Novel Genetic Algorithm with Dual Chromosome Representation for Resource Allocation in Container-Based Clouds

Tan, Boxiong; Ma, Hui; Mei, Yi

doi:10.1109/cloud.2019.00078

Cited by 23 publications

(11 citation statements)

References 11 publications

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“…To compare the ICA’s performance to the Efficient Resource Allocation with Score (ERAS) (Lepakshi and Prashanth, 2020) and GA (Tan et al , 2019) algorithms and encode them, MATLAB was used. A computer is used with Core2Due and 2 GHz memory.…”

Section: Resultsmentioning

confidence: 99%

“…As opposed to current algorithms that only regard EFT for allocation, the results revealed that the ERAS algorithm provides higher efficiency with improved reliability. Tan et al (2019) suggested a dual-chromosome Genetic Algorithm (GA) to address the RAP in container-based clouds. Their studies contrasted the GA to a better-suited descending algorithm on real-world datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Tan et al (2019) suggested a dual-chromosome Genetic Algorithm (GA) to address the RAP in container-based clouds. Their studies contrasted the GA to a better-suited descending algorithm on real-world datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

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A new method for human resource allocation in cloud-based e-commerce using a meta-heuristic algorithm

Al-Shourbaji

Zogaan

2021

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PurposeThe human resource (HR) allocation problem is one of the critical dimensions of the project management process. Due to this nature of the problem, researchers are continually optimizing one or more critical scheduling and allocation challenges in different ways. This study aims to optimize two goals, increasing customer satisfaction and reducing costs using the imperialist competitive algorithm.Design/methodology/approachCloud-based e-commerce applications are preferred to conventional systems because they can save money in many areas, including resource use, running expenses, capital costs, maintenance and operation costs. In web applications, its core functionality of performance enhancement and automated device recovery is important. HR knowledge, expertise and competencies are becoming increasingly valuable carriers for organizational competitive advantage. As a result, HR management is becoming more relevant, as it seeks to channel all of the workers’ energy into meeting the organizational strategic objectives. The allocation of resources to maximize benefit or minimize cost is known as the resource allocation problem. Since discovering solutions in polynomial time is complicated, HR allocation in cloud-based e-commerce is an Nondeterministic Polynomial time (NP)-hard problem. In this paper, to promote the respective strengths and minimize the weaknesses, the imperialist competitive algorithm is suggested to solve these issues. The imperialist competitive algorithm is tested by comparing it to the literature’s novel algorithms using a simulation.FindingsEmpirical outcomes have illustrated that the suggested hybrid method achieves higher performance in discovering the appropriate HR allocation than some modern techniques.Practical implicationsThe paper presents a useful method for improving HR allocation methods. The MATLAB-based simulation results have indicated that costs and waiting time have been improved compared to other algorithms, which cause the high application of this method in practical projects.Originality/valueThe main novelty of this paper is using an imperialist competitive algorithm for finding the best solution to the HR allocation problem in cloud-based e-commerce.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A new method for human resource allocation in cloud-based e-commerce using a meta-heuristic algorithm

Al-Shourbaji

Zogaan

2021

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“…In spite of the speedy growth in technologies, there are certain problems in managing and developing MS in the cloud 22,23 . The allocation of container resources in the cloud is an NP‐hard issue and it should be resolved using genetic algorithm, reinforcement learning approach, chemical reaction optimization algorithm, and reinforcement learning‐based microservice allocation (RL‐MA), improved particle swarm optimization based quantum evolutionary algorithm (IPOQEA) 24,25 . The optimum solution will be attained only by assessing each possible combination.…”

Section: Introductionmentioning

confidence: 99%

Self‐improved moth flame for optimal container resource allocation in cloud

Vhatkar¹,

Bhole²

2022

Concurrency and Computation

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SUMMARY Resource allocation in the cloud is becoming more complicated and challenging due to the rising necessities of cloud services. Effective management of virtual resources in the cloud is of large significance since it has a great impact on both the operational cost and scalability of the cloud environment. Nowadays, containers are becoming more popular in this regard due to their characteristics like reduced overhead and portability. Conventional resource allocation schemes are usually modeled for the migration and allocation of virtual machines (VM), as a result; the question may arise on, “how these strategies can be adapted for the management of a containerized cloud”. This work evolves the solution to this issue by introducing a new fitness oriented moth flame algorithm (F‐MFA) for optimizing the allocation of containers. Further in this work, the optimal allocation relies on certain constraints like balanced cluster use, system failure, total network distance (TND), security and threshold distance, and credibility factor as well. In the end, the supremacy of the presented model is computed to the conventional models in terms of cost and convergence analysis.

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“…Variable-length chromosome outperforms fixed-length in satellite constellations [12] and road traffic coordination as a multipath optimization problem [13]. A dual chromosome is better than a single chromosome in the optimization of resource allocation in container-based clouds [14]. In this paper, we proposed a shorter problemspecific chromosome than [2], which faster and produces a better quality of the solutions.…”

Section: Introductionmentioning

confidence: 99%

An Effective Chromosome Representation on Proportional Tuition Fees Assessment Using NSGA-II

Jauhari

Mahmudy

Basuki

2019

JITeCS

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Proportional tuition fees assessment is an optimization process to find a compromise point between student willingness to pay and institution income. Using a genetic algorithm to find optimal solutions requires effective chromosome representations, parameters, and operator genetic to obtain efficient search. This paper proposes a new chromosome representation and also finding efficient genetic parameters to solve the proportional tuition fees assessment problem. The results of applying the new chromosome representation are compared with another chromosome representation in the previous study. The evaluations show that the proposed chromosome representation obtains better results than the other in both execution time required and the quality of the solutions.

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Novel Genetic Algorithm with Dual Chromosome Representation for Resource Allocation in Container-Based Clouds

Cited by 23 publications

References 11 publications

A new method for human resource allocation in cloud-based e-commerce using a meta-heuristic algorithm

A new method for human resource allocation in cloud-based e-commerce using a meta-heuristic algorithm

Self‐improved moth flame for optimal container resource allocation in cloud

An Effective Chromosome Representation on Proportional Tuition Fees Assessment Using NSGA-II

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