Multi Objective Task Scheduling Algorithm for Cloud Computing Using Whale Optimization Technique

Reddy, G. Narendrababu; Kumar, S. Phani

doi:10.1007/978-981-10-8657-1_22

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…That of PSO include MOPSO , TSPSO (Jena 2015) and HAPSO (Midya et al 2018). Other meta-heuristic algorithms include Multi-objective Whale Optimization Algorithm (MWOA) (Reddy and Kumar 2017), nature-inspired Chaotic Squirrel Search Algorithm (CSSA) (Sanaj and Prathap 2020), etc.…”

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

Zhou,

Tian,

Buyya

et al. 2024

Artif Intell Rev

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With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling.

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Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

Zhou,

Tian,

Buyya

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…On the other hand, performance evaluation in multi-cloud systems was not successfully adapted by cloud environments. G. N. Reddyet et al [15] introduced the whale optimization approach as a solution to the cloud data centre work scheduling problem. They evaluated resource utilisation, energy use, and service quality as fitness indicators.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

Paulraj

Sethukarasi²,

Neelakandan³

et al. 2023

PLoS ONE

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Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species’ obligate brood parasitism (laying eggs in other species’ nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior.

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“…Authors in paper Reddy and Kumar (2017) have discussed the whale optimization-based algorithm which mimics the humpback whales. The authors shared that these whales hunt their food which is many small fishes close to the surface.…”

Section: Related Workmentioning

confidence: 99%

“…In cloud computing, plenty of tasks runs concurrently and use the resources online. The scheduling reduces the computation time and processing time of tasks (Alkhanak et al, 2016, Liu et al, 2017, Reddy and Kumar, 2017, Rimal and Maier, 2016, Zhu et al, 2015, Wahab, O. A., et al 2017.…”

mentioning

confidence: 99%

Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment

Lakhwani

Sharma

Sandhu

et al. 2023

International Journal of Cloud Applications and Computing

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Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, scheduling of tasks from scientific workflows on VMs requires efficient and optimized workflow scheduling techniques. This paper proposes an optimised workflow scheduling approach that aims to improve the utilization of cloud resources without increasing execution time and execution cost.

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Multi Objective Task Scheduling Algorithm for Cloud Computing Using Whale Optimization Technique

Cited by 14 publications

References 12 publications

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment

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