scholarly journals Genetic Algorithm for Independent Job Scheduling in Grid Computing

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Muhanad Tahrir Younis ◽  
Shengxiang Yang
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Job Scheduling ◽  
Computational Power ◽  
New Mutation ◽  
Complete Problem ◽  
Geographically Distributed ◽  
Np Complete ◽  
And Storage ◽  
Better Than

Grid computing refers to the infrastructure which connects geographically distributed computers ownedby various organizations allowing their resources, such as computational power and storage capabilities, to beshared, selected, and aggregated. Job scheduling is the problem of mapping a set of jobs to a set of resources.It is considered one of the main steps to e ciently utilise the maximum capabilities of grid computing systems.The problem under question has been highlighted as an NP-complete problem and hence meta-heuristic methodsrepresent good candidates to address it. In this paper, a genetic algorithm with a new mutation procedure tosolve the problem of independent job scheduling in grid computing is presented. A known static benchmark forthe problem is used to evaluate the proposed method in terms of minimizing the makespan by carrying out anumber of experiments. The obtained results show that the proposed algorithm performs better than some knownalgorithms taken from the literature.

scholarly journals Exploration based Genetic Algorithm for Job Scheduling on Grid Computing

2016 ◽  
Vol 5 (3) ◽  
pp. 91-100
Author(s):  
Hanaa Abdelrahman ◽  
Mohammed Bakri Bashir ◽  
Adil Yousif
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Large Scale ◽  
Job Scheduling ◽  
Optimization Problems ◽  
Scheduling Algorithm ◽  
Adaptive Method ◽  
Virtual Organizations ◽  
Good Effect ◽  
Exploration Process

Grid computing presents a new trend to distribute and Internet computing to coordinate large scale heterogeneous resources providing sharing and problem solving in dynamic, multi- institutional virtual organizations. Scheduling is one of the most important problems in computational grid to increase the performance. Genetic Algorithm is adaptive method that can be used to solve optimization problems, based on the genetic process of biological organisms. The objective of this research is to develop a job scheduling algorithm using genetic algorithm with high exploration processes. To evaluate the proposed scheduling algorithm this study conducted a simulation using GridSim Simulator and a number of different workload. The research found that genetic algorithm get best results when increasing the mutation and these result directly proportional with the increase in the number of job. The paper concluded that, the mutation and exploration process has a good effect on the final execution time when we have large number of jobs. However, in small number of job mutation has no effects.

scholarly journals Quantum­Inspired Genetic Algorithm for Solving the Test Suite Minimization Problem

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Minimization Problem ◽  
Test Suite ◽  
Test Cases ◽  
Special Importance ◽  
Quantum Superposition ◽  
Complete Problem ◽  
Minimal Subset ◽  
Np Complete ◽  
Test Requirements

Test Suite Minimization problem is a nondeterministic polynomial time (NP) complete problem insoftware engineering that has a special importance in software testing. In this problem, a subset with a minimalsize that contains a number of test cases that cover all the test requirements should be found. A brute­forceapproach to solving this problem is to assume a size for the minimal subset and then search to find if there is asubset of test cases with the assumed size that solves the problem. If not, the assumed minimal size is graduallyincremented, and the search is repeated. In this paper, a quantum­inspired genetic algorithm (QIGA) will beproposed to solve this problem. In it, quantum superposition, quantum rotation and quantum measurement willbe used in an evolutionary algorithm. The paper will show that the adopted quantum techniques can speed upthe convergence of the classical genetic algorithm. The proposed method has an advantage in that it reduces theassumed minimal number of test cases using quantum measurements, which makes it able to discover the minimalnumber of test cases without any prior assumptions.

Evaluating Heuristics for Scheduling Dependent Jobs in Grid Computing Environments

2012 ◽  
pp. 1099-1113
Author(s):  
Geoffrey Falzon ◽  
Maozhen Li
Keyword(s):  
Performance Evaluation ◽  
Grid Computing ◽  
Job Scheduling ◽  
Critical Role ◽  
Difficult Problem ◽  
Acyclic Graphs ◽  
Computing Environments ◽  
Np Complete ◽  
Grid Resources ◽  
Direct Acyclic Graphs

Job scheduling plays a critical role in the utilisation of grid resources by mapping a number of jobs to grid resources. However, the heterogeneity of grid resources adds some challenges to the work of job scheduling, especially when jobs have dependencies which can be represented as Direct Acyclic Graphs (DAGs). It is widely recognised that scheduling m jobs to n resources with an objective to achieve a minimum makespan has shown to be NP-complete, requiring the development of heuristics. Although a number of heuristics are available for job scheduling optimisation, selecting the best heuristic to use in a given grid environment remains a difficult problem due to the fact that the performance of each original heuristic is usually evaluated under different assumptions. This paper evaluates 12 representative heuristics for dependent job scheduling under one set of common assumptions. The results are presented and analysed, which provides an even basis in comparison of the performance of those heuristics. To facilitate performance evaluation, a DAG simulator is implemented which provides a set of tools for DAG job configuration, execution, and monitoring. The components of the DAG simulator are also presented in this paper.

High Exploitation Genetic Algorithm for Job Scheduling on Grid Computing

2016 ◽  
Vol 9 (3) ◽  
pp. 221-228 ◽  
Author(s):  
Walaa AbdElrouf ◽  
Adil Yousif ◽  
Mohammed Bakri Bashir
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Job Scheduling

An Improved Genetic Algorithm and A New Discrete Cuckoo Algorithm for Solving the Classical Substitution Cipher

2019 ◽  
Vol 10 (2) ◽  
pp. 109-130 ◽  
Author(s):  
Ashish Jain ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Cuckoo Search ◽  
Combinatorial Problems ◽  
Secret Key ◽  
Improved Genetic Algorithm ◽  
Efficiency And Effectiveness ◽  
Np Complete ◽  
Metaheuristic Techniques ◽  
Better Than ◽  
Substitution Cipher

Searching secret key of classical ciphers in the keyspace is a challenging NP-complete problem that can be successfully solved using metaheuristic techniques. This article proposes two metaheuristic techniques: improved genetic algorithm (IGA) and a new discrete cuckoo search (CS) algorithm for solving a classical substitution cipher. The efficiency and effectiveness of the proposed techniques are compared to the existing tabu search (TS) and genetic algorithm (GA) techniques using three criteria: (a) average number of key elements correctly detected, (b) average number of keys examined before determining the required key, and (c) the mean performance time. As per the results obtained, the improved GA is comparatively better than the existing GA for criteria (a) and (c), while the proposed CS strategy is significantly better than rest of the algorithms (i.e., GA, IGA, and TS) for all three criteria. The obtained results indicate that the proposed CS technique can be an efficient and effective option for solving other similar NP-complete combinatorial problems also.

OPTIMAL COMPETITIVE HOPFIELD NETWORK WITH STOCHASTIC DYNAMICS FOR MAXIMUM CUT PROBLEM

2004 ◽  
Vol 14 (04) ◽  
pp. 257-265 ◽  
Author(s):  
JIAHAI WANG ◽  
ZHENG TANG ◽  
QIPING CAO ◽  
RONGLONG WANG
Keyword(s):  
Communication Networks ◽  
Stochastic Dynamics ◽  
Undirected Graph ◽  
Vlsi Circuits ◽  
Hopfield Network ◽  
Local Minima ◽  
Complete Problem ◽  
Maximum Cut ◽  
Np Complete ◽  
Better Than

In this paper, introducing stochastic dynamics into an optimal competitive Hopfield network model (OCHOM), we propose a new algorithm that permits temporary energy increases which helps the OCHOM escape from local minima. The goal of the maximum cut problem, which is an NP-complete problem, is to partition the node set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The problem has many important applications including the design of VLSI circuits and design of communication networks. Recently, Galán-Marín et al. proposed the OCHOM, which can guarantee convergence to a global/local minimum of the energy function, and performs better than the other competitive neural approaches. However, the OCHOM has no mechanism to escape from local minima. The proposed algorithm introduces stochastic dynamics which helps the OCHOM escape from local minima, and it is applied to the maximum cut problem. A number of instances have been simulated to verify the proposed algorithm.

scholarly journals Creating of model of genetic algorithm used to optimization of construction of jet engine element

Mechanik ◽  
2017 ◽  
Vol 90 (7) ◽  
pp. 603-605
Author(s):  
Adam Kozakiewicz ◽  
Rafał Kieszek
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Turbine Engines ◽  
Jet Engine ◽  
Complete Problem ◽  
Traditional Technology ◽  
Np Complete

In this paper authors show results of optimization of compressor discs in turbine engines. The problem of optimizing the thickness of the disc brought to the NP-complete problem, and solved it by using one of the genetic algorithms – evolutionary algorithm. Correctness of model and optimization algorithm were constantly checked. At the end of this paper, compressor disc created due to traditional technology and disc created by BLISK technology were compared.

An Improved Genetic Algorithm and A New Discrete Cuckoo Algorithm for Solving the Classical Substitution Cipher

2021 ◽  
pp. 1431-1455
Author(s):  
Ashish Jain ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Problems ◽  
Secret Key ◽  
Improved Genetic Algorithm ◽  
Efficiency And Effectiveness ◽  
Effective Option ◽  
Np Complete ◽  
Metaheuristic Techniques ◽  
Better Than ◽  
Substitution Cipher

Searching secret key of classical ciphers in the keyspace is a challenging NP-complete problem that can be successfully solved using metaheuristic techniques. This article proposes two metaheuristic techniques: improved genetic algorithm (IGA) and a new discrete cuckoo search (CS) algorithm for solving a classical substitution cipher. The efficiency and effectiveness of the proposed techniques are compared to the existing tabu search (TS) and genetic algorithm (GA) techniques using three criteria: (a) average number of key elements correctly detected, (b) average number of keys examined before determining the required key, and (c) the mean performance time. As per the results obtained, the improved GA is comparatively better than the existing GA for criteria (a) and (c), while the proposed CS strategy is significantly better than rest of the algorithms (i.e., GA, IGA, and TS) for all three criteria. The obtained results indicate that the proposed CS technique can be an efficient and effective option for solving other similar NP-complete combinatorial problems also.

Evaluating Heuristics for Scheduling Dependent Jobs in Grid Computing Environments

2012 ◽  
pp. 31-46
Author(s):  
Geoffrey Falzon ◽  
Maozhen Li
Keyword(s):  
Performance Evaluation ◽  
Grid Computing ◽  
Job Scheduling ◽  
Critical Role ◽  
Difficult Problem ◽  
Acyclic Graphs ◽  
Computing Environments ◽  
Np Complete ◽  
Grid Resources ◽  
Direct Acyclic Graphs

Job scheduling plays a critical role in the utilisation of grid resources by mapping a number of jobs to grid resources. However, the heterogeneity of grid resources adds some challenges to the work of job scheduling, especially when jobs have dependencies which can be represented as Direct Acyclic Graphs (DAGs). It is widely recognised that scheduling m jobs to n resources with an objective to achieve a minimum makespan has shown to be NP-complete, requiring the development of heuristics. Although a number of heuristics are available for job scheduling optimisation, selecting the best heuristic to use in a given grid environment remains a difficult problem due to the fact that the performance of each original heuristic is usually evaluated under different assumptions. This paper evaluates 12 representative heuristics for dependent job scheduling under one set of common assumptions. The results are presented and analysed, which provides an even basis in comparison of the performance of those heuristics. To facilitate performance evaluation, a DAG simulator is implemented which provides a set of tools for DAG job configuration, execution, and monitoring. The components of the DAG simulator are also presented in this paper.

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