Modeling an Optimized Approach for Load Balancing in Cloud

Abstract

Despite significant infrastructure improvements, cloud computing still faces numerous challenges in terms of load balancing. Several techniques have been applied in the literature to improve load balancing efficiency. Recent research manifested that load balancing techniques based on metaheuristics provide better solutions for proper scheduling and allocation of resources in the cloud. However, most of the existing approaches consider only a single or few QoS metrics and ignore many important factors. The performance efficiency of these approaches is further enhanced by merging with machine learning techniques. These approaches combine the relative benefits of load balancing algorithm backed up by powerful machine learning models such as Support Vector Machines (SVM). In the cloud, data exists in huge volume and variety that requires extensive computations for its accessibility, and hence performance efficiency is a major concern. To address such concerns, we propose a load balancing algorithm, namely, Data Files Type Formatting (DFTF) that utilizes a modified version of Cat Swarm Optimization (CSO) along with SVM. First, the proposed system classifies data in the cloud from diverse sources into various types, such as text, images, video, and audio using one to many types of SVM classifiers. Then, the data is input to the modified load balancing algorithm CSO that efficiently distributes the load on VMs. Simulation results compared to existing approaches showed an improved performance in terms of throughput (7%), the response time (8.2%), migration time (13%), energy consumption (8.5%), optimization time (9.7%), overhead time (6.2%), SLA violation (8.9%), and average execution time (9%). These results outperformed some of the existing baselines used in this research such as CBSMKC, FSALB, PSO-BOOST, IACSO-SVM, CSO-DA, and GA-ACO.

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

“…Energies 2021, 14, x FOR PEER REVIEW Junaid et al [38] proposed CSO and SVM methods for the load balancing a in cloud to reduce the energy consumption. The file type is classified in the cl SVM is applied to classify the files one by one.…”

“…The DNN method has the limitation of the ov problem that affects the performance of the model. The general architecture of D it is implemented in the prediction of network energy consumption as shown in Junaid et al [38] proposed CSO and SVM methods for the load balancing algorithm in cloud to reduce the energy consumption. The file type is classified in the cloud and SVM is applied to classify the files one by one.…”

“…Another taxonomy in the cloud energy consumption is the machine learning methods [38][39][40] and this is applied to predict higher energy consumption and SLA violation.…”

“…The resource allocation is provided based on the predicted results of the machine learning to avoid higher energy consumption and SLA violation. The commonly applied machine learning methods are the support vector machine (SVM) [38,39] and deep neural network (DNN) [40]. The DNN model shows better prediction performance compared to the SVM.…”

Optimal Load Balancing in Cloud using ANFIS and MGWO based Polynomial Neural Network

“…High communication cost is considered as the major drawback of this approach. Junaid et al (2019) developed an optimized approach that utilized Data Files Type Formatting under a cloud environment. Accuracy, precision, f-measure, recall were the performance metrics utilized for evaluation.…”