Virtual Machine Classification-based Approach to Enhanced Workload Balancing for Cloud Computing Applications

Elrotub, Mousa; Gherbi, Abdelouahed

doi:10.1016/j.procs.2018.04.120

Cited by 19 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Elroub and Gherbi [20] discussed the classification technique for grouping of VMs and user tasks. The CPU and RAM utilization is considered for VM classification while the size of the user task and its associated information from log files are considered for task classification.…”

Section: Existing Workmentioning

confidence: 99%

Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud

Mulla¹,

Krishna²,

Tickoo³

2020

IJCNC

View full text Add to dashboard Cite

Cloud computing is a growing service computing trend that offers users a range of on-demand variety of services from applications, processing capability, and storage based on the concept of the "Pay-As-Per-Use" model. Organizations from every sector are now realizing the benefits offered by cloud computing technology and moving towards the cloud. Cloud computing offers numerous advantages over conventional computing. However, it still faces a few challenges such as resource utilization in a cloud data centre and quality of service to the end-users due to improper workload balance among available resources. Heterogeneous cloud resources also impact cloud systems overall performance. We proposed an enhanced load balancing algorithm in this research paper for efficient VM allocation in a heterogeneous cloud. Our proposed algorithm allocates independent user tasks or requests to available virtual machines in cloud datacentre efficiently to manage proper load balancing. This algorithm is aimed at minimizing user request response time and the time required for data centre processing. The results obtained showed a significant reduction in user request response time and data centre processing time as compared to "Throttled" and "Round Robin (RR)" algorithms.

show abstract

Section: Existing Workmentioning

confidence: 99%

Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud

Mulla¹,

Krishna²,

Tickoo³

2020

IJCNC

View full text Add to dashboard Cite

show abstract

“…Therefore, the support vector regression SVR model could be simplified to formula (2), in which parameter C played a balancing role in the model complexity and training error.…”

Section: Support Vector Regression Load Prediction Based On Genetic Amentioning

confidence: 99%

“…The approach arranged virtual machines in groups, and several tasks shared the same VM resources. The goal of our proposal was to allow more dynamic resources and to improve the QoS requirements by maximizing the usage of the resources and user satisfaction, such as increasing resource utilization and reducing the number of job rejections [2].…”

Section: Introductionmentioning

confidence: 99%

Virtual machine scheduling strategy based on machine learning algorithms for load balancing

Xin

Liú

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

With the rapid increase of user access, load balancing in cloud data center has become an important factor affecting cluster stability. From the point of view of green scheduling, this paper proposed a virtual machine intelligent scheduling strategy based on machine learning algorithm to achieve load balancing of cloud data center. Firstly, a load forecasting algorithm based on genetic algorithm (SVR_GA), k-means clustering algorithm based on optimized min-max, and adaptive differential evolution algorithm (ESA_DE) to enhance local search ability are proposed to solve the load imbalance problem in cloud data center. The experimental results showed that compared with other classical algorithms, the proposed virtual machine scheduling strategy reduces the number of virtual machine migration by 94.5% and the energy consumption of cloud data center by 49.13%.

show abstract

“…The examples of IaaS are Amazon EC2 (Elastic Cloud Computing) and S3 (Simple Storage Service). Elrotub et al proposed a method to allocate task to VMs and achieve high level of Quality of Services [3]. The algorithm uses CPU, memory and RAM utilization as parameters.…”

Section: Infrastructure As a Servicementioning

confidence: 99%

Reduced Energy Consumption in Cloud Computing Environment

Apeksha¹,

Revar²

2019

IJCA

View full text Add to dashboard Cite

Development in Information Technology led to the increasing necessity of computing and storage. Cloud services is one of the technologies with huge demand and hence involves more computing resources and storage. Consequently, the energy consumption by the cloud is also increasing. Cloud data centers consume large amount of energy and there by discharging carbon dioxide to the atmosphere. Dynamic efforts are put in to this research to minimize the energy consumption of data centers. This work recommends a technique for energy efficient resource management. Prior techniques do not emphasis on the variations of workloads and deficient in probing the effect of algorithms on performance. Virtual machine configuration also plays a crucial role for reducing energy consumption and resource wastage but is not given much importance. To address these weaknesses, this work recommends a novel method to map groups of tasks to customized virtual machine types. Virtual machine migration is done to stabilize the load by manipulating the load using MIPS, RAM and Bandwidth.

show abstract

Virtual Machine Classification-based Approach to Enhanced Workload Balancing for Cloud Computing Applications

Cited by 19 publications

References 4 publications

Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud

Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud

Virtual machine scheduling strategy based on machine learning algorithms for load balancing

Reduced Energy Consumption in Cloud Computing Environment

Contact Info

Product

Resources

About