A Nonparametric Multivariate Method for Performance Analysis of Virtual Machines in Cloud Computing Systems

2014 ◽  
Author(s):  
Chang Liu ◽  
Ming Yu ◽  
Yu Zhang
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Virtual Machines ◽  
Computing Systems ◽  
Multivariate Method

A simplified reliability analysis method for cloud computing systems considering common-cause failures

2017 ◽  
Vol 231 (3) ◽  
pp. 324-333 ◽  
Author(s):  
Ruiying Li ◽  
Qiong Li ◽  
Ning Huang ◽  
Rui Kang
Keyword(s):  
Cloud Computing ◽  
State Space ◽  
Reliability Analysis ◽  
Virtual Machines ◽  
Single Server ◽  
Analysis Method ◽  
Computing Systems ◽  
Common Cause ◽  
Cloud Computing System ◽  
Common Cause Failures

Virtualization is one of the main features of cloud computing systems, which enables building multiple virtual machines on a single server. However, this feature brings new challenge in reliability modeling, as the failure of the server will make all its co-located virtual machines inoperable, which is a typical common-cause failure. To satisfy the demand of the cloud computing system, the reliability of the system is defined as the probability that at least a given number of virtual machines are operable. State-space enumeration is one method to calculate such reliability; however, due to the large number of combinations, it is time-consuming and impractical. To solve this problem, we propose a simplified reliability analysis method based on fault tree and state-space models. Two illustrative examples are studied to show the process and the effectiveness of our method. State enumeration and Monte Carlo simulation are also used to prove the correctness of our method as back-to-back verifications. Compared to the reliability analysis without considering common-cause failures, our results are quite different, which illustrates the necessity of considering common-cause failures in the reliability of cloud computing systems.

scholarly journals Heuristic-based Approach for Dynamic Consolidation of Software Licenses in Cloud Data Centers

2021 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
Leila Helali ◽  
◽  
Mohamed Nazih Omri
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Energy Aware ◽  
Computing Systems ◽  
Cloud Data ◽  
Software Licenses ◽  
Cloud Data Centers ◽  
Distributed Cloud

Since its emergence, cloud computing has continued to evolve thanks to its ability to present computing as consumable services paid by use, and the possibilities of resource scaling that it offers according to client’s needs. Models and appropriate schemes for resource scaling through consolidation service have been considerably investigated,mainly, at the infrastructure level to optimize costs and energy consumption. Consolidation efforts at the SaaS level remain very restrained mostly when proprietary software are in hand. In order to fill this gap and provide software licenses elastically regarding the economic and energy-aware considerations in the context of distributed cloud computing systems, this work deals with dynamic software consolidation in commercial cloud data centers 𝑫𝑺𝟑𝑪. Our solution is based on heuristic algorithms and allows reallocating software licenses at runtime by determining the optimal amount of resources required for their execution and freed unused machines. Simulation results showed the efficiency of our solution in terms of energy by 68.85% savings and costs by 80.01% savings. It allowed to free up to 75% physical machines and 76.5% virtual machines and proved its scalability in terms of average execution time while varying the number of software and the number of licenses alternately.

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.

Sign in / Sign up

Export Citation Format

Share Document