Maximizing Availability and Minimizing Markesan for Task Scheduling in Grid Computing Using NSGA II

Sahu, Dinesh Prasad; Singh, Karan; Prakash, Shiv

doi:10.1007/978-81-322-2526-3_24

Cited by 5 publications

(2 citation statements)

References 14 publications

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“… Crossover: Crossover [7][8][9][10] is the operator responsible for mating the chromosomes. Some popular crossover operators, in use, are uniform crossover, arithmetic, Heuristic, Intermediate, Scattered, OR, PMX, CX etc.…”

Section: Related Workmentioning

confidence: 99%

A Note on Bioinformatics using Genetic Algorithms

Sahu¹,

Singh²,

Sajid³

et al. 2015

IJCA

Self Cite

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The Bioinformatics (BI) is a sequence alignment, usually three sequences which can be RNA, DNA and proteins. Because the three or more given sequences can be of large lengths, aligning them by hand can be time consuming and in some cases traditionally impossible. Thus BI comes into use thereby aligning each sequence, and revealing the similar part of the given gene. BI finds great use in bioinformatics where it is used to predict the protein structure, its function, family or its domain. These problems are related to AI and can be classified in the NP complete domain of problems. Thus with the goal of identifying maximum similarities among the sequences, we can use various approaches and techniques like Genetic Algorithms (GA) and its variant in BI.

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Section: Related Workmentioning

confidence: 99%

A Note on Bioinformatics using Genetic Algorithms

Sahu¹,

Singh²,

Sajid³

et al. 2015

IJCA

Self Cite

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“…Heuristic algorithms can be divided into two categories according to their characteristics: Static ones [3][4][5][6] are generally simple strategies and are suitable for tasks that are known before scheduling. Dynamic algorithms [7][8][9] consider the dynamics of cloud platforms, but there are some problems, such as a large number of iterations and long computation times [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Task Scheduling Based on Adaptive Priority Experience Replay on Cloud Platforms

Gao

Shi

et al. 2023

Electronics

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Task scheduling algorithms based on reinforce learning (RL) have been important methods with which to improve the performance of cloud platforms; however, due to the dynamics and complexity of the cloud environment, the action space has a very high dimension. This not only makes agent training difficult but also affects scheduling performance. In order to guide an agent’s behavior and reduce the number of episodes by using historical records, a task scheduling algorithm based on adaptive priority experience replay (APER) is proposed. APER uses performance metrics as scheduling and sampling optimization objectives with which to improve network accuracy. Combined with prioritized experience replay (PER), an agent can decide how to use experiences. Moreover, this algorithm also considers whether a subtask is executed in a workflow to improve scheduling efficiency. Experimental results on Tpc-h, Alibaba cluster data, and scientific workflows show that a model with APER has significant benefits in terms of convergence and performance.

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SecQSON: Secure Query Scheduling and Ontology-Based Searching in Map-Evaluate-Reduce-Enabled Grid Environment

Nalini,

Kiran

2023

Lecture Notes in Electrical Engineering

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Maximizing Availability and Minimizing Markesan for Task Scheduling in Grid Computing Using NSGA II

Cited by 5 publications

References 14 publications

A Note on Bioinformatics using Genetic Algorithms

A Note on Bioinformatics using Genetic Algorithms

Task Scheduling Based on Adaptive Priority Experience Replay on Cloud Platforms

SecQSON: Secure Query Scheduling and Ontology-Based Searching in Map-Evaluate-Reduce-Enabled Grid Environment

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