Makespan Optimisation in Cloudlet Scheduling with Improved DQN Algorithm in Cloud Computing

Chraibi, Amine; Alla, Said Ben; Ezzati, Abdellah

doi:10.1155/2021/7216795

Cited by 10 publications

(2 citation statements)

References 33 publications

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“…The results of FCFS, RR, SJF, and GA using K-means and LACO are compared against each other, and it was found that LACO (using K-means) algorithm outperforms the other algorithm in terms of makespan and degree of balance. The makespan [14,15] calculated for a set of cloudlets(T1, T2….TK) indicates the maximum execution time spent by all VMs(VM1, VM2….VMn) in executing the cloudlets (Eq-2). 𝑀𝑎𝑘𝑒𝑠𝑝𝑎𝑛 = 𝑚𝑎𝑥(𝐸(𝑉𝑀 1 ), 𝐸(𝑉𝑀 2 ), … … .…”

Section: Performance and Results Analysismentioning

confidence: 99%

Dynamic Task Migration for Enhanced Load Balancing in Cloud Computing using K-means Clustering and Ant Colony Optimization

Priyadarshini,

Pradhan,

Pattnaik

et al. 2023

IJRITCC

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Cloud computing efficiently allocates resources, and timely execution of user tasks is pivotal for ensuring seamless service delivery. Central to this endeavour is the dynamic orchestration of task scheduling and migration, which collectively contribute to load balancing within virtual machines (VMs). Load balancing is a cornerstone, empowering clouds to fulfill user requirements promptly. To facilitate the migration of tasks, we propose a novel method that exploits the synergistic potential of K-means clustering and Ant Colony Optimization (ACO). Our approach aims to maximize the cloud ecosystem by improving several critical factors, such as the system's make time, resource utilization efficiency, and workload imbalance mitigation. The core objective of our work revolves around the reduction of makespan, a metric directly tied to the overall system performance. By strategically employing K-means clustering, we effectively group tasks with similar attributes, enabling the identification of prime candidates for migration. Subsequently, the ACO algorithm takes the reins, orchestrating the migration process with an inherent focus on achieving global optimization. The multifaceted benefits of our approach are quantitatively assessed through comprehensive comparisons with established algorithms, namely Round Robin (RR), First-Come-First-Serve (FCFS), Shortest Job First (SJF), and a genetic load balancing algorithm. To facilitate this evaluation, we harness the capabilities of the CloudSim simulation tool, which provides a platform for realistic and accurate performance analysis. Our research enhances cloud computing paradigms by harmonizing task migration with innovative optimization techniques. The proposed approach demonstrates its prowess in harmonizing diverse goals: reducing makespan, elevating resource utilization efficiency, and attenuating the degree of workload imbalance. These outcomes collectively pave the way for a more responsive and dependable cloud infrastructure primed to cater to user needs with heightened efficacy. Our study delves into the intricate domain of cloud-based task scheduling and migration. By synergizing K-means clustering and ACO algorithms, we introduce a dynamic methodology that refines cloud resource management and bolsters the quintessential facet of load balancing. Through rigorous comparisons and meticulous analysis, we underscore the superior attributes of our approach, showcasing its potential to reshape the landscape of cloud computing optimization.

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Section: Performance and Results Analysismentioning

confidence: 99%

Dynamic Task Migration for Enhanced Load Balancing in Cloud Computing using K-means Clustering and Ant Colony Optimization

Priyadarshini,

Pradhan,

Pattnaik

et al. 2023

IJRITCC

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“…In [19] developed an ACO method to select the best virtual machine for executing a cloudlet to reduce energy consumption and execution time. We also proposed two tasks scheduling works [20] and [21]; the first accelerated the PSO task scheduling algorithm, and the second improved the makespan and other performance metrics using deep q-learning.…”

Section: Int J Elec and Comp Eng Issn: 2088-8708mentioning

confidence: 99%

An efficient cloudlet scheduling via bin packing in cloud computing

Chraibi

Alla

Ezzati

2022

IJECE

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<p>In this ever-developing technological world, one way to manage and deliver services is through cloud computing, a massive web of heterogenous autonomous systems that comprise adaptable computational design. Cloud computing can be improved through task scheduling, albeit it being the most challenging aspect to be improved. Better task scheduling can improve response time, reduce power consumption and processing time, enhance makespan and throughput, and increase profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling problem into a bin packing problem. Three modifies implementations of bin packing algorithms were proposed to be used for task scheduling (MBPTS) based on the minimisation of makespan. The results, which were based on the open-source simulator CloudSim, demonstrated that the proposed MBPTS was adequate to optimise balance results, reduce waiting time and makespan, and improve the utilisation of the resource in comparison to the current scheduling algorithms such as the particle swarm optimisation (PSO) and first come first serve (FCFS).</p>

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A novel dynamic multi-objective task scheduling optimization based on Dueling DQN and PER

et al. 2023

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Makespan Optimisation in Cloudlet Scheduling with Improved DQN Algorithm in Cloud Computing

Cited by 10 publications

References 33 publications

Dynamic Task Migration for Enhanced Load Balancing in Cloud Computing using K-means Clustering and Ant Colony Optimization

Dynamic Task Migration for Enhanced Load Balancing in Cloud Computing using K-means Clustering and Ant Colony Optimization

An efficient cloudlet scheduling via bin packing in cloud computing

A novel dynamic multi-objective task scheduling optimization based on Dueling DQN and PER

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