Performance And Reliability Analysis Of Computer Systems (an Example-based Approach Using The Sharpe Software

Sahner, Robin; Trivedi, Kishor S.; Puliafito, Antonio

doi:10.1109/tr.1997.664017

Cited by 260 publications

(244 citation statements)

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“…Besides if we associate a probability to each edge, an ADG can be modelled by a Discrete Time Markov Chain (Sahner et al, 1997). Given n nodes in the ADG, the initial probability distribution on each node (that a particular node is compromised) is π(0) = (1, 0, 0, .…”

Section: Besides the Possibility Of Transition Is Determined By Equamentioning

confidence: 99%

Improving Cloud Survivability through Dependency based Virtual Machine Placement

Min

Bai²,

Zang

et al. 2012

Proceedings of the International Conference on Security and Cryptography

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Abstract:Cloud computing is becoming more and more popular in computing infrastructure and it also introduces new security problems. For example, a physical server shared by many virtual machines can be taken over by an attacker if the virtual machine monitor is compromised through one of the virtual machines. Thus, collocating with vulnerable virtual machines, or "bad neighbours", on the same physical server introduces additional security risks. Moreover, the connections between virtual machines, such as the network connection between a web server and its back end database server, are natural paths of attacks. Therefore, both virtual machine placement and connections among virtual machines in the cloud have great impact over the overall security of cloud. In this paper, we quantify the security risks of cloud environments based on virtual machine vulnerabilities and placement schemes. Based on our security evaluation, we develop techniques to generate virtual machine placement that can minimize the security risks considering the connections among virtual machines. According to the experimental results, our approach can greatly improve the survivability of most virtual machines and the whole cloud. The computing costs and deployment costs of our techniques are also practical.

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Section: Besides the Possibility Of Transition Is Determined By Equamentioning

confidence: 99%

Improving Cloud Survivability through Dependency based Virtual Machine Placement

Min

Bai²,

Zang

et al. 2012

Proceedings of the International Conference on Security and Cryptography

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“…Among the tools which are still available and in use are SHARPE, PDQ and JMT. The Symbolic Hierarchical Automated Reliability and Performance Evaluator (SHARPE) [9] is an integrated package for describing and analyzing hierarchical stochastic models, including QN, fault trees, reliability models and so on. Pretty Damn Quick (PDQ) 2 is a QN package with bindings for multiple languages (including Java, PHP, Perl, Python and C).…”

Section: Related Workmentioning

confidence: 99%

The qnetworks Toolbox: A Software Package for Queueing Networks Analysis

Marzolla

2010

Analytical and Stochastic Modeling Techniques and Applications

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Abstract. Queueing Networks (QNs) are a useful performance modelling notation. They can be used to describe many kinds of systems, and efficient solution techniques have been developed for some classes of QN models. Despite the fact that QNs have been extensively studied, very few software packages for QN analysis are available today. In this paper we describe the qnetworks toolbox, a free software package for QN analysis for GNU Octave. qnetworks provides implementations of solution algorithms for single station queueing systems as well as for product and some non product form QN models. Exact, approximate and bound analysis can be performed. Additional utility functions and algorithms for Markov Chains analysis are also included. The qnetworks package is available as free and open source software, allowing users to study, modify and extend the code. This makes qnetworks a viable teaching tool.

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“…The Symbolic Hierarchical Automated Reliability and Performance Evaluator (SHARPE) that solves stochastic models was selected as our subject [36]. This application was first developed in 1986 for three user groups: practicing engineers, researchers in performance and reliability modeling, and science and engineering students.…”

Section: Overview Of Sharpementioning

confidence: 99%

“…For each one of these measures we hypothesize the coverage growth to be lognormal, due to the theoretical foundations underpinning the lognormal software reliability growth model [26]. To validate this hypothesis we generate empirical coverage data from extensive testing of a software application named SHARPE [36], which has approximately 30 KLOC. We then fit three functions to this empirical data, namely, exponential, lognormal and log-Poisson and compare the quality of the fits using Akaike Information Criteria (AIC) [2] and log likelihood (LLH) [15] [31].…”

Section: Introductionmentioning

confidence: 99%

The marginal value of increased testing: an empirical analysis using four code coverage measures

Gokhale¹,

Mullen²

2006

J. Braz. Comp. Soc.

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Performance And Reliability Analysis Of Computer Systems (an Example-based Approach Using The Sharpe Software

Cited by 260 publications

References 0 publications

Improving Cloud Survivability through Dependency based Virtual Machine Placement

Improving Cloud Survivability through Dependency based Virtual Machine Placement

The qnetworks Toolbox: A Software Package for Queueing Networks Analysis

The marginal value of increased testing: an empirical analysis using four code coverage measures

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