Meta-heuristic Approaches for Effective Scheduling in Infrastructure as a Service Cloud: A Systematic Review

Konjaang, J. Kok; Xu, Lina

doi:10.1007/s10922-020-09577-2

Cited by 21 publications

(3 citation statements)

References 159 publications

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“…The proposed change in Eq. ( 16) increases the solution diversity by generating the values of a in the range of [1,3]. It improves the exploration potential of PEWOA.…”

Section: Proposed Parallel Enhanced Whale Optimization Algorithmmentioning

confidence: 99%

“…Cloud computing has become a prime resource for a variety of applications including banking, healthcare, entertainment, and E-commerce etc. It provides numerous sorts of services on a pay-per-use basis to both users and applications by utilizing its computing, storage, and bandwidth resources [1]. The three services models are Platform as a Service, Infrastructure as a Service and Software as a Service [2], while the deployment models are private, public, community and hybrid [3].…”

Section: Introductionmentioning

confidence: 99%

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Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

Khan,

Aziz,

Osman

et al. 2024

IEEE Access

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Cloud computing has been imperative for computing systems worldwide since its inception. The researchers strive to leverage the efficient utilization of cloud resources to execute workload quickly in addition to providing better quality of service. Among several challenges on the cloud, task scheduling is one of the fundamental NP-hard problems. Meta-heuristic algorithms are extensively employed to solve task scheduling as a discrete optimization problem and therefore several meta-heuristic algorithms have been developed. However, they have their own strengths and weaknesses. Local optima, poor convergence, high execution time, and scalability are the predominant issues among meta-heuristic algorithms. In this paper, a parallel enhanced whale optimization algorithm is proposed to schedule independent tasks in the cloud with heterogeneous resources. The proposed algorithm improves solution diversity and avoids local optima using a modified encircling maneuver and an adaptive bubble net attacking mechanism. The parallelization technique keeps the execution time low despite its internal complexity. The proposed algorithm minimizes the makespan while improving resource utilization and throughput. It demonstrates the effectiveness of the proposed PEWOA against the best performing enhanced whale optimization algorithm (WOAmM) and Multi-core Random Matrix Particle Swarm Optimization (MRMPSO). The algorithm consistently produces better results with varying number of tasks on GoCJ dataset, indicating better scalability. The experiments are conducted in CloudSim utilizing a variety of GoCJ and HCSP instances. Various statistical tests are also conducted to evaluate the significance of the results.

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“…The proposed change in Eq. ( 16) increases the solution diversity by generating the values of a in the range of [1,3]. It improves the exploration potential of PEWOA.…”

Section: Proposed Parallel Enhanced Whale Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

Khan,

Aziz,

Osman

et al. 2024

IEEE Access

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“…The systematic approach for resource allocation algorithms is classified into heuristics and meta-heuristics. The heuristics algorithms focus on a specific problem, whereas the metaheuristics algorithms are interdisciplinary [4], [5]. Figure 2 represents resource allocation algorithms used in grid computing systems.…”

Section: Literature Surveymentioning

confidence: 99%

GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing

Sharma,

Chaurasia,

Sharma

et al. 2023

IEEE Access

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Resource sharing, selection, and aggregation are vital functions of grid computing. However, managing resources in a grid-based environment is a stimulating task. It is necessary to update the topographical dispersal of resources possessed by various organisations with various usage guidelines, financial frameworks, load, and availability patterns. Users and servers have different objectives, methods, and needs. This article suggests a cost-effective framework for resource management in grid computing to look at and address these resource management difficulties. The proposed framework has three main functions, which help in grid construction, load balancing, and resource allocation. A Genetic engineering approach has been implemented to establish a relationship between the resource pool and the jobs of the nodes that improve the resource utilization. The proposed methodology also optimizes the overall cost by minimizing turnaround time. The results of proposed research are compared with commonly used algorithms and claiming 1.5 to 10% better results.

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Mathematical and Software for Building the Rating of the Largest IaaS Suppliers by the Threshold Aggregation Method

Разумников

2023

Lecture Notes in Electrical Engineering

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Meta-heuristic Approaches for Effective Scheduling in Infrastructure as a Service Cloud: A Systematic Review

Cited by 21 publications

References 159 publications

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing

Mathematical and Software for Building the Rating of the Largest IaaS Suppliers by the Threshold Aggregation Method

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