Load Balancing Based Task Scheduling with ACO in Cloud Computing

Gupta, Ashish; Garg, Ritu

doi:10.1109/comapp.2017.8079781

Cited by 69 publications

(26 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ants can find a high-quality solution in a search space and they share their knowledge in the form of pheromone update strategy and solve the problem efficiently. Further, ACO has been utilized in solving the load balancing problem that results in reduced computational time and has an efficient global search that does not fall into local optima [96]- [98], [106].…”

mentioning

confidence: 99%

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

View full text Add to dashboard Cite

Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q-learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.

show abstract

mentioning

confidence: 99%

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Gupta and Garg 8 present a mechanism called load balancing ant colony optimization (LB‐ACO). It aims to reduce the execution time while providing the load balancing.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of ACO‐based improved virtual machine allocation in cloud for IoT‐enabled healthcare

Kadarla

Sharma

2019

Concurrency and Computation

View full text Add to dashboard Cite

The Internet of Things (IoT)-enabled healthcare environment irregularly requires the resources from the Cloud to handle massive amounts of data, which impacts the response times of the Cloud. A typical healthcare application scenario could be to monitor the heart-patients who require the highest response times, immediate attention of all the stakeholders, and very quick decisions. This paper replaces the default First-Come-First-Serve (FCFS) Virtual Machine (VM) allocation scheme of the Cloud computing in CloudSim with the implementation of Ant Colony Optimization (ACO) by the efficient tuning of parameters. An IoT-enabled healthcare environment is designed, which irregularly requires the resources from the Cloud to handle massive amounts of data to assess the impact of the response times of the Cloud. Several experiments have been carried out to assess the optimal VM allocation using ACO by varying different parameters like the variation of the number of ants, the strength of pheromone, the number of VMs, the number of hosts, the strength of computing power of processing elements, the number of users, and the size of the workloads. Experimental results indicate that the ACO optimally utilizes the resources in a Cloud when compared to the default FCFS strategy. The results indicate that on an average, the ACO gives 25% to 30% improved response times than FCFS in the given healthcare and Cloud scenarios.

show abstract

“…It is the total amount of time required to complete a group of tasks which indicates the maximum completion time. It can be calculated by [15][16][17] Makespan =∑ (2) Makespan = Max (Comp_Time [i, j])…”

Section: Relatedworkmentioning

confidence: 99%

Cloud Computing Load Balancing using Genetic and Throttled Hybrid Algorithm

Eladl¹,

Ziedan²,

Gaafar³

2019

IJET

View full text Add to dashboard Cite

The demand for cloud computing resources is increasing due to its accessibility everywhere at any time. When the number of clients for cloud services increases, the load on the cloud nodes becomes high. This status requires load balancing to evenly distribute client requests among the available Virtual Machines (VMs) in a Data Center (DC). There are different standard dynamic load balancing techniques, such as Throttled and Active Monitoring. In this paper, a Genetic Algorithm (GA) is incorporated with a throttled to improve load balancing. The improvement is achieved by enhancing the overall response time, the data center processing time, and the maximum resource utilization. Simulation results show the improved performance of the proposed method compared to the ESCE and Throttled. Keyword-Cloud Computing, Load Balancing, Genetic Algorithm, Cloud Analyst I. INTRODUCTION Cloud computing technology has seen a speedy growth in recent years. It has affected the growth in several sub-technologies, like storage, distributed networks, virtualization, participation, and connectivity. In [1, 2], a cloud is considered as a distributed system that can handle diverse resource requirements by users. The rule system of a cloud-user relationship is planned by the Service Level Agreement (SLA), which is an agreement between a user and a service provider. As indicated in[3], the physical structure and repairing system can be achieved by the cloud, not the user. This automatically decreases the total cost and increases system efficiency. As indicated in [4], cloud computing gives an easy way to hold data and files, and it includes the following features: virtualization, distributed computing, and web services. Any complex task that calls for enormous computational resources can be serviced by cloud computing using distributed resources in a decentralized style [5]. Although cloud computing has many countenances, there are obstacles as load balancing over the resources and task scheduling [6]. Task scheduling in a cloud environment is a problem of specifying tasks to an appropriate machine to finish their work. A task should be done within a given period. The cloud task scheduler restores the information from the cloud service manager about the case of available resources [7]. Therefore, the scheduling of task problem can be qualified as the method of finding out a model mapping for execution of user tasks with the aim of reaching the desired goals [8]. Algorithms of task scheduling in cloud computing can be done depending on diverse objectives such as balancing the load, minimizing waiting time, and maximizing the utilization of resources and the throughput of the full system. Therefore, an efficient task scheduling algorithm aims to balance diverse and conflicting parameters together at the same time[9]. Moreover, resources are not utilized efficiently due to the rise in the load so for that reason load balancing is required [10]. Load balancing is the approach of redistributing the whole load into separate nodes to guarantee th...

show abstract

Load Balancing Based Task Scheduling with ACO in Cloud Computing

Cited by 69 publications

References 11 publications

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

Performance analysis of ACO‐based improved virtual machine allocation in cloud for IoT‐enabled healthcare

Cloud Computing Load Balancing using Genetic and Throttled Hybrid Algorithm

Contact Info

Product

Resources

About