Author(s): Sweta S Munnoli and Devi Venkatesh Gowtham

Abstract: In addition to cutting down on energy use, the green cloud-based service also drastically cuts down on operating expenses. Strongly coupled data centers demand controlled energy, constant performance, and overall optimization of excess energy consumption in order to do considerable calculations, which in turn give entire processing influence from a wide collection of resources. An energy-saving scheduling method is the focus of the research, which makes use of green cloud technologies. In recent years, cloud computing has been integrated into several domains, including computing, research, industry, and business. The need for specialized hardware along with additional resources is rendered obsolete by the provision of many services across the internet through cloud computing. Problems with energy efficiency, resource heterogeneity, and resource usage are only a few of the issues that cloud computing systems encounter. Consolidation approaches like as task scheduling as well as virtual machines (VMs) are used to address these concerns. There has been a plethora of research on task scheduling. Researchers have examined the issue using a variety of metrics and aims. The essay delves into the topic of virtualized cloud data centers' energy usage and effective resource use. Task categorization and thresholds form the basis of the proposed method, which aims to improve resource use and scheduling efficiency. Every company needs a highly efficient structure that is both flexible and homogeneous across all of their cloud environments. This research aims to reduce energy consumption in green cloud systems by using a hybrid scheduling strategy that combines a minimal completion time with a priority-based weighted round-robin. An Artificial Neural Network (ANN) based load balancing method is presented in this study. ANN makes demand predictions and then distributes resources accordingly. This method uses less energy than the cautious over-provisioning strategy since it constantly adjusts the number of active servers based on current demand. In addition, a server that is operating at a high utilization may handle more task with the same amount of power, but it will use more power overall. At last, we take a look at the current state of load balancing in cloud computing and compare it to others using different metrics.

DOI: 10.33545/27076571.2024.v5.i1b.112

Pages: 109-113 | Views: 658 | Downloads: 213

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