Multi-Objective Local Pollination-Based Gray Wolf Optimizer for Task Scheduling Heterogeneous Cloud Environment

Gokuldhev, M.; Singaravel, G.; Mohan, Nitinder

doi:10.1142/s0218126620501005

Cited by 15 publications

(4 citation statements)

References 42 publications

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“…In some specific scenarios, we can give weight to constrains to meet complex scenes. For multi-objectives problems, the objectives can be integrated based on their weights in realistic scenarios or can be evaluated by Pareto efficiency [23,95,120]. Approaches for scheduling in Cloud computing can be classified into six categories including Dynamic Programming(DP), Probability algorithm (Random), Heuristic method, Meta-Heuristic algorithm, Hybrid algorithms and Machine Learning.…”

Section: Discussionmentioning

confidence: 99%

Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions

Zhou¹,

Tian²,

Buyya³

2021

Preprint

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As the quantity and complexity of information processed by software systems increase, large-scale software systems have an increasing requirement for high-performance distributed computing systems. With the acceleration of the Internet in Web 2.0, Cloud computing as a paradigm to provide dynamic, uncertain and elastic services has shown superiorities to meet the computing needs dynamically. Without an appropriate scheduling approach, extensive Cloud computing may cause high energy consumptions and high cost, in addition that high energy consumption will cause massive carbon dioxide emissions. Moreover, inappropriate scheduling will reduce the service life of physical devices as well as increase response time to users' request. Hence, efficient scheduling of resource or optimal allocation of request, that usually a NP-hard problem, is one of the prominent issues in emerging trends of Cloud computing. Focusing on improving quality of service (QoS), reducing cost and abating contamination, researchers have conducted extensive work on resource scheduling problems of Cloud computing over years. Nevertheless, growing complexity of Cloud computing, that the super-massive distributed system, is limiting the application of scheduling approaches. Machine learning, a utility method to tackle problems in complex scenes, is used to resolve the resource scheduling of Cloud computing as an innovative idea in recent years. Deep reinforcement learning (DRL), a combination of deep learning (DL) and reinforcement learning (RL), is one branch of the machine learning and has a considerable prospect in resource scheduling of Cloud computing. This paper surveys the methods of resource scheduling with focus on DRL-based scheduling approaches in Cloud computing, also reviews the application of DRL as well as discusses challenges and future directions of DRL in scheduling of Cloud computing.

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Section: Discussionmentioning

confidence: 99%

Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions

Zhou¹,

Tian²,

Buyya³

2021

Preprint

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“…is work required testing with real-time applications and adding more parameters to the method are needed [28]. Compare the various scheduling algorithms in this work.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

et al. 2022

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Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real-time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology-enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES-ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC-PSO, and PSO-COGENT algorithms, the created HES-ACO algorithm was simulated at CloudSim and found to optimize all parameters.

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“…On May 13, the called for strengthening the construction of teachers' innovation and entrepreneurship education and teaching capacity, improving student entrepreneurship guidance services, and improving innovation and entrepreneurship funding support and policy guarantee systems [12]. They have been elevated to a national strategic position [13].…”

Section: Introductionmentioning

confidence: 99%

Optimization method of college students' entrepreneurial path based on improved multi-objective gray wolf algorithm

Qiu

2023

Preprint

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Fromperspectives, and they can be classified into three categories according to different viewpoints: "platform theory", that is, the entrepreneurial environment is regarded as the government and society for entrepreneurs to create new A public platform built by enterprises; "factor theory", that is, the entrepreneurial environment is understood as a combination of various factors affecting entrepreneurial behavior; "system theory", that is, the entrepreneurial environment is regarded as a complex integrated into various entrepreneurial environment elements. The system believes that the entrepreneurial environment refers to the situation around the entrepreneur, which is of course, because the connotation and extension of the entrepreneurial environment are very rich, it is difficult to accurately define its concept. Drawing on the viewpoints of "factor theory" and "system theory", the author believes development of college students and their innovative careers.With the advent of the Internet era, more and more college students use the Internet to start businesses. If college students want to realize their own value through entrepreneurship, they need to choose their own development path. The optimization of it, namely, the optimization of entrepreneurial opportunities, the optimization of entrepreneurial resources, and the optimization of entrepreneurial teams. In order proposes two improvement strategies based on the analysis of the individual movement of the gray wolf when the algorithm is optimized. The second is to improve the control parameter adjustment strategy, and use the power function instead of the linear function. This paper analyzes choice by optimizing the entrepreneurial path to further improve the entrepreneurialone, it provides a favorable environment.

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Multi-Objective Local Pollination-Based Gray Wolf Optimizer for Task Scheduling Heterogeneous Cloud Environment

Cited by 15 publications

References 42 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

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

Optimization method of college students' entrepreneurial path based on improved multi-objective gray wolf algorithm

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