HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in the Cloudlets

Abstract

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.

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

“…It involves the use of FS algorithms to filter out irrelevant and redundant data features from the original dataset to prevent over-fitting [6,13] and improve the classification accuracy of the model. Feature selection also reduces the classification models' complexity in time and space domains [14][15][16][17][18]. The main idea of this paper is to employ the TLBO-based algorithm for features subset selection in BC diagnosis.…”

Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes

“…Hence, a hybrid adaptive approach called Hoeffding Naive Bayes Tree (hnbt) which performs better than the component prediction methods for both complex and simple concepts has been proposed. This concept of this method based on executing a naive Bayes prediction on each training feature, then, comparing the prediction performance with the majority class [19][20][21][22][23][24][25]. The number of times the naïve Bayes makes a correct prediction of the true class is noted (by taking counts) compared to the majority class.…”

An Effective Context Sensitive Offloading System for Mobile Cloud Environments using Support Value-based Classification

“…Cost-efficient offloading scheme OMMC [11] TOPSIS and Energy model Managed the trade-off between time and energy consumption.…”

“…Better accuracy in offloading Precise execution offloading mCloud [22] TOPSIS and Cost model mCloud Context-aware CoSMOS [22] Cost functions CoSMOS Context-aware Energy effective offloading choice algorithm [11] Lyapunov optimization algorithm Minimized average energy consumption, response time, less computational complexity Energy-efficient energy consumption model. Our proposed extricates the context information of input tasks via these models.…”