No abstract

Financial costs and energy savings are considered to be more critical on average for computationally intensive workflows, as such workflows which generally require extended execution times, and thus, require efficient energy consumption and entail a high financial cost. Through the effective utilization of scheduled gaps, the total execution time in a workflow can be decreased by placing uncompleted tasks in the gaps through approximate computations. In the current research, a novel approach based on multi-objective optimization is utilized with CloudSim as the underlying simulator in order to evaluate the VM (virtual machine) allocation performance. In this study, we determine the energy consumption, CPU utilization, and number of executed instructions in each scheduling interval for complex VM scheduling solutions to improve the energy efficiency and reduce the execution time. Finally, based on the simulation results and analyses, all of the tested parameters are simulated and evaluated with a proper validation in CloudSim. Based on the results, multi-objective PSO (particle swarm optimization) optimization can achieve better and more efficient effects for different parameters than multi-objective GA (genetic algorithm) optimization can.

No abstract

Power consumption in both wired and wireless networks is becoming an increasingly important concern across the world. Most scholars have concentrated on wireless, so we have taken wired networks as our main focus. In particular, wired network operators had an increased network concern to meet the decrease in traffic levels from which service quality (QoS) is achieved and maximize power consumption. To achieve the QOS and power consumption in a wired network, we provided comprehensive Power Consumption Management (PCMS) research modules that provide current and future access networks. The results of PCMS are being tested out with the genetic algorithm gives us the best-optimized module.