Results 1  10
of
29
Modelbased evaluation: From dependability to security
 IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
, 2004
"... The development of techniques for quantitative, modelbased evaluation of computer system dependability has a long and rich history. A wide array of modelbased evaluation techniques are now available, ranging from combinatorial methods, which are useful for quick, roughcut analyses, to statebased ..."
Abstract

Cited by 56 (2 self)
 Add to MetaCart
The development of techniques for quantitative, modelbased evaluation of computer system dependability has a long and rich history. A wide array of modelbased evaluation techniques are now available, ranging from combinatorial methods, which are useful for quick, roughcut analyses, to statebased methods, such as Markov reward models, and detailed, discreteevent simulation. The use of quantitative techniques for security evaluation is much less common, and has typically taken the form of formal analysis of small parts of an overall design, or experimental red teambased approaches. Alone, neither of these approaches is fully satisfactory, and we argue that there is much to be gained through the development of a sound modelbased methodology for quantifying the security one can expect from a particular design. In this work, we survey existing modelbased techniques for evaluating system dependability, and summarize how they are now being extended to evaluate system security. We find that many techniques from dependability evaluation can be applied in the security domain, but that significant challenges remain, largely due to fundamental differences between the accidental nature of the faults commonly assumed in dependability evaluation, and the intentional, human nature of cyber attacks.
A Unified Approach for Specifying Measures of Performance, Dependability, and Performability
, 1991
"... Methods for evaluating system performance, dependability, and performability are becoming increasingly more important, particularly in the case of critical applications. Central to the evaluation process is the definition of specific measures of system behavior that are of interest to a user. This p ..."
Abstract

Cited by 54 (7 self)
 Add to MetaCart
Methods for evaluating system performance, dependability, and performability are becoming increasingly more important, particularly in the case of critical applications. Central to the evaluation process is the definition of specific measures of system behavior that are of interest to a user. This paper presents a unified approach to the specification of measures of performance, dependability, and performability. The unification is achieved by 1) using a model class well suited for representation of all three aspects of system behavior, and 2) system behavior. The resulting approach permits the specification of many nontraditional as well as traditional measures of system performance, dependability, and performability in a unified manner. Example instantiations of variables within this class are given and their relationships to variables used in traditional performance and dependability evaluations are illustrated.
Reward Model Solution Methods With Impulse And Rate Rewards: An Algorithm And Numerical Results
, 1994
"... This thesis has been submitted in partial fulfillment of requirements for an advanced ..."
Abstract

Cited by 25 (6 self)
 Add to MetaCart
This thesis has been submitted in partial fulfillment of requirements for an advanced
Stiffnesstolerant methods for transient analysis of stiff Markov chains
 Microelectronics and Reliability
, 1994
"... AbstractThree methods for numerical transient analysis of Markov chains, the modified Jensen's method (Jensen's method with steadystate detection of the underlying DTMC and computation of Poisson probabilities using the method of Fox and Glynn [1]), a thirdorder Lstable implicit RungeKutta met ..."
Abstract

Cited by 24 (7 self)
 Add to MetaCart
AbstractThree methods for numerical transient analysis of Markov chains, the modified Jensen's method (Jensen's method with steadystate detection of the underlying DTMC and computation of Poisson probabilities using the method of Fox and Glynn [1]), a thirdorder Lstable implicit RungeKutta method, and a secondorder Lstable method, TRBDF2, are compared. These methods are evaluated on the basis of their performance (accuracy of the solution and computational cost) on stiff Markov chains. Steadystate detection in Jensen's method results in large savings of computation time for Markov chains when mission time extends beyond the steadystate point. For stiff models, computation of Poisson probabilities using traditional methods runs into underflow problems. Fox and Glynn's method for computing Poisson probabilities avoids underflow problems for all practical problems and yields highly accurate solutions. We conclude that for mildly stiff Markov chains, the modified Jensen's method is the method of choice. For stiff Markov chains, we recommend the use of the Lstable ODE methods. If low accuracy (upto eight decimal places) is acceptable, then TRBDF2 method should be used. If higher accuracy is desired, then we recommend thirdorder implicit RungeKutta method. 1.
Recent Developments in NonMarkovian Stochastic Petri Nets
, 1998
"... Analytical modeling plays a crucial role in the analysis and design of computer systems. Stochastic Petri Nets represent a powerful paradigm, widely used for such modeling in the context of dependability, performance and performability. Many structural and stochastic extensions have been proposed in ..."
Abstract

Cited by 18 (4 self)
 Add to MetaCart
Analytical modeling plays a crucial role in the analysis and design of computer systems. Stochastic Petri Nets represent a powerful paradigm, widely used for such modeling in the context of dependability, performance and performability. Many structural and stochastic extensions have been proposed in recent years to increase their modeling power, or their capability to handle large systems. This paper reviews recent developments by providing the theoretical background and the possible areas of application. Markovian Petri nets are first considered together with very well established extensions known as Generalized Stochastic Petri nets and Stochastic Reward Nets. Key ideas for coping with large state spaces are then discussed. The challenging area of nonMarkovian Petri nets is considered, and the related analysis techniques are surveyed together with the detailed elaboration of an example. Finally new models based on Continuous or Fluid Stochastic Petri Nets are briefly discussed.
An Algorithm to Calculate Transient Distributions of Cumulative Reward
 Commun. in Statist. – Stochastic Models
, 1998
"... Markov reward models have been widely used to solve a variety of problems. In these models, reward rates are associated to the states of a continuous time Markov chain, and impulse rewards are associated to transitions of the chain. Reward rates are gained per unit time in the associated state, and ..."
Abstract

Cited by 18 (2 self)
 Add to MetaCart
Markov reward models have been widely used to solve a variety of problems. In these models, reward rates are associated to the states of a continuous time Markov chain, and impulse rewards are associated to transitions of the chain. Reward rates are gained per unit time in the associated state, and impulse rewards are instantaneous values that are gained each time certain transitions occur. We develop an efficient algorithm to calculate the distribution of the total accumulated reward over a given interval of time when both rate and impulse rewards are present. As special cases, we obtain an algorithm which is used when only rate rewards occur and another algorithm to handle the case of models for which only impulse rewards are present. The development is based purely on probabilistic arguments, and the recursions obtained are simple and have a low computational cost. 1 This work was done while E. de Souza e Silva was on leave from the Federal University of Rio de Janeiro partially su...
and W.Korfhage. Collecting Unused Processing Capacity: An Analysis of Transient Distributed Systems
 IEEE Transactions on Parallel and Distributed Systems
, 1993
"... AbstractDistributed systems frequently have large numbers of idle computers and workstations. If we could make use of these, then considerable computing power could be harnessed at low cost. We analyze such systems using Brownian motion with drift to model the execution of a program distributed ove ..."
Abstract

Cited by 15 (0 self)
 Add to MetaCart
AbstractDistributed systems frequently have large numbers of idle computers and workstations. If we could make use of these, then considerable computing power could be harnessed at low cost. We analyze such systems using Brownian motion with drift to model the execution of a program distributed over the idle computers in a network of idle and busy processors, determining how the use of these “transient ” processors affects a program’s execution time. We find the probability density of a programs finishing time on both single and multiple transient processors, explore these results for qualitative insight, and suggest some approximations for the finishing time probability density that may be useful. Index Terms Brownian motion, distributed processing, idle processors, performance analysis, transient processors.
Modeling IP Traffic Using the Batch Markovian Arrival Process
 Performance Evaluation
, 2003
"... In this paper, we show how to utilize the expectationmaximization (EM) algorithm for efficient and numerical stable parameter estimation of the batch Markovian an'ival process (BMAP). In fact, effective computational formulas for the Estep of the EM algorithm are presented, which utilize the we ..."
Abstract

Cited by 14 (0 self)
 Add to MetaCart
In this paper, we show how to utilize the expectationmaximization (EM) algorithm for efficient and numerical stable parameter estimation of the batch Markovian an'ival process (BMAP). In fact, effective computational formulas for the Estep of the EM algorithm are presented, which utilize the wellknown randomization technique and a stable calculation of Poisson jump probabilities.
Computing Bounds on Steady State Availability of Repairable Computer Systems
, 1994
"... One of the most important performance measures for computer system designers is system availability. Most often, Markov models are used in representing systems for dependability/availability analysis. Due to complex interactions between components and complex repair policies, the Markov model often ..."
Abstract

Cited by 13 (3 self)
 Add to MetaCart
One of the most important performance measures for computer system designers is system availability. Most often, Markov models are used in representing systems for dependability/availability analysis. Due to complex interactions between components and complex repair policies, the Markov model often has an irregular structure and closed form solutions are extremely difficult to obtain. Also, a realistic system model often has an unmanageably large state space and it quickly becomes impractical to even generate the entire transition rate matrix. In this paper, we present a methodology that can (i) bound the system steady state availability and at the same time, (ii) drastically reduce the state space of the model that must be solved. The bounding algorithm is iterative and generates a part of the transition matrix at each step. At each step, tighter bounds on system availability are obtained. The algorithm also allows the size of the submodel, to be solved at each step, to be chosen so a...
Performability Modelling Tools and Techniques
 Perf. Ev
, 1996
"... Over the last decade considerable effort has been put in the development of techniques to assess the performance and the dependability of computer and communication systems in an integrated way. This socalled performability modelling becomes especially useful when the system under study can operate ..."
Abstract

Cited by 12 (5 self)
 Add to MetaCart
Over the last decade considerable effort has been put in the development of techniques to assess the performance and the dependability of computer and communication systems in an integrated way. This socalled performability modelling becomes especially useful when the system under study can operate partially, which is for instance the case for faulttolerant computer systems and distributed systems. Modelling techniques are a fundamental prerequisite for actually doing performability analysis. A prerequisite of a more practical but not less important nature is the availability of software tools to support the modelling techniques and to allow system designers to incorporate the new techniques in the design process of systems. Since performability modelling requires many aspects of a system to be specified, high requirements should be posed on performability modelling tools. Moreover, these tools should be structured such that the models can be specified at a level that is easy to unde...