Adaptive Distributed Load Balancing Algorithm Based on Live Migration of Virtual Machines in Cloud

Zhao, Yi; Huang, Wenlong

doi:10.1109/ncm.2009.350

Cited by 99 publications

(63 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Paul and Sanyal, (2011) used credit based scheduling decision for evaluating group of task in the task queue and find the minimal completion time of all task. Zhao and Huang (2009) reduced the migration time of virtual machines through shared storage and fulfilling the zero-downtime relocation of virtual machines by transforming them as Red Hat cluster services. Li et al (2011) proposed a hybrid energy-efficient scheduling algorithm using dynamic migration that not only reduces response time but also conserves energy besides achieving load balancing.…”

Section: Related Workmentioning

confidence: 99%

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Tiwari¹,

Singh²,

Sharma³

2018

Journal of Computer Science

View full text Add to dashboard Cite

Abstract:As the cloud computing technology is gaining popularity with time, more and more users and applications are shifting towards it. This is why clouds are experiencing high load, which demands for load balancing of user tasks submitted to cloud for execution. This makes load balancing of non-preemptive tasks a key issue in cloud computing. Superior task scheduling leads to balanced loads among cloud nodes, which results in faster execution of tasks. Task scheduling in cloud environment is an instance of NP-hard optimization problem. When few nodes in a cloud are overloaded whereas other nodes are under loaded then in such situation the performance of overloaded VMs is diminished. It demands a task scheduling so that the incoming tasks can be distributed uniformly across virtual machines (VMs) for proper utilization of available resources. In this study, we propose a novel load balancing algorithm named Viral System Based Load Balancing (VSB-LB) algorithm, which is based on bio-inspired viral system algorithm that distributes the tasks uniformly among VMs. The proposed algorithm is compared with basic load balancing algorithms such as First Come First Serve (FCFS), Weighted Round Robin (WRR) as well as newer bio-inspired Load balance Aware Genetic Algorithm (LAGA) to show its effectiveness. Simulation results proved that VSBLB outperforms FCFS and WRR and LAGA for performing load balancing.

show abstract

Section: Related Workmentioning

confidence: 99%

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Tiwari¹,

Singh²,

Sharma³

2018

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…This algorithm [35] uses the concept of compare and balance to reach the in equilibrium situation and manages balancing of load of systems. A comparison based on the sorting approach is applied.…”

Section: Compare and Balancementioning

confidence: 99%

Cloud Load Balancing: A Perspective Study

Pandey¹

2017

ijecs

View full text Add to dashboard Cite

Cloud computing has boomed its horizon with large pace as commercial infrastructure in the IT industries meeting the vast requirements of computing resources. There are several issues such as load balancing, virtual machine migration, automated service provisioning, algorithm complexity, etc., demanding to be resolved. Each of these issues needs load balancing to be resolved. This aims for distributing the unwanted dynamic workload between the nodes residing in cloud and this desires of every computing resource must be assigned on proficient and reasonable ground.Load balancing has become crucial for efficient performance in distributed environments. Cloud computing is an emerging technology demanding more services and better results. Thus load balancing for the cloud is very interesting and important research area. Many algorithms are proposed to provide efficient techniques for assigning the client's requests to available cloud nodes.This paper studies cloud computing along with research challenges in load balancing. Load balancing has been a major issue for cloud computing environment. Efficient load balancing scheme ensures efficient resource utilization by providing the resources to cloud on-demand of users' basis. By implementing appropriate scheduling criteria load balancing may prioritize users. The aim of this study is to peep in various load balancing algorithms to address its challenges in variety of cloud environment. This study provides a perspective view of the latest approaches in load balancing that will certainly help the future researchers in this field

show abstract

“…Compare_and_balance [21] ,proposed a distributed load balancing mechanism supported by VM live migration. The VM with the highest combination of CPU and I/O usages was selected for migration.…”

Section: Algorithms and Techniques For Live Migration And Their Key Omentioning

confidence: 99%

Survey Study of Virtual Machine Migration Techniques in Cloud Computing

Alhammadi¹,

Ghaleb²,

Hamaid³

et al. 2017

IJCA

View full text Add to dashboard Cite

Migration of virtual machine is one of the most important features in virtual machine technology .It involves moving a VM from a physical host to another, facilitating balancing load, utilization of resource and reducing energy consumption. The goal of this paper is to present a survey of the main types of virtual machine live migration, their classifications and a comparative study of these techniques in a particular class. It also discusses the algorithms used in virtual machine migration.

show abstract

Adaptive Distributed Load Balancing Algorithm Based on Live Migration of Virtual Machines in Cloud

Cited by 99 publications

References 8 publications

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Cloud Load Balancing: A Perspective Study

Survey Study of Virtual Machine Migration Techniques in Cloud Computing

Contact Info

Product

Resources

About