Modeling an Optimized Approach for Load Balancing in Cloud

Muhammad Junaid,Syed Sajid Hussain,Rao Naveed Bin Rais,Adnan Sohail,Adeel Ahmed,Osman Khalid,Naveed Ejaz,Imran Ali Khan

doi:10.1109/access.2020.3024113

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.

Highlights

Over the years, an increase in online applications has resulted in huge volumes of data accumulated daily
The output of the Support Vector Machines (SVM) is fed into Improved Cat Swarm Optimization (ICSO) for load balancing in the cloud environment
File type classification is done in various formats such as video, audio, text, and images in a cloud environment resulting in an appropriate data class

Summary

Introduction

An increase in online applications has resulted in huge volumes of data accumulated daily. Despite the significant evolution of clouding computing to handle such diverse data, still it faces numerous challenges in real-time processing and load balancing of resources employed to process mega volumes of data. In [1], the authors have applied the Bin-packing algorithm for multi capacity Bin-packing to achieve task waiting time and degree of imbalance on cloud resources. In a work by [3], the authors used a dynamic clustering algorithm to achieve throughput and execution time. A study by [4] applied a dynamic real clustering algorithm for achieving geographical load balancing in the cloud that results in better throughput and response time. In [5], the authors applied adaptive load balancing to achieve optimal resource provisioning resulting in better resource utilization and throughput

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