Cloud Computing Based Resource Allocation by Random Load Balancing Technique

Bano, Hamida; Javaid, Nadeem; Tehreem, Komal; Ansar, Kainat; Zahid, Maheen; Nazar, Tooba

doi:10.1007/978-3-030-02613-4_3

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…The method failed to utilize energy consumption and networking resources in the upper‐level controller. Bano et al 4 devised a Cloud–fog computing platform that offered the services through the internet using remote servers from residential areas. Here, fog computing was utilized for reducing the load and latency rates.…”

Section: Literature Surveymentioning

confidence: 99%

“…It is based on the important perception of virtualization 2 . With the virtualization technology, users can execute server, memory, networks, and storage resources in a much appropriate way 3,4 . Due to the innovative expansion of cloud computing, there are several organizations and persons who are permissible to subcontract the substantial data to the cloud as an alternative of constructing and preserving limited data centers.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic resource allocation in cloud infrastructure using ant lion‐based auto‐regression model

Kavitha

Rao

2022

Int J Communication

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Summary Cloud computing provides a quick, simple, and gainful means for configuring and assigning the resources for a web‐based appliance, like medical records systems, smart grid applications, and security management infrastructures. The optimization of resource allocation in the cloud is a major task for meeting customer demands and maximizing profit. This paper devises an optimization method for allocating resources in cloud infrastructures. The main contribution is to provide a framework for allocating the available resource to the deserving tasks. Here, the dynamic resource allocation is performed to allocate the resources dynamically as requested by users without affecting the system performance. Here, the ant lion‐based auto‐regressive optimization (ALAO) strategy is employed to allocate the cloud resources. ALAO is designed by integrating Ant Lion Optimizer (ALO) in auto‐regression model for further enhancing the allocation of resources, and a new fitness function is adapted, which considers certain parameters, such as cost, speed, and load. The results proved that the proposed ALAO algorithm attained enhanced performance with a maximal profit of 0.153, a minimum load of 0.028, and a minimal task assignment cost of 0.094.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic resource allocation in cloud infrastructure using ant lion‐based auto‐regression model

Kavitha

Rao

2022

Int J Communication

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“…There are various devices in the fog environment, i.e. servers, gateways, routers which consume energy while performing operations [15]. Load balancing mainly is done to reduce the overall energy consumption in the fog nodes.…”

Section: Performance Measurements That Impact Load Balancingmentioning

confidence: 99%

A systematic study of load balancing approaches in the fog computing environment

Kaur

Aron

2021

J Supercomput

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Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal proportion. It has become a necessity in the fog layer to equally, and equitably distribute all the workload among the existing VMs in the segment. Till now, many load balancing techniques have been proposed for fog computing. An empirical study of existing methods in load balancing have been conducted, and taxonomy has been presented in a hierarchical form. Besides, the article contains the year-wise comprehensive review and summary of research articles published in the area of load balancing from 2013 to 2020. Furthermore, article also contains our proposed fog computing architecture to resolve load balancing problem. It also covers current issues and challenges that can be resolved in future research works. The paper concludes by providing future directions.

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Research on Cloud Office Resource Allocation Algorithm Based on Correction Weight PSO

Wang

Chun²,

Gan

et al. 2022

Advances in Artificial Intelligence and Security

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Cloud Computing Based Resource Allocation by Random Load Balancing Technique

Cited by 3 publications

References 10 publications

Dynamic resource allocation in cloud infrastructure using ant lion‐based auto‐regression model

Dynamic resource allocation in cloud infrastructure using ant lion‐based auto‐regression model

A systematic study of load balancing approaches in the fog computing environment

Research on Cloud Office Resource Allocation Algorithm Based on Correction Weight PSO

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