Nature-inspired metaheuristic scheduling algorithms in cloud: a systematic review

Bothra, Sandeep Kumar; Singhal, Sunita

doi:10.17586/2226-1494-2021-21-4-463-472

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…This research provides a comprehensive view of the state-of-the-art cloud load balancing methods. It examines Metaheuristic Load balancing algorithms, taxonomy, key features, and challenges [8].…”

Section: Motivationmentioning

confidence: 99%

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Zhou,

Lilhore,

et al. 2023

J Cloud Comp

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Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.

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Section: Motivationmentioning

confidence: 99%

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Zhou,

Lilhore,

et al. 2023

J Cloud Comp

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“…Comprehensive work has been done by us on various meta-heuristic algorithms in literature [9], some of them are genetic algorithm (GA) [12], ant colony optimization (ACO) [13], particle swarm optimization (PSO) [14], artificial bee colony algorithm (ABC) [15] etc.…”

Section: Related Workmentioning

confidence: 99%

“…Due to its task-independent character, the meta-heuristic method delivers a global optimal solution. The researcher's ultimate objective is to maximize cloud resource usage while lowering costs for cloud's end users [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Cost effective hybrid genetic algorithm for workflow scheduling in cloud

Bothra

Singhal²,

Goyal³

2022

SRIT

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Cloud computing plays a significant role in everyone’s lifestyle by snugly linking communities, information, and trades across the globe. Due to its NP-hard nature, recognizing the optimal solution for workflow scheduling in the cloud is a challenging area. We proposed a hybrid meta-heuristic cost-effective load-balanced approach to schedule workflow in a heterogeneous environment. Our model is based on a genetic algorithm integrated with predict earliest finish time (PEFT) to minimize makespan. Instead of assigning the task randomly to a virtual machine, we apply a greedy strategy that assigns the task to the lowest-loaded virtual machine. After completing the mutation operation, we verify the dependency constraint instead of each crossover operation, which yields a better outcome. The proposed model incorporates the virtual machine’s performance variance as well as acquisition delay, which concedes the minimum makespan and computing cost. One of the most astounding aspects of our cost-effective hybrid genetic algorithm (CHGA) is its capacity to anticipate by creating an optimistic cost table (OCT) while maintaining quadratic time complexity. Based on the results of our meticulous experiments on some real-world workflow benchmarks and comprehensive analysis of some recently successful scheduling algorithms, we concluded that the performance of our CHGA is melodious. CHGA is 14.58188%, 11.40224%, 11.75306%, and 9.78841% cheaper than standard Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Cost Effective Genetic Algorithm(CEGA), and Cost-Effective Load-balanced Genetic Algorithm (CLGA), respectively.

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“…Therefore, to avoid long-term aesthetic fatigue, film artists will use a variety of forms of expression to expand their creations. However, the combination of dance choreography, lens selection, on-site videography, and post-editing in film dance is complex, which will test the ability of equipment and filmmakers more than traditional pure film [2]. Previously, embedded systems were the core of various digital products for filmmaking [3].…”

Section: Introductionmentioning

confidence: 99%

Film dance creation practice supported by Cyber Physical System

Lin

Zhang

2023

PLoS ONE

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The traditional eight arts include film and dance. Dance is often included in the creation of films. With the progress of the times, dance has shown unprecedented vitality in film. This also puts forward higher requirements for shooting technology in film creation. To solve the contradiction between high performance of equipment and energy sensing, a new energy aware scheduling framework is proposed based on Cyber Physical System, which utilizes the balance between performance and energy consumption optimization, and uses allocation technology and list scheduling to ensure energy constraints. Besides, a highly energy-efficient and stable scheduling algorithm for film creation is constructed. The algorithm problems are mainly divided into functional safety requirements, verification problems, and energy consumption optimization problems under functional safety. The experimental results show that the system can obtain better schedulability at a lower time complexity and reasonably reflect the dynamic and static energy constraints ratio. The basic framework system based on dynamic step size also achieves better time performance than other step sizes. According to the experiment’s findings, the energy consumption of all scheduling components is relatively low and can be maintained within a specific range. The research and analysis of this study can provide a theoretical reference for the equipment algorithm of film dance creation practice, promote interaction with technical practice, and assist in promoting the development process of film dance creation.

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Nature-inspired metaheuristic scheduling algorithms in cloud: a systematic review

Cited by 4 publications

References 26 publications

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Cost effective hybrid genetic algorithm for workflow scheduling in cloud

Film dance creation practice supported by Cyber Physical System

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