Comparative Analysis of Load Balancing Algorithms for Efficient Task Scheduling in Cloud Computing

Abstract: In the field of information technology cloud computing is a recently developed technology. In such a complicated system, an effective load balancing scheme is critical in order to meet peak user demands and deliver high-quality services. Load balancing is a way of distributing workload among several nodes over network links in order to maximize resource utilization, decrease data processing and response time, and avoid overload. There have been a number of load balancing algorithms suggested that concentrate on important factors including processing time, response time, and processing costs. These techniques, however, ignore cloud computing scenarios. At the same time, there is a few research works that focuses on the subject of load balancing in cloud computing. Motivated by this issue, this study addresses the load balancing challenge in cloud computing by comparing natural inspired Load Balancing Algorithms based on the resource utilization metric. The chosen load balancing methods will next be tested and assessed using the CloudSim simulator to choose the proper natural inspired Load balancing algorithms that solves the problem of load balancing in cloud computing, according to the result of the simulation it can be concluded that the LBA_HB is better than the HBB_LB based on the results for the response time, MakeSpan, and the degree of imbalance. Keywords: Load Balancing, Cloud Computing, Algorithms, CloudSim, HBB-LB, LBA_HB. Title: Comparative Analysis of Load Balancing Algorithms for Efficient Task Scheduling in Cloud Computing Author: Sawsan Rabaya, Yazeed Al Moayed International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 11, Issue 3, July 2023 - September 2023 Page No: 160-171 Research Publish Journals Website: www.researchpublish.com Published Date: 18-September-2023 DOI: https://doi.org/10.5281/zenodo.8355059 Paper Download Link (Source) https://www.researchpublish.com/papers/comparative-analysis-of-load-balancing-algorithms-for-efficient-task-scheduling-in-cloud-computingInternational Journal of Computer Science and Information Technology Research, ISSN 2348-1196 (print), ISSN 2348-120X (online), Research Publish Journals, Website: www.researchpublish.co

An optimized Load Balancing Technique for Virtual Machine Migration in Cloud Computing

Cloud computing (CC) is a service that uses subscription storage & computing power. Load balancing in distributed systems is one of the most critical pieces. CC has been a very interesting and important area of research because CC is one of the best systems that stores data with reduced costs and can be viewed over the internet at all times. Load balance facilitates maintaining high user retention & resource utilization by ensuring that each computing resource is correctly and properly distributed. This paper describes cloud-based load balancing systems. CC is virtualization of hardware like storage, computing, and security by virtual machines (VM). The live relocation of these machines provides many advantages, including high availability, hardware repair, fault tolerance, or workload balancing. In addition to various VM migration facilities, during the migration process, it is subject to significant security risks which the industry hesitates to accept. In this paper we have discussed CC besides this we also emphasize various existing load balancing algorithms, advantages& also we describe the PSO optimization technique

Hybrid load balance based on genetic algorithm in cloud environment

Load balancing is an efficient mechanism to distribute loads over cloud resources in a way that maximizes resource utilization and minimizes response time. Metaheuristic techniques are powerful techniques for solving the load balancing problems. However, these techniques suffer from efficiency degradation in large scale problems. This paper proposes three main contributions to solve this load balancing problem. First, it proposes a heterogeneous initialized load balancing (HILB) algorithm to perform a good task scheduling process that improves the makespan in the case of homogeneous or heterogeneous resources and provides a direction to reach optimal load deviation. Second, it proposes a hybrid load balance based on genetic algorithm (HLBGA) as a combination of HILB and genetic algorithm (GA). Third, a newly formulated fitness function that minimizes the load deviation is used for GA. The simulation of the proposed algorithm is implemented in the cases of homogeneous and heterogeneous resources in cloud resources. The simulation results show that the proposed hybrid algorithm outperforms other competitor algorithms in terms of makespan, resource utilization, and load deviation

A combined computing framework for load balancing in multi-tenant cloud eco-system

Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date:September 30th 2022

Relationships Among Dimensions of Information System Success and Benefits of Cloud

Despite the many benefits offered by cloud computing’s design architecture, there are many fundamental performance challenges for IT managers to manage cloud infrastructures to meet business expectations effectively. Grounded in the information systems success model, the purpose of this quantitative correlational study was to evaluate the relationships among the perception of information quality, perception of system quality, perception of service quality, perception of system use, perception of user satisfaction, and net benefits of cloud computing services. The participants (n = 137) were IT cloud services managers in the United States, who completed the DeLone and McLean ISS authors’ validated survey instrument. The multiple regression finding were signification, F(5, 131) = 85.16, p \u3c .001, R2 = 0.76. In the final model, perception of information quality (β = .188, t = 2.844, p \u3c .05), perception of service quality (β = .178, t = 2.102, p \u3c .05), and perception of user satisfaction (β = .379, t = 5.024, p \u3c .001) were statistically significant; perception of system quality and perception of system use were not statistically significant. A recommendation is for IT managers to implement comprehensive customer evaluation of the cloud service(s) to meet customer expectations and afford satisfaction. The implications for positive social change include decision-makers in healthcare, human services, social services, and other critical service organizations better understand the vital predictors of attitude toward system use and user satisfaction of customer-facing cloud-based applications