scholarly journals Modeling And Performance Evaluation Of Mapreduce In Cloud Computing Systems Using Queueing Network Model

2018 ◽  
Author(s):  
Guzlan Miskeen
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Network Model ◽  
Queueing Network ◽  
Computing Systems ◽  
And Performance

Optimal Resource Usage in Multi-Cloud Computing Environment

2013 ◽  
Vol 3 (1) ◽  
pp. 44-57 ◽  
Author(s):  
Veena Goswami ◽  
Choudhury Nishkanta Sahoo
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Queueing Network ◽  
Computing Environment ◽  
New Paradigm ◽  
Cloud Computing Environment ◽  
Optimal Resource ◽  
Optimal Resource Management ◽  
And Performance ◽  
Multi Cloud

Cloud computing has emerged as a new paradigm for accessing distributed computing resources such as infrastructure, hardware platform, and software applications on-demand over the internet as services. This paper presents an optimal resource management framework for multi-cloud computing environment. The authors model the behavior and performance of applications to integrate different service-providers for end-to-end-requirements. Each service model caters to specific type of requirements and there are already number of players with own customized products/services offered. Intercloud Federation and Service delegation models are part of Multi-Cloud environment where the broader target is to achieve infinite pool of resources. They propose an analytical queueing network model to improve the efficiency of the system. Numerical results indicate that the proposed provisioning technique detects changes in arrival pattern, resource demands that occur over time and allocates multiple virtualized IT resources accordingly to achieve application Quality of Service targets.

scholarly journals Build and Evaluate a Free Virtual Cluster on Amazon Elastic Compute Cloud for Scientific Computing

2017 ◽  
Vol 13 (08) ◽  
pp. 121 ◽  
Author(s):  
Jie Xiong ◽  
Shen-Han Shi ◽  
Song Zhang
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Scientific Computing ◽  
Prototype System ◽  
Virtual Cluster ◽  
Huge Amount ◽  
Computing Systems ◽  
Comprehensive Performance ◽  
Amazon Ec2

Scientific computing requires a huge amount of computing resources, but not all the scientific researchers have an access to sufficient high-end computing systems. Currently, Amazon provides a free tier account for cloud computing which could be used to build a virtual cluster. In order to investigate whether it is suitable for scientific computing, we first describe how to build a free virtual cluster using StarCluster on Amazon Elastic Compute Cloud (EC2). Then, we perform a comprehensive performance evaluation of the virtual cluster built before. The results show that a free virtual cluster is easily built on Amazon EC2 and it is suitable for the basic scientific computing. It is especially valuable for scientific researchers, who do not have any HPC or cluster, to develop and test their prototype system of scientific computing without paying anything, and move it to a higher performance virtual cluster when necessary by choosing more powerful instance on Amazon EC2.

scholarly journals Dynamic Scalable Stochastic Petri Net: A Novel Model for Designing and Analysis of Resource Scheduling in Cloud Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Hua He ◽  
Shanchen Pang ◽  
Zenghua Zhao
Keyword(s):  
Cloud Computing ◽  
Petri Net ◽  
Performance Indicators ◽  
Large Scale ◽  
Computing Systems ◽  
Average Completion Time ◽  
Stochastic Petri Net ◽  
Fair Scheduling ◽  
And Performance ◽  
Improved Algorithm

Performance evaluation of cloud computing systems studies the relationships among system configuration, system load, and performance indicators. However, such evaluation is not feasible by dint of measurement methods or simulation methods, due to the properties of cloud computing, such as large scale, diversity, and dynamics. To overcome those challenges, we present a novel Dynamic Scalable Stochastic Petri Net (DSSPN) to model and analyze the performance of cloud computing systems. DSSPN can not only clearly depict system dynamic behaviors in an intuitive and efficient way but also easily discover performance deficiencies and bottlenecks of systems. In this study, we further elaborate some properties of DSSPN. In addition, we improve fair scheduling taking into consideration job diversity and resource heterogeneity. To validate the improved algorithm and the applicability of DSSPN, we conduct extensive experiments through Stochastic Petri Net Package (SPNP). The performance results show that the improved algorithm is better than fair scheduling in some key performance indicators, such as average throughput, response time, and average completion time.

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