Metaheuristic Algorithms for Independent Task Scheduling in Symmetric and Asymmetric Cloud Computing Environment

Samee, Nagwan M. Abdel; Ahmed, Sara; Seoud, Rania Ahmed Abdel Azeem Abul

doi:10.3844/jcssp.2019.594.611

Cited by 5 publications

(8 citation statements)

References 33 publications

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“…Metaheuristics techniques are based on analogues of biological concepts. It has been demonstrated that metaheuristic-based strategies can achieve near-optimal solutions in a reasonable amount of time for some complex problems [2,28]. Metaheuristic approaches were used to address resource scheduling difficulties such as makespan and response time reduction.…”

Section: Metaheuristic Techniques Used In Cloud Task Schedulingmentioning

confidence: 99%

See 1 more Smart Citation

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm for Ecosystem Sustainability

Zubair¹,

Razak²,

Ngadi³

2021

Preprint

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The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The modified SOS algorithm is developed to solve independent task scheduling problems. This paper proposes a modified symbiotic organisms search based scheduling algorithm for efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aimed to minimize the task execution time (Makespan), response, degree of imbalance and cost and improve the convergence speed for an optimal solution in an IaaS cloud. The performances of the proposed technique have been evaluated using a Cladism toolkit simulator, and the solutions are found to be better than the existing standard (SOS) technique and PSO.

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Section: Metaheuristic Techniques Used In Cloud Task Schedulingmentioning

confidence: 99%

“…Cloud computing is a modern computing model that offers virtualization computing services as a utility to Cloud service users [1][2][3][4]. It is a concept for obtaining resources from a customizable shared resource, such as a group of networks, servers, storage, utilities, and applications, in instantaneously and based on request.…”

Section: Introductionmentioning

confidence: 99%

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm for Ecosystem Sustainability

Zubair¹,

Razak²,

Ngadi³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Cloud computing is a modern computing model that offers the virtualization of computing services as a utility to Cloud service users [1][2][3][4]. It is a concept for obtaining resources from a customizable shared resource, such as a group of networks, servers, storage, utilities, and applications, instantaneously and based on request.…”

Section: Introductionmentioning

confidence: 99%

“…Heuristic and metaheuristic scheduling techniques have been used to address the task scheduling problem in cloud computing [2,16]. For more trivial concerns, heuristic methods provide optimal results, but, as the size of the problem rises, the solutions generated by these algorithms will be less optimal.…”

Section: Introductionmentioning

confidence: 99%

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

Zubair

Razak

Ngadi

et al. 2022

Sensors

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The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm’s mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.

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“…Several metaheuristic algorithms that mimic the actions of insect or animal classes in nature have been developed using a combination of deterministic rules and randomness [9] [11]. Many of the optimization problems that can be formulated are extremely nonlinear, with multimodal objective landscapes and a series of dynamic, nonlinear constraints [12][13] [14].…”

Section: Introductionmentioning

confidence: 99%

Current Perspective of Symbiotic Organisms Search Technique in Cloud Computing Environment: A Review

Zubair¹,

Razak²,

Ngadi³

et al. 2021

IJACSA

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Nature-inspired algorithms in computer science and engineering are algorithms that take their inspiration from living things and imitate their actions in order to construct functional models. The SOS algorithm (symbiotic organisms search) is a new promising metaheuristic algorithm. It is based on the symbiotic relationship that exists between different species in an ecosystem. Organisms develop symbiotic bonds like mutualism, commensalism, and parasitism to survive in their environment. Standard SOS has since been modified several times, either by hybridization or as better versions of the original algorithm. Most of these modifications came from engineering construction works and other discipline like medicine and finance. However, little improvement on the standard SOS has been noticed on its application in cloud computing environment, especially cloud task scheduling. As a result, this paper provides an overview of SOS applications in task scheduling problem and suggest a new enhanced method for better performance of the technique in terms of fast convergence speed.

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Metaheuristic Algorithms for Independent Task Scheduling in Symmetric and Asymmetric Cloud Computing Environment

Cited by 5 publications

References 33 publications

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm for Ecosystem Sustainability

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm for Ecosystem Sustainability

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

Current Perspective of Symbiotic Organisms Search Technique in Cloud Computing Environment: A Review

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