over time and to discover a new research focus.…”

Section: Introductionmentioning

confidence: 99%

Examining the developments in scheduling algorithms research: A bibliometric approach

Omotehinwa¹

2022

Heliyon

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“…Because of the increased complexity in the scheduling part of the grid, it matches the class of NP-complete problems. The algorithms stated in [3] can be applied to scheduling jobs in grid environment are of different types, and so the grid platforms. So, it is not possible to apply a single algorithm for all the grid platforms.…”

Section: Introductionmentioning

confidence: 99%

An Automated Parameter Tuning Method for Ant Colony Optimization for Scheduling Jobs in Grid Environment

Ankita¹,

Sahana²

2019

IJISA

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The grid infrastructure has evolved as the integration and collaboration of multiple computer systems, networks, different databases and other network resources. The problem of scheduling in grid environment is an NP complete problem where conventional approaches like First Come First Serve (FCFS), Shortest Job First (SJF), Round Robin Scheduling algorithm (RR), Backfilling is not preferred because of the unexpectedly high computational cost and time in the worst case. Different algorithms, for example bio-inspired algorithms like Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Genetic Algorithm and Particle Swarm Optimization (PSO) are there which can be applied for solving NP complete problems. Among these algorithms, ACO is designed specifically to solve minimum cost problems and so it can be easily applied in grid environment to calculate the execution time of different jobs. Algorithms have different parameters and the performance of these algorithms extremely depends on the values of its parameters. In this paper, we have proposed a method to tune the parameters of ACO and discussed how parameter tuning affects the performance of ACO which in turn affects the performance of grid environment when applied for scheduling.

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Job Scheduling Algorithms on Grid Computing: State-of- the Art

Abstract: Scheduling jobs on computational grids is identified as NP-complete problem

Cited by 11 publications

References 65 publications

WorkStealing Algorithm for Load Balancing in Grid Computing

WorkStealing Algorithm for Load Balancing in Grid Computing

Examining the developments in scheduling algorithms research: A bibliometric approach

An Automated Parameter Tuning Method for Ant Colony Optimization for Scheduling Jobs in Grid Environment

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