Abstract: Cloud computing is one of the significant technologies that is used to provide seamless internet surfing for large-scale applications and data storing purposes. The cloud is described as a large platform that enables users to access data from the internet without needing to buy storing space in their equipment such as a computer. Many studies have analysed the load-balancing technique on the cloud to distribute tasks equally between servers using the Equally Spread Current Execution (ESCE) algorithm. ESCE, which is a dynamic load balancer, has quite a few problems such as average level performance and too long of response time which affected the Quality of Services. This research has simulated a cloud computing concept using the ESCE Load Modelling technique with the CloudAnalyst simulator for three servers of Data Center (DC) locations. The ESCE was simulated to enhance its algorithm’s performance as a load balancer and higher throughput in the cloud environment. The result shows that ESCE average overall response time is shortest when the DC is located at R0 with response times of 15.05s, 13.05s with 10 VMs, and 8.631s with the Optimize Response Time brokerage policy. This research is significant to promote notable load-balancing technique testing for virtualized cloud machines data centers on Quality of Services (QoS) aware tasks for Internet of Things (IoT) services.

Anisah Hamimi Zamri, Nor Syazwani Mohd Pakhrudin, Shuria Saaidin and Murizah Kassim. “Equally Spread Current Execution Load Modelling with Optimize Response Time Brokerage Policy for Cloud Computing”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.2 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140257

@article{Zamri2023,
title = {Equally Spread Current Execution Load Modelling with Optimize Response Time Brokerage Policy for Cloud Computing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140257},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140257},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Anisah Hamimi Zamri and Nor Syazwani Mohd Pakhrudin and Shuria Saaidin and Murizah Kassim}
}