Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment

2020 ◽  
Vol 17 (6) ◽  
pp. 2430-2434
Author(s):  
R. S. Rajput ◽  
Dinesh Goyal ◽  
Rashid Hussain ◽  
Pratham Singh
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Auto Scaling ◽  
Adequate Resource ◽  
Cloud Resources

The cloud computing environment is accomplishing cloud workload by distributing between several nodes or shift to the higher resource so that no computing resource will be overloaded. However, several techniques are used for the management of computing workload in the cloud environment, but still, it is an exciting domain of investigation and research. Control of the workload and scaling of cloud resources are some essential aspects of the cloud computing environment. A well-organized load balancing plan ensures adequate resource utilization. The auto-scaling is a technique to include or terminate additional computing resources based on the scaling policies without involving humans efforts. In the present paper, we developed a method for optimal use of cloud resources by the implementation of a modified auto-scaling feature. We also incorporated an auto-scaling controller for the optimal use of cloud resources.

Sharing VM Resources With Using Prediction of Future User Requests for an Efficient Load Balancing in Cloud Computing Environment

2021 ◽  
Vol 13 (2) ◽  
pp. 37-64
Author(s):  
Mousa Elrotub ◽  
Ahmed Bali ◽  
Abdelouahed Gherbi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Performance Metrics ◽  
Virtual Machines ◽  
Machine Learning Techniques ◽  
Data Repository ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Learning Techniques

The problem of balancing user requests in cloud computing is becoming more serious due to the variation of workloads. Load balancing and allocation processes still need more optimizing methodologies and models to improve performance and increase the quality of service. This article describes a solution to balance user workload efficiently by proposing a model that allows each virtual machine (VM) to maximize the serving number of requests based on its capacity. The model measures VMs' capacity as a percentage and maps groups of user requests to appropriate active virtual machines. Finding the expected patterns from a big data repository, such as log data, and using some machine learning techniques can make the prediction more efficiently. The work is implemented and evaluated using some performance metrics, and the results are compared with other research. The evaluation shows the efficiency of the proposed approach in distributing user workload and improving results.

scholarly journals Self - Adaptive Load Balancing Using Live Migration of Virtual Machines in Cloud Environment

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 735-745
Author(s):  
V. Lavanya ◽  
M. Saravanan ◽  
E.P. Sudhakar
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Live Migration ◽  
Management Activity ◽  
Cloud Computing Environment ◽  
Efficient Resource ◽  
Server Overload ◽  
Self Adaptive

In this paper, a self-adaptive load balancing technique is proposed using live migration of heterogeneous virtual machines (VM) in a Hyper-V based cloud environment. A cloud supported plugin as a management activity within the infrastructure as a service strategy. It is proposed to assist the load balancing process in such a way so that all hypervisors are almost equally loaded once the overload status gets triggered. In the cloud computing environment, load balancing plays a major role if the large number of events triggered has a high impact on the performance of the system. The efficiency of cloud computing is based on the efficient load balancing having a self-adjustable technique using live migration of VMs across clusters of nodes. The proposed load balancing model is efficient in performance improvement by efficient resource utilization and also it helps to avoid the situation occurrence of server hanging by the cause of server overload within the infrastructure of multiple Microsoft Hyper-V hypervisors environment.

scholarly journals Data Service Trust Measurement in Cloud Environment

2019 ◽  
Author(s):  
Lin Shi ◽  
Zilong Wang ◽  
Ning Chen ◽  
Jie Chen
Keyword(s):  
Cloud Computing ◽  
Web Application ◽  
Virtual Machines ◽  
Management Service ◽  
Cloud Environment ◽  
Computing Environment ◽  
Optimization Scheme ◽  
Processing Scheme ◽  
Cloud Computing Environment ◽  
Cloud Data

Abstract Highly trusted issues will be one of the main obstacles to a new era of highly trusted cloud computing. In the cloud computing environment, because sensitive applications and user data are put into the cloud, they run in virtual machines in the data center. Among them, due to the existence of access vulnerability, virtualization vulnerability, web application vulnerability, etc., high trust issues arise from data control, identity authentication, lack of information and other related issues. The introduction of trust mechanisms can be very facilitate the solution of related issues, achieve highly trusted quantification, analysis, and modeling of cloud data centers, meet high trust requirements, and provide users with a highly trusted cloud computing environment. This article mainly studies the trust measure of data services in cloud environment. In this paper, the optimization scheme is verified through experiments, and the traditional big data processing scheme, the original Sahara and the optimization scheme are compared in six cases. Overall, the optimization scheme has a significant performance improvement. Compared with the default configuration of Sahara, the configuration of the new interface has increased the throughput in DFSIO by 120%. Using the design of the unified cache management service, Tachyon can reach 13 in specific situations. In the execution time of Sort workloads, the optimization scheme generally decreased by about 50% compared to the original Sahara, and the memory utilization increased from 80% to 96% in our experiments, but in the cache isolation and other areas need to be improved. The results are basically in line with expectations, which also confirms the rational thinking and value of this article on BDAaS performance research.

scholarly journals Comparing load balancing algorithms for web application in cloud environment

2020 ◽  
Vol 17 (2) ◽  
pp. 1104
Author(s):  
Zakaria Benlalia ◽  
Karim Abouelmehdi ◽  
Abderrahim Beni-hssane ◽  
Abdellah Ezzati
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Web Application ◽  
Cloud Environment ◽  
Computing Environment ◽  
New Paradigm ◽  
Cloud Computing Environment ◽  
On Demand ◽  
Software And Hardware

<p>Cloud computing has emerged as a new paradigm for providing on-demand computing resources and outsourcing software and hardware infrastructures. Load balancing is one of the major concerns in cloud computing environment means how to distribute load efficiently among all the nodes. For solving such a problem, we need some load balancing algorithms, so in this paper we will compare the existing algorithms for web application.and based on results obtained we choose the best among them.</p>

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