A new approach to improve load balancing for increasing fault tolerance and decreasing energy consumption in cloud computing

Load balancing makes cloud computing greater knowledgeable and could increase client pleasure. At reward cloud computing is among the all most systems which offer garage of expertise in very lowers charge and available all the time over the net. However, it has extra vital hassle like security, load administration and fault tolerance. Load balancing inside the cloud computing surroundings has a large impact at the presentation. The set of regulations relates the sport idea to the load balancing manner to amplify the abilties in the public cloud environment. This textual content pronounces an extended load balance mannequin for the majority cloud concentrated on the cloud segregating proposal with a swap mechanism to select specific strategies for great occasions.

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Factors Affecting Fault Tolerance during Load Balancing in Cloud Computing

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i11.6079 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1523-1533

Author(s):

Bidush Kumar Sahoo , Et. al.

Keyword(s):

Cloud Computing ◽

Fault Tolerance ◽

Load Balancing ◽

South India ◽

Analysis Tool ◽

Factors Affecting ◽

Software Firms ◽

Statistical Analysis Tool ◽

Cost Efficient ◽

Support Cost

Cloud computing is built upon the advancement of virtualization and distributed computing to support cost-efficient usage of computing resources and to provide on demand services. After methodical analysis on various factors affecting fault tolerance during load balancing is performed and it is concluded that the factors influencing fault tolerance in load balancing are cloud security, adaptability etc. in comparatively more software firms. In this paper, we have created a model for various IT industries for checking the fault tolerance during Load balancing. An exploration is done with the help of some renowned IT farms and industries in South India. This work consists of 20 hypotheses which may affect the fault tolerance during load balancing in South India. It is verified by using potential statistical analysis tool i.e. Statistical Package for Social Science (SPSS).

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Performance Enhancement and Reduce Energy Consumption with Load Balancing Strategy in Green Cloud Computing

Advances in Intelligent Systems and Computing - Progress in Advanced Computing and Intelligent Engineering ◽

10.1007/978-981-33-4299-6_48 ◽

2021 ◽

pp. 585-597

Author(s):

Hitesh A. Bheda ◽

Chirag S. Thaker ◽

Darshan B. Choksi

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Load Balancing ◽

Performance Enhancement ◽

Green Cloud Computing ◽

Reduce Energy Consumption ◽

Green Cloud

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Resource Scheduling and Load Balancing Fusion Algorithm with Deep Learning Based on Cloud Computing

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch058 ◽

2020 ◽

pp. 1042-1057

Author(s):

Xiaojing Hou ◽

Guozeng Zhao

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Energy Consumption ◽

Load Balancing ◽

Energy Cost ◽

Learning Strategy ◽

Resource Scheduling ◽

High Energy ◽

Fusion Algorithm ◽

Low Efficiency

With the wide application of the cloud computing, the contradiction between high energy cost and low efficiency becomes increasingly prominent. In this article, to solve the problem of energy consumption, a resource scheduling and load balancing fusion algorithm with deep learning strategy is presented. Compared with the corresponding evolutionary algorithms, the proposed algorithm can enhance the diversity of the population, avoid the prematurity to some extent, and have a faster convergence speed. The experimental results show that the proposed algorithm has the most optimal ability of reducing energy consumption of data centers.

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Resource Scheduling and Load Balancing Fusion Algorithm with Deep Learning Based on Cloud Computing

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2018070104 ◽

2018 ◽

Vol 13 (3) ◽

pp. 54-72 ◽

Cited By ~ 2

Author(s):

Xiaojing Hou ◽

Guozeng Zhao

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Energy Consumption ◽

Load Balancing ◽

Data Centers ◽

Learning Strategy ◽

Resource Scheduling ◽

High Energy ◽

Fusion Algorithm ◽

Low Efficiency

With the wide application of the cloud computing, the contradiction between high energy cost and low efficiency becomes increasingly prominent. In this article, to solve the problem of energy consumption, a resource scheduling and load balancing fusion algorithm with deep learning strategy is presented. Compared with the corresponding evolutionary algorithms, the proposed algorithm can enhance the diversity of the population, avoid the prematurity to some extent, and have a faster convergence speed. The experimental results show that the proposed algorithm has the most optimal ability of reducing energy consumption of data centers.

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