Energy-Aware Prediction-Based Load Balancing Approach with VM Migration for the Cloud Environment

Patel, Durga; Gupta, R. K.; Pateriya, R. K.

doi:10.1007/978-981-13-6351-1_6

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…In recent years, a lot of algorithms have been proposed to schedule tasks on VMs [9][10][11][12][13][14][15][16][17][18][19]. These algorithms differ from each other in their methods and their objectives which are divided into three categories which are task scheduling, VMs placement, and complete mapping.…”

Section: Related Workmentioning

confidence: 99%

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Genetic-Based Multi-objective Task Scheduling Algorithm in Cloud Computing Environment

Emara¹

2021

IJIES

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Recently, resource management is the major issues in cloud computing (CC) environment because of dynamic heterogeneity of cloud computing environment. The task scheduling and virtual machines (VMs) allocation play a vital role in resources management of CC. Most of existing works for these issues aim to achieve single objective as maximizing resource utilization, load balancing, or power management. Currently, the big challenge in CC is building task scheduling and virtual machines (VMs) allocation algorithms that consider all these objectives in the same time. This problem is called task scheduling with VMs allocation multi-objectives problem which is NP completeness problem. In this paper, a new task scheduling algorithm is proposed for achieving efficient resource management based on these objectives. This proposed algorithm uses a modified genetic algorithm (GA) to find the optimal solution for choosing the most appropriate VMs for executing received tasks and their appropriate servers that will deploy these VMs. This proposed algorithm uses a matrix structure for representing the chromosome of GAS which combines the ids of tasks, VMs, and servers. Simulation results show that the proposed algorithm achieves better performance than ETVMC, TSACS, and ACO algorithms in terms of makespan, scheduling length, throughput, resource utilization, energy consumption, and imbalance degree.

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Section: Related Workmentioning

confidence: 99%

“…The disadvantage of this work is that load balancing and minimizing power consumption objectives are not considered. Although there are more VMs placement algorithms like [18] but they are developed to achieve a single objective and not to achieve a multi objective.…”

Section: Vms Placement Algorithmsmentioning

confidence: 99%

Genetic-Based Multi-objective Task Scheduling Algorithm in Cloud Computing Environment

Emara¹

2021

IJIES

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“…Patel et al [54] focus on minimizing the VM migrations to save energy consumption. The migration of heavily loaded VMs considers CPU utilization as a parameter to evaluate its performance.…”

Section: Energy-aware Load Balancing Techniquesmentioning

confidence: 99%

“…Also, assigning priority tasks and choosing the relevant VM for each job can be enhanced by considering the functions' QoS parameters. [54] Can be applied on any other algorithm to achieve better results.…”

Section: Soltanshahi Et Almentioning

confidence: 99%

Load Balancing in Cloud Computing: A Comprehensive Survey on Recent Techniques

2021

IJATCSE

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The Internet has become a fundamental necessity of daily activity. It has a more significant impact on modernizing the digital world. Therefore, cloud computing has been one of the most promising technological advances in recent days. It aims to provide millions of consumers around the globe with smooth computing services. In workload distributions and system behavior, the cloud environment is extremely dynamic, leading to load imbalances across data center resources. Balancing load across the systems is a crucial component of cloud computing, avoiding some nodes' overloading while others have little or no work to do. The problem of load balancing in the cloud has been linked to a large amount of research, recognizing its role and impact. This paper bestows a thorough investigation of the new load balancing algorithms for the cloud environment. This study's primary focus is the concept of load balancing, a literature review on load balancing and task scheduling techniques, and different measurement parameters

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“…Energy e ciency has thus become a major concern, ensuring ubiquitous monitoring, consistent and seamless communication. The authors in [10,11] have implemented an IoT based energy management system using edge computation infrastructure in convergence with deep reinforcement learning (DRL). The unpredictability in energy supplies could be overcome using such DRL based scheduling scheme yielding better energy consumption at lower cost with even lower delays.…”

Section: Related Workmentioning

confidence: 99%

Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything

Priya

Bhattacharya

Maddikunta

et al. 2020

Journal of Parallel and Distributed Computing

106

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The smart applications dominating the planet in the present day and age, have innovatively progressed to deploy Internet of Things (IoT) based systems and related infrastructures in all spectrums of life. Since, variety of applications are being developed using this IoT paradigm, there is an immense necessity for storing data, processing them to get meaningful information and render suitable services to the end-users. The "thing" in this decade is not only a smart sensor or a device; it can be any physical or household object, a smart device or a mobile. With the ever increasing rise in population and smart device usage in every sphere of life, when all of such "thing"s generates data, there is a chance of huge data tra c in the internet. This could be handled only by integrating "Internet of Everything (IoE)" paradigm with a completely diversified technology -Cloud Computing. In order to handle this heavy flow of data traffic and process the same to generate meaningful information, various services in the global environment are utilized. Hence the primary focus revolves in integrating these two diversified paradigm shifts to develop intelligent information processing systems. Energy E cient Cloud Based Internet of Everything (EECloudIoE) architecture is proposed in this study, which acts as an initial step in integrating these two wide areas thereby providing valuable services to Preprint submitted to Elsevier February 4, 2020 the end users. The utilization of energy is optimized by clustering the various IoT network using Wind Driven Optimization Algorithm. Next, an optimized Cluster Head (CH) is chosen for each cluster, using Firefly Algorithm resulting in reduced data tra c in comparison to other non-clustering schemes. The proposed clustering of IoE is further compared with the widely used state of the art techniques like Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA) and Adaptive Gravitational Search algorithm (AGSA). The results justify the superiority of the proposed methodology outperforming the existing approaches with an increased life-time and reduction in tra c.

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Energy-Aware Prediction-Based Load Balancing Approach with VM Migration for the Cloud Environment

Cited by 17 publications

References 8 publications

Genetic-Based Multi-objective Task Scheduling Algorithm in Cloud Computing Environment

Genetic-Based Multi-objective Task Scheduling Algorithm in Cloud Computing Environment

Load Balancing in Cloud Computing: A Comprehensive Survey on Recent Techniques

Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything

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