Multi objective task scheduling algorithm in cloud computing using grey wolf optimization

Mangalampalli, Sudheer; Karri, Ganesh Reddy; Kumar, Mithilesh

doi:10.1007/s10586-022-03786-x

Cited by 16 publications

(7 citation statements)

References 33 publications

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“…Mangalampalli, et al [35] proposed a Multi-Objective Task Scheduling Gray Wolf Optimization (MOTSGWO). This algorithm is capable of making scheduling decisions in real-time by considering the current state of cloud resources and future workload demands.…”

Section: Related Workmentioning

confidence: 99%

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

LI,

WANG

2024

ijacsa

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Task scheduling in cloud computing is a complex optimization problem influenced by the ever-changing user requirements and the different architectures of cloud systems. Efficiently distributing workloads across Virtual Machines (VMs) is critical to mitigate the negative consequences of inadequate and excessive workloads, such as higher power consumption and possible machine malfunctions. This paper presents a novel method for dynamic load balancing using a Modified Artificial Bee Colony (MABC) algorithm. The ABC algorithm has exceptional competence in solving complex nonlinear optimization problems based on bee colonies' foraging behavior. Nevertheless, the traditional version of the ABC algorithm cannot effectively use resources, resulting in a rapid decline in population diversity and an ineffective spread of knowledge about the best solution between generations. To address these limitations, this study integrates a genetic model into the algorithm, enhancing population diversity through crossover and mutation operators. The developed algorithm is compared with the prevailing algorithms to confirm its effectiveness. The results of the proposed MABC algorithm for the load balancing method are compared with the current ones, and it is observed that this algorithm is more beneficial in terms of cost and energy as well as resource utilization.

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Section: Related Workmentioning

confidence: 99%

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

LI,

WANG

2024

ijacsa

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“…Mangalampalli et al [31] developed a nature-inspired multi-objective job placement method employing the GWO algorithm. Their primary goal was to dynamically make job dispatching decisions considering the current state of cloud resources and future workload demands.…”

Section: Literature Surveymentioning

confidence: 99%

“…• Lack of data security during placement • Increased latency • Resource utilization deficiency [31] Multi-objective task scheduling grey wolf optimization (MOTSGWO)…”

Section: Aur Weightsmentioning

confidence: 99%

A Hybrid Many-Objective Optimization Algorithm for Job Scheduling in Cloud Computing Based on Merge-and-Split Theory

Khaleel,

Safran,

Alfarhood

et al. 2023

Mathematics

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Scheduling jobs within a cloud environment is a critical area of research that necessitates meticulous analysis. It entails the challenge of optimally assigning jobs to various cloud servers, each with different capabilities, and is classified as a non-deterministic polynomial (NP) problem. Many conventional methods have been suggested to tackle this difficulty, but they often struggle to find nearly perfect solutions within a reasonable timeframe. As a result, researchers have turned to evolutionary algorithms to tackle this problem. However, relying on a single metaheuristic approach can be problematic as it may become trapped in local optima, resulting in slow convergence. Therefore, combining different metaheuristic strategies to improve the overall system enactment is essential. This paper presents a novel approach that integrates three methods to enhance exploration and exploitation, increasing search process efficiency and optimizing many-objective functions. In the initial phase, we adopt cooperative game theory with merge-and-split techniques to train computing hosts at different utilization load levels, determining the ideal utilization for each server. This approach ensures that servers operate at their highest utilization range, maximizing their profitability. In the second stage, we incorporate the mean variation of the grey wolf optimization algorithm, making significant adjustments to the encircling and hunting phases to enhance the exploitation of the search space. In the final phase, we introduce an innovative pollination operator inspired by the sunflower optimization algorithm to enrich the exploration of the search domain. By skillfully balancing exploration and exploitation, we effectively address many-objective optimization problems. To validate the performance of our proposed method, we conducted experiments using both real-world and synthesized datasets, employing CloudSim software version 5.0. The evaluation involved two sets of experiments to measure different evaluation metrics. In the first experiment, we focused on minimizing factors such as energy costs, completion time, latency, and SLA violations. The second experiment, in contrast, aimed at maximizing metrics such as service quality, bandwidth utilization, asset utilization ratio, and service provider outcomes. The results from these experiments unequivocally demonstrate the outstanding performance of our algorithm, surpassing existing state-of-the-art approaches.

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“…This approach aims to describe cloud task scheduling as a mathematical function and to solve the constructed mathematical problem using the EA. Malti et al [30] proposed a multi-objective task scheduling grey wolf optimization (MOTSGWO) algorithm to optimize important parameter settings such as energy consumption, migration duration, and utilization in cloud services for the linear relationship between applications and workloads in the cloud task scheduler. Experimental results display that the MOTSGWO is outperform other scheduling algorithms.…”

Section: The Optimization Problem For Cloud Task Schedulingmentioning

confidence: 99%

Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling

2023

KSII TIIS

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Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a largescale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

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Multi objective task scheduling algorithm in cloud computing using grey wolf optimization

Cited by 16 publications

References 33 publications

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

A Hybrid Many-Objective Optimization Algorithm for Job Scheduling in Cloud Computing Based on Merge-and-Split Theory

Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling

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