Efficient Task Scheduling using Load Balancing in Cloud Computing

Kaur, Rupinder; Dhindsa, Dr.Kanwalvir Singh

doi:10.35444/ijana.2018.10037

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…The suggested model implements load balancing at the fog layer to improve resource utilization. Kaur and Dhindsa [47] discussed several strategies and techniques for managing parallel jobs and services to improve CPU utilization in the cloud infrastructure. They proposed the usage of GWO and PSO for efficient distribution of workload.…”

Section: Related Workmentioning

confidence: 99%

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

et al. 2023

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Cloud Computing is the dynamic provisioning of resources to provide services to end-users over the internet. The realization of cloud computing requires addressing several challenges, such as resource discovery, security, scheduling, and load balancing. Among these research issues, load balancing is the most challenging one. Therefore, in the past few years, research into various static and dynamic algorithms to achieve optimal results is gaining importance. This research proposes Swarm Intelligence (SI) as a loadbalancing solution for cloud computing. Several alternatives in the literature (like genetic algorithm, ACO, PSO, BAT, GWO, and many others) are investigated, but none consider the load balancing convergence time with global optimization. Among these algorithms, this research emphasizes Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). A combined approach of GWO-PSO that capitalizes on the benefits of fast convergence and global optimization is proposed in this paper. These two techniques enhance system efficiency and resource allocation, working together to solve the load-balancing challenge. Compared to other traditional approaches, the findings of this research are promising while achieving globally optimized fast convergence and reducing overall response time. On average, the overall response time of the proposed technique is reduced to 12% as compared to other algorithms. Furthermore, the best optimal value obtained from the objective function of the proposed GWO-PSO algorithm improves PSO to 97.253% in terms of convergence.

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Section: Related Workmentioning

confidence: 99%

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

et al. 2023

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“…The suggested algorithm's purpose is to map the workflow's tasks to the VMs in such a way that the overall workflow makespan is minimized while simultaneously balancing the allocated load across each VM. Henrique Yoshikazu Shishido et al [15] investigate the influence of both GA and PSO on workflow scheduling attempts. To test the metaheuristics' performance, a cost-and security-aware workflow scheduling algorithm was chosen.…”

Section: About Here]mentioning

confidence: 99%

Workflow Task Scheduling Hybrid swarm intelligence in cloud Computing

Ghafir

Alam

Siddiqui

et al. 2022

Preprint

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Users may access virtual, scalable, and dynamic resources using cloud computing, which is a novel technology that charges them only for the resources they use. This technology is reliant on a network to function. Several services, like as e-commerce apps, use the whole network as a result of the sheer scope of the network. Cloud computing has progressed through three phases in terms of practical development: distributed computing, parallel computing, and grid computing. Cloud computing is a cutting-edge technology that instals networks of computers, many of which are situated in faraway locations, to conduct operations on massive amounts of data in real time. Cloud computing makes use of a workflow model to describe various scientific and web-based applications in the cloud. On the heterogeneous cloud environment, one of the most difficult problems to solve is the scheduling of massive workflows of jobs that adhere to scientific criteria. Other challenges are unique to public cloud computing, such as security and privacy. For example, customers must be happy with quality of service (QoS) metrics such as scalability and dependability, as well as the requirement to maximise end-user resource consumption rates, among other things. This study examines scheduling algorithms that are based on the Water cycle optimization principle. Specifically, it is intended to aid users in determining the most appropriate quality of service consideration for big workflows in infrastructure as a service Shabina Ghafir et al. (IaaS) cloud applications and mapping tasks to available resources. In addition, the scheduling methods are classified based on the variation of the WCA algorithm that is used to implement them. For the purpose of comparing and identifying the potency of the suggested system, the current round-robin (RR), ALO, and PSO approaches have been selected. The results showed that the suggested approach reduces the cost by 9.8 percent for GA-PSO, 10 percent for PSO, 20 percent for ALO, 30 percent for RR, and 12 percent for GA by using a combination of these techniques. The suggested solution decreases load balancing and makespan by 8 percent compared to GA-PSO, 10 percent compared to ALO, 20 percent compared to PSO, 35 percent compared to RR, and 45 percent compared to GA. Furthermore, the performance of energy consumption and reliability are both satisfactory.

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“…Nature-inspired BAT algorithm has been proposed in [15] which reduces the idle time of the virtual machines and thus improving their efficiency. A combination of Particle Swarm Optimization and Grey Wolf Optimization (PSO_GWO) has been compared with the BAT algorithm in [16] with the proposed PSO_GWO algorithm resulting in reduced total execution time and total execution cost. PSO strives for local optimization while GWO optimizes locally.…”

Section: Related Workmentioning

confidence: 99%

Load Balancing in Cloud Computing Using Hungarian Algorithm

Bala¹,

Chishti²

2019

IJWMT

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Cloud computing is a highly popular computing paradigm providing on-demand resources with high reliability and availability. The user requests are fulfilled by providing a virtual machine with the requested configuration. However, with the ever-increasing load on the cloud resources, the need for optimal resource utilization of the cloud resources has become the need of the hour. Load balancing has been identified as one of the possible ways to improve resource utilization in the cloud and the current state-of-the-art algorithms indicate the numerous attempts made to find the approximate solution for this NP-hard problem. In this work, we have focused on evaluating the efficiency of the Hungarian algorithm for load distribution in the cloud and compared its performance with First-come-first-serve (FCFS). The simulations were carried out in CloudSim and show remarkable improvement in various performance parameters. Finish time of a given task schedule was reduced by 41% and average execution time was reduced by 13% in the Hungarian algorithm when compared with FCFS. The simulations were carried out under different workload conditions to validate our results.

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Efficient Task Scheduling using Load Balancing in Cloud Computing

Cited by 9 publications

References 11 publications

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing

Workflow Task Scheduling Hybrid swarm intelligence in cloud Computing

Load Balancing in Cloud Computing Using Hungarian Algorithm

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