scholarly journals Performance Analysis of Virtual Machine in Cloud Architecture

2021 ◽  
Vol 23 (07) ◽  
pp. 924-929
Author(s):  
Dr. Kiran V ◽  
◽  
Akshay Narayan Pai ◽  
Gautham S ◽  
◽  
...  
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Web Application ◽  
Virtual Machines ◽  
Storage Capacity ◽  
Processing Data ◽  
Host Machine ◽  
Essential Components ◽  
Software Code ◽  
Ease Of Access

Cloud computing is a technique for storing and processing data that makes use of a network of remote servers. Cloud computing is gaining popularity due to its vast storage capacity, ease of access, and diverse variety of services. When cloud computing advanced and technologies such as virtual machines appeared, virtualization entered the scene. When customers’ computing demands for storage and servers increased, however, virtual machines were unable to match those expectations due to scalability and resource allocation limits. As a consequence, containerization became a reality. Containerization is the process of packaging software code along with all of its essential components, including frameworks, libraries, and other dependencies, such that they may be separated or separated in their own container. The program operating in containers may execute reliably in any environment or infrastructure. Containers provide OS-level virtualization, which reduces the computational load on the host machine and enables programs to run much faster and more reliably. Performance analysis is very important in comparing the throughput of both VM-based and Container-based designs. To analyze it same web application is running in both the designs. CPU usage and RAM usage in both designs were compared. Results obtained are tabulated and a Proper conclusion has been given.

scholarly journals Deployment of Applications Using Nginx Ingress Controller

2021 ◽  
Vol 23 (07) ◽  
pp. 352-357
Author(s):  
Gautham S ◽  
◽  
Maddula Abhijit ◽  
Prof. Sahana. B ◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Traffic Management ◽  
Virtual Machines ◽  
Storage Capacity ◽  
Production Environment ◽  
Wide Range ◽  
Software Code ◽  
Ease Of Access

Cloud computing is a method of storing and manipulating data by utilizing a network of remote servers. Cloud computing is becoming increasingly popular owing to its large storage capacity, ease of access, and wide range of services. Virtualization entered the picture when cloud computing progressed, and technologies or software such as virtual machines emerged. However, when customers’ computational needs for storage and servers rose, virtual machines were unable to meet those expectations owing to scalability and resource allocation limitations. As a result, containerization came into the picture. Containerization refers to the packaging of software code together with all of its necessary elements such as frameworks, libraries, and other dependencies such that they are isolated or segregated in their own container. Kubernetes used as an orchestration tool implements an ingress controller to route external traffic to deployments running on pods via ingress resource. This enables effective traffic management among the running applications avoiding unwanted blackouts in the production environment.

scholarly journals Efficient Resources Allocation and Energy Reduction with Virtual Machines for Cloud Computing

2021 ◽  
Vol 11 (2) ◽  
pp. 52-58
Author(s):  
Anand Mehta ◽  
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Support Vector ◽  
Virtual Machine Migration ◽  
Machine Learning Methods ◽  
Host Machine ◽  
Application Deployment ◽  
Consumption Energy ◽  
Best Fit

Cloud computing is an internet provisioned method for sharing the resources on demand by network management, storage, services, applications and the serves that necessitate management optimal effort. VMM (virtual machine migration) plays a major role in enhancing the resource utilization, application isolation, processing nodes, fault tolerance in VMs for enhancing nodes portability and for maximizing the efficiency of physical server. For balancing the clouds with resources for the enhanced performance, varied users are served with application deployment in the cloud environment is considered as the major task. The user can rent or request the resources when it becomes significant. The emphasis of this paper is on different energy VM energy efficient module as per machine learning methods. While allocating the VMs to the host machines, MBFD (Modified Best Fit Decreasing) is considered and the classification of host machine capability such as overloaded, normal loaded and underloaded is executed according to SVM (Support vector machine). SVM is utilized as a classifier for analyzing the MBFD algorithm and for the classification of the host as per the job properties. In this procedure, the numbers of jobs that are not allocated are examined via simulation which is computed by means of time consumption, energy consumption and a total number of migrations.

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 Efficient Virtual Machine Memory Transfer in Datacenter with optimal downtime

2019 ◽  
Author(s):  
Girish L
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Flow Chart ◽  
Key Factors ◽  
Virtual Machine Migration ◽  
Memory Transfer ◽  
Vm Migration ◽  
Host Machine ◽  
Machine Memory

Cloud computing is a technology which relies onsharing various computing resources instead of having localservers to handle applications. Cloud computing is driven byvirtualization technology. Virtual machines need migration fromone host to anther due to the presence of error or over loading orslowness in the current running host machine. Live Virtualmachine migration is the transfer of running virtual machinefrom one host to another without stopping the current runningtask. During this live virtual machine migration Downtime is oneof the key factors that have to be considered and assessed.Here we present detailed survey on what are the importance oflive virtual machine migration in cloud computing technologyand various techniques to reduce the downtime during livevirtual machine migration. The flow chart showing the steps usedin Pre copy approach for VM migration. And also we presentthe result of the comparison between the two virtual machinemigration environments, VMWare and Xen Server.

scholarly journals Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud

2021 ◽  
Vol 11 (16) ◽  
pp. 7379
Author(s):  
Oleg Bystrov ◽  
Ruslan Pacevič ◽  
Arnas Kačeniauskas
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Virtual Machines ◽  
Cloud Infrastructure ◽  
Parallel Processes ◽  
Load Imbalance ◽  
Software Execution ◽  
Performance Challenges ◽  
Infrastructure Services ◽  
The Impact

The pervasive use of cloud computing has led to many concerns, such as performance challenges in communication- and computation-intensive services on virtual cloud resources. Most evaluations of the infrastructural overhead are based on standard benchmarks. Therefore, the impact of communication issues and infrastructure services on the performance of parallel MPI-based computations remains unclear. This paper presents the performance analysis of communication- and computation-intensive software based on the discrete element method, which is deployed as a service (SaaS) on the OpenStack cloud. The performance measured on KVM-based virtual machines and Docker containers of the OpenStack cloud is compared with that obtained by using native hardware. The improved mapping of computations to multicore resources reduced the internode MPI communication by 34.4% and increased the parallel efficiency from 0.67 to 0.78, which shows the importance of communication issues. Increasing the number of parallel processes, the overhead of the cloud infrastructure increased to 13.7% and 11.2% of the software execution time on native hardware in the case of the Docker containers and KVM-based virtual machines of the OpenStack cloud, respectively. The observed overhead was mainly caused by OpenStack service processes that increased the load imbalance of parallel MPI-based SaaS.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

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