A Computational Grid Scheduling Model to Maximize Reliability Using Modified GA

Raza, Zahid; Vidyarthi, Deo Prakash

doi:10.4018/978-1-4666-2065-0.ch002

Cited by 2 publications

(5 citation statements)

References 22 publications

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“…The EGA is a computational technique for simulating evolution processes and natural selection in organisms. 10,19 It involves steps such as generation of the initial population, evaluation, selection, crossover, mutation, and regeneration. The initial population is significant because it symbolizes the solution and is typically produced at random.…”

Section: Egamentioning

confidence: 99%

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Efficient hybrid enhanced genetic algorithm and ant colony system model for rerouting multimedia message in multiple node–link failures within wireless network

Owoade

Ogundile

Babalola

et al. 2023

Int J Communication

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Summary Failure‐resilient wireless networks have attracted the interest of the research community and have been an open area of concern in the studies of wireless network in recent years. Accordingly, different research on restoration techniques have been carried out to proffer solution to the network component failures. In the same vein, this article proposes a hybridized enhanced genetic algorithm and ant colony system (EGAACS) survivability model, which can instantly resolve node–link failure problems, thus improving the quality of service of the wireless network. The EGAACS is a hybrid model that combines the principles of the enhanced genetic algorithm (EGA) and ant colony system (ACS) models to form a capacity efficiency solution that outperforms the ACS and EGA models in terms of path cost and transmission delay. The resilience of this proposed EGAACS model is verified for different wireless network sizes (20, 30, 40, and 50 node networks). Simulation results show that the proposed EGAACS model generates the best close to optimal paths in terms of the path cost and transmission delay in comparison to the EGA and ACS models. In fact, the performance of the proposed EGAACS model is more conspicuous as the size of the network increases. More importantly, the proposed EGAACS model is suitable for real‐time wireless network as it exhibits moderate computational time complexity.

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Section: Egamentioning

confidence: 99%

“…[4][5][6][7] These proposed methods differ in network formulations and requirements. Examples of these models are swarm intelligence based on ant colony system (ACS), 8,9 enhanced genetic algorithm (EGA), 10 ant colony optimization (ACO), 11 and generic algorithm (GA). 11 Nevertheless, these models have their specific limitations.…”

mentioning

confidence: 99%

Efficient hybrid enhanced genetic algorithm and ant colony system model for rerouting multimedia message in multiple node–link failures within wireless network

Owoade

Ogundile

Babalola

et al. 2023

Int J Communication

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show abstract

“…It also proposes a solution integrated with grid monitoring and optimization tool. Reliability-based scheduling is proposed in [28,30] with an objective of maximizing reliability. Reliability-based scheduling is proposed in [28,30] with an objective of maximizing reliability.…”

Section: Related Workmentioning

confidence: 99%

“…In , CG scheduling that gives priority to security is discussed. Reliability‐based scheduling is proposed in with an objective of maximizing reliability. Reliability‐based scheduling using modified GA is discussed in .…”

Section: Related Workmentioning

confidence: 99%

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Maximizing availability for task scheduling in computational grid using genetic algorithm

Prakash

Vidyarthi

2014

Concurrency and Computation

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Computational grid provides a wide distributed platform for high-end compute intensive applications. Grid scheduling is often carried out to schedule the submitted jobs on the nodes of the grid so that some characteristic parameter is optimized. Availability of the computational nodes is one of the important characteristic parameters and measures the probability of the node availability for job execution. This paper addresses the availability of the grid computational nodes for the job execution and proposes a model to maximize it. As such, the task scheduling problem in grid is nondeterministic polynomial-time hard, and often, metaheuristics techniques are applied to solve it. Genetic algorithm, a metaheuristic technique based on evolutionary computation, has been used to solve such complex optimization problem. This work proposes a technique for the grid scheduling problem using genetic algorithm with the objective to maximize availability. Simulation experiment, to evaluate the performance of the proposed algorithm, is conducted, and results reveal the effectiveness of the model. A comparative study has also been performed.

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A Computational Grid Scheduling Model to Maximize Reliability Using Modified GA

Cited by 2 publications

References 22 publications

Efficient hybrid enhanced genetic algorithm and ant colony system model for rerouting multimedia message in multiple node–link failures within wireless network

Efficient hybrid enhanced genetic algorithm and ant colony system model for rerouting multimedia message in multiple node–link failures within wireless network

Maximizing availability for task scheduling in computational grid using genetic algorithm

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