Network load balancing and data categorization in cloud computing

Arunachalam Komathi, Somala Rama Kishore, Somala Rama Kishore, Athiyoor Kannan Velmurugan, Athiyoor Kannan Velmurugan, Maddipetlolu Rajendran Pavithra, Maddipetlolu Rajendran Pavithra, Yoganand Selvaraj, Yoganand Selvaraj, Akbar Sumaiya Begum, Akbar Sumaiya Begum, Dhakshnamoorthy Muthukumaran, Dhakshnamoorthy Muthukumaran


Cloud computing (CC) is rising quickly as a successful model presenting an on-demand structure. In the CC, the present investigation shows that load-balancing methods established on meta-heuristics offer better solutions for appropriate scheduling and allotment of resources. Conversely, several traditional approaches believe in only some quality of service (QoS) metrics and reject several significant components. Network load balancing and data categorization (NBDC) is proposed. This approach aims to enhance load balancing in the cloud field. This approach consists of two phases: the support vector machine (SVM) algorithm-based data categorization and the ant colony optimization (ACO) algorithm for distributing the network load on the virtual machine (VM). The SVM algorithm performs several data formats, such as text, image, audio, and video, resultant data class that offers high categorization accuracy in the cloud. The ACO algorithm reaches an efficient load balancing based on the time of execution (TE), time of throughput (TT), time of overhead (TO), time of optimization, and migration count (MC). Simulation results related to the baseline approach demonstrate an enhanced system function in terms of service level agreement violation, throughput, execution time, energy utilization, and execution time.


Ant colony optimization; Cloud computing; Data categorization; Load balancing; Support vector machine

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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