Performance analysis of virtual machines and containers in cloud computing

2016 ◽  
Author(s):  
Rabindra K. Barik ◽  
Rakesh K. Lenka ◽  
K. Rahul Rao ◽  
Devam Ghose
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Virtual Machines

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 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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