An Energy-optimized Embedded load balancing using DVFS computing in Cloud Data centers

Task scheduling is a significant challenge in the cloud environment as it affects the network's performance regarding the workload of the cloud machines. It also directly impacts the consumed energy, therefore the profit of the cloud provider. This paper proposed an algorithm that prioritizes the tasks regarding their execution deadline. We also categorize the physical machines considering their configuration status. Henceforth, the proposed method assigns the jobs to the physical machines with the same priority class close to the user. Furthermore, we reduce the consumed energy of the machines processing the low-priority tasks using the DVFS method. The proposed method migrates the jobs to maintain the workload balance, or if the machines' class changed according to their scores. We have evaluated and validated the proposed method in the CloudSim library. The simulation results demonstrate that the proposed method optimized energy consumption by 12% and power consumption by 20%.

Automated summary

This paper proposes an algorithm that prioritizes tasks based on their execution deadline and categorizes physical machines based on their configuration status, aiming to optimize task scheduling in the cloud environment. By assigning jobs to physically close machines with the same priority class, and reducing energy consumption through the DVFS method for low-priority tasks, the proposed method achieves workload balance and machine class change. Evaluation using the CloudSim library shows an optimized energy consumption of 12% and power consumption of 20% with this method.