An improved load balanced metaheuristic scheduling in cloud

Aruna, M.; Bhanu, D.; Karthik, S.

doi:10.1007/s10586-017-1213-9

Cited by 21 publications

(8 citation statements)

References 18 publications

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“…This strategy has been simulated with cloud sim toolkit package. The results proved that the proposed algorithm gave better performance than the Min-Min, PSO, and FCFS methods [26]. Jena proposed a method that focused on task scheduling using a Multi-Objective nested Particle Swarm Optimization (MOPSO) to make the best of energy and processing time.…”

Section: Ct=extm+∑mentioning

confidence: 95%

Cloud Computing Load Balancing using Genetic and Throttled Hybrid Algorithm

Eladl¹,

Ziedan²,

Gaafar³

2019

IJET

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The demand for cloud computing resources is increasing due to its accessibility everywhere at any time. When the number of clients for cloud services increases, the load on the cloud nodes becomes high. This status requires load balancing to evenly distribute client requests among the available Virtual Machines (VMs) in a Data Center (DC). There are different standard dynamic load balancing techniques, such as Throttled and Active Monitoring. In this paper, a Genetic Algorithm (GA) is incorporated with a throttled to improve load balancing. The improvement is achieved by enhancing the overall response time, the data center processing time, and the maximum resource utilization. Simulation results show the improved performance of the proposed method compared to the ESCE and Throttled. Keyword-Cloud Computing, Load Balancing, Genetic Algorithm, Cloud Analyst I. INTRODUCTION Cloud computing technology has seen a speedy growth in recent years. It has affected the growth in several sub-technologies, like storage, distributed networks, virtualization, participation, and connectivity. In [1, 2], a cloud is considered as a distributed system that can handle diverse resource requirements by users. The rule system of a cloud-user relationship is planned by the Service Level Agreement (SLA), which is an agreement between a user and a service provider. As indicated in[3], the physical structure and repairing system can be achieved by the cloud, not the user. This automatically decreases the total cost and increases system efficiency. As indicated in [4], cloud computing gives an easy way to hold data and files, and it includes the following features: virtualization, distributed computing, and web services. Any complex task that calls for enormous computational resources can be serviced by cloud computing using distributed resources in a decentralized style [5]. Although cloud computing has many countenances, there are obstacles as load balancing over the resources and task scheduling [6]. Task scheduling in a cloud environment is a problem of specifying tasks to an appropriate machine to finish their work. A task should be done within a given period. The cloud task scheduler restores the information from the cloud service manager about the case of available resources [7]. Therefore, the scheduling of task problem can be qualified as the method of finding out a model mapping for execution of user tasks with the aim of reaching the desired goals [8]. Algorithms of task scheduling in cloud computing can be done depending on diverse objectives such as balancing the load, minimizing waiting time, and maximizing the utilization of resources and the throughput of the full system. Therefore, an efficient task scheduling algorithm aims to balance diverse and conflicting parameters together at the same time[9]. Moreover, resources are not utilized efficiently due to the rise in the load so for that reason load balancing is required [10]. Load balancing is the approach of redistributing the whole load into separate nodes to guarantee th...

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Section: Ct=extm+∑mentioning

confidence: 95%

Cloud Computing Load Balancing using Genetic and Throttled Hybrid Algorithm

Eladl¹,

Ziedan²,

Gaafar³

2019

IJET

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“…In [32], a model for load balancing on the Internet is presented, aiming to reduce the overall processing time for different tasks. In the other work, both the firefly algorithm and particle swarm optimization are made to balance the load of the entire system and reduce the makespan as well [33]. In [34], the MOGWO method, which is one of the multi-objective algorithms, is used to distribute data equitably among virtual machines and to reduce the working time.…”

Section: Algorithms In Service Allocationmentioning

confidence: 99%

Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets

et al. 2021

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The expansion of Internet of Things (IoT) services and the huge amount of data generated by different sensors signify the importance of cloud computing services such as Storage as a Service more than ever. IoT traffic imposes such extra constraints on the cloud storage service as sensor data preprocessing capability and load-balancing between data centers and servers in each data center. Furthermore, service allocation should be allegiant to the quality of service (QoS). In the current work, an algorithm is proposed that addresses the QoS in storage service allocation. The proposed hybrid multi-objective water cycle and grey wolf optimizer (MWG) considers different QoS objectives (e.g., energy, processing time, transmission time, and load balancing) in both the fog and cloud Layers, which were not addressed altogether. The MATLAB script is used to simulate and implement our algorithms, and services of different servers, e.g., Amazon, Dropbox, Google Drive, etc., are considered. The MWG has 7%, 13%, and 25% improvement, respectively, in comparison with multi-objective water cycle algorithm (MOWCA), k-means based GA (KGA), and non-dominated sorting genetic algorithm (NSGAII) in metric of spacing. Moreover, the MWG has 4%, 4.7%, and 7.3% optimization in metric of quality in comparison to MOWCA, KGA, and NSGAII, respectively. The new hybrid algorithm, MWG, not only yielded to the consideration of three objectives in service selection but also improved the performance compared to the works that considered one or two objective(s). The overall optimization shows that the MWG algorithm has 7.8%, 17%, and 21.6% better performance than MOWCA, KGA, and NSGAII in the obtained best result by considering different objectives, respectively.

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“…Distributed load Iranpour et al [20] two dynamic work load balancing are engaged in managing the load of the large scale SAAS. Here the fuzzy is used in the admission control for the cloud and the game theory is employed in the first layer load balancing, where the next layer uses either the RR or the LCM and Randles et al [18] the heuristics Li, et al [9], Zhao et al [31], LD et al [32], Adamuthe et al [42] and the metaheuristic aruna et al [45], the heuristics and the metaheuristic approaches for the load balancing employs the optimization technique based on the honey bee, PSO, GA, Tabu search and machine learning techniques for the balancing of the loads and enhance the performance in terms of response time, resource utilization, cost and the processing time.…”

Section: Survey On Virtual-balancing For Loads On M-cloudmentioning

confidence: 99%

Survey on Virtual Load Balancing Architectures in Mobile Cloud

Duraipandian¹

2019

JSWS

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The mobile cloud computing, that enables the mobile device application to be hosted, equipped and constructed by the cloud-computing has become more prominent among wide range of application and the concerns that range either in the small-scale or the large-scale. This lead to the overcrowded accessing of the mobile clouds which lead to insufficiency of resources and the latency in the service provisioning. The possible researches emerged with the solutions of load balancing, but the in capabilities in the traditional load balancing developed were in compatible with the mobile cloud computing. So the paper details the survey on the virtual load balancing techniques available and their employment in the mobile cloud computing to improve the balancing of the influx traffic that are addressed towards the mobile cloud computing.

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An improved load balanced metaheuristic scheduling in cloud

Cited by 21 publications

References 18 publications

Cloud Computing Load Balancing using Genetic and Throttled Hybrid Algorithm

Cloud Computing Load Balancing using Genetic and Throttled Hybrid Algorithm

Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets

Survey on Virtual Load Balancing Architectures in Mobile Cloud

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