HSO: A Hybrid Swarm Optimization Algorithm for Re-Ducing Energy Consumption in the Cloudlets

Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made

OFFLOADING DECISION SELECTION METHOD FOR ENERGY EFFICIENCY AND LOW LATENCY IN HETEROGENE SIMUATION ENVIRONMENTS

Mobile Cloud Computing (MCC) is a technology that can overcome the problems of high computing and limited resources owned by mobile devices. However, in practice, MCC has a very long transmission distance from the mobile device, resulting in a large latency. Mobile Edge Computing (MEC) is a technology that exists to overcome this problem. However, new problems arise from the presence of this MEC.One of the problems that arise is the selection of offloading decisions from mobile devices. Several studies consider energy efficiency / large latency or both in determining offloading decisions. However, there are not many studies that consider the movement of mobile devices in determining offloading decisions. Even though the movement of mobile devices is also very influential on latency because tasks need to be migrated to another edge server when a mobile device has moved. Several studies that have addressed this issue apply the solution to smaller, less heterogeneous simulation environments.This study used a new method of offloading decision-making that pays attention to the movement of mobile devices in a heterogeneous environment. This proposed method uses Black Widow Optimization in solving the problem of decision selection when offloading. From the simulation results, the performance of the proposed method is better than the comparison method in terms of the amount of energy consumption and delay latency.

HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets

Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made