Reports de recerca
http://hdl.handle.net/2117/3646
Sun, 26 Feb 2017 12:39:25 GMT2017-02-26T12:39:25ZMETFAC-2.1 User's Guide
http://hdl.handle.net/2117/25119
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Carrasco, Juan A.; Suñé, Víctor
Technical Report
Mon, 22 Dec 2014 07:47:11 GMThttp://hdl.handle.net/2117/251192014-12-22T07:47:11ZCarrasco, Juan A.Suñé, VíctorCumulative dominance and heuristic performance in binary multi-attribute choice
http://hdl.handle.net/2117/24906
Cumulative dominance and heuristic performance in binary multi-attribute choice
Carrasco, Juan A.; Baucells, M; Hogarth, R M
Several studies have reported high performance of simple decision heuristics in multi-attribute decision making. In this paper, we focus on situations where attributes are binary and analyze the performance of Deterministic-Elimination-By-Aspects (DEBA) and similar decision heuristics. We consider non-increasing weights and two probabilistic models for the attribute values: one where attribute values are independent Bernoulli randomvariables; the other one where they are binary random variables with inter-attribute positive correlations. Using these models, we show that good performance of DEBA is explained by the presence of cumulative as opposed to simple dominance. We therefore introduce the concepts of cumulative dominance compliance and fully cumulative dominance compliance and show that DEBA satisfies those properties. We derive a lower bound with which cumulative dominance compliant heuristics will choose a best alternative and show that, even with many attributes, this is not small. We also derive an upper bound for the expected loss of fully cumulative compliance heuristics and show that this is moderate even when the number of attributes is large. Both bounds are independent of the values of the weights.
Working paper 895, Department of Economics and Business, Universitat Pompeu Fabra
Wed, 03 Dec 2014 10:18:01 GMThttp://hdl.handle.net/2117/249062014-12-03T10:18:01ZCarrasco, Juan A.Baucells, MHogarth, R MSeveral studies have reported high performance of simple decision heuristics in multi-attribute decision making. In this paper, we focus on situations where attributes are binary and analyze the performance of Deterministic-Elimination-By-Aspects (DEBA) and similar decision heuristics. We consider non-increasing weights and two probabilistic models for the attribute values: one where attribute values are independent Bernoulli randomvariables; the other one where they are binary random variables with inter-attribute positive correlations. Using these models, we show that good performance of DEBA is explained by the presence of cumulative as opposed to simple dominance. We therefore introduce the concepts of cumulative dominance compliance and fully cumulative dominance compliance and show that DEBA satisfies those properties. We derive a lower bound with which cumulative dominance compliant heuristics will choose a best alternative and show that, even with many attributes, this is not small. We also derive an upper bound for the expected loss of fully cumulative compliance heuristics and show that this is moderate even when the number of attributes is large. Both bounds are independent of the values of the weights.Process variability in sub-16nm bulk CMOS technology
http://hdl.handle.net/2117/15667
Process variability in sub-16nm bulk CMOS technology
Rubio Sola, Jose Antonio; Figueras Pàmies, Joan; Vatajelu, Elena Ioana; Canal Corretger, Ramon
The document is part of deliverable D3.6 of the TRAMS Project (EU FP7 248789), of public nature, and shows and justifies the levels of variability used in the research project for sub-18nm bulk CMOS technologies.
Mon, 26 Mar 2012 18:45:53 GMThttp://hdl.handle.net/2117/156672012-03-26T18:45:53ZRubio Sola, Jose AntonioFigueras Pàmies, JoanVatajelu, Elena IoanaCanal Corretger, RamonThe document is part of deliverable D3.6 of the TRAMS Project (EU FP7 248789), of public nature, and shows and justifies the levels of variability used in the research project for sub-18nm bulk CMOS technologies.A failure-distance based method to bound the reliability of non-repairable Fault-tolerant systems without the knowledge of minimal cuts
http://hdl.handle.net/2117/7846
A failure-distance based method to bound the reliability of non-repairable Fault-tolerant systems without the knowledge of minimal cuts
Suñé, Víctor; Carrasco, Juan A.
CTMC (continuous-time Markov chains) are a commonly used formalism for modeling
fault-tolerant systems. One of the major drawbacks of CTMC is the well-known
state-space explosion problem. This work develops and analyzes a method (SC-BM) to
compute bounds for the reliability of non-repairable fault-tolerant systems in which only
a portion of the state space of the CTMC is generated. SC-BM uses the failure distance
concept as the method described in [1] but, unlike that method, which is based on the
computation of exact failure distances, SC-BM uses lower bounds for failure distances,
which are computed on the system fault tree, avoiding the computation and holding of
all minimal cuts as required in [1]. This is important since computation of all minimal
cuts is NP-hard and the number of minimal cuts can be very large. In some cases SCBM
gives exactly the same bounds as the method described in [1]; in other cases it gives
less tighter bounds. SC-BM computes tight bounds for the reliability of quite complex
systems with an affordable number of generated states for short to quite large mission
times. The analysis of several examples seems to show that the bounds obtained by
SC-BM appreciably outperform those obtained by simpler methods, eg [2], and, when
they are not equal, are only slightly worse than the bounds obtained by the method in
[1]. In addition, the overhead in CPU time due to computing lower bounds for failure
distances seems to be reasonable.
Fri, 25 Jun 2010 15:21:51 GMThttp://hdl.handle.net/2117/78462010-06-25T15:21:51ZSuñé, VíctorCarrasco, Juan A.CTMC (continuous-time Markov chains) are a commonly used formalism for modeling
fault-tolerant systems. One of the major drawbacks of CTMC is the well-known
state-space explosion problem. This work develops and analyzes a method (SC-BM) to
compute bounds for the reliability of non-repairable fault-tolerant systems in which only
a portion of the state space of the CTMC is generated. SC-BM uses the failure distance
concept as the method described in [1] but, unlike that method, which is based on the
computation of exact failure distances, SC-BM uses lower bounds for failure distances,
which are computed on the system fault tree, avoiding the computation and holding of
all minimal cuts as required in [1]. This is important since computation of all minimal
cuts is NP-hard and the number of minimal cuts can be very large. In some cases SCBM
gives exactly the same bounds as the method described in [1]; in other cases it gives
less tighter bounds. SC-BM computes tight bounds for the reliability of quite complex
systems with an affordable number of generated states for short to quite large mission
times. The analysis of several examples seems to show that the bounds obtained by
SC-BM appreciably outperform those obtained by simpler methods, eg [2], and, when
they are not equal, are only slightly worse than the bounds obtained by the method in
[1]. In addition, the overhead in CPU time due to computing lower bounds for failure
distances seems to be reasonable.Transient analysis of large Markov models with absorbing states using regenerative randomization
http://hdl.handle.net/2117/7845
Transient analysis of large Markov models with absorbing states using regenerative randomization
Carrasco, Juan A.
In this paper, we develop a new method, called regenerative randomization, for the transient
analysis of continuous time Markov models with absorbing states. The method has the same
good properties as standard randomization: numerical stability, well-controlled computation
error, and ability to specify the computation error in advance. The method has a benign behavior
for large t and is significantly less costly than standard randomization for large enough models
and large enough t. For a class of models, class C, including typical failure/repair reliability
models with exponential failure and repair time distributions and repair in every state with failed components, stronger theoretical results are available assessing the efficiency of the method in terms of “visible” model characteristics. A large example belonging to that class is used to illustrate the performance of the method and to show that it can indeed be much faster than standard randomization.
Fri, 25 Jun 2010 15:14:30 GMThttp://hdl.handle.net/2117/78452010-06-25T15:14:30ZCarrasco, Juan A.In this paper, we develop a new method, called regenerative randomization, for the transient
analysis of continuous time Markov models with absorbing states. The method has the same
good properties as standard randomization: numerical stability, well-controlled computation
error, and ability to specify the computation error in advance. The method has a benign behavior
for large t and is significantly less costly than standard randomization for large enough models
and large enough t. For a class of models, class C, including typical failure/repair reliability
models with exponential failure and repair time distributions and repair in every state with failed components, stronger theoretical results are available assessing the efficiency of the method in terms of “visible” model characteristics. A large example belonging to that class is used to illustrate the performance of the method and to show that it can indeed be much faster than standard randomization.A generalized method for the transient analysis of Markov models of fault-tolerant systems with deferred repair
http://hdl.handle.net/2117/7844
A generalized method for the transient analysis of Markov models of fault-tolerant systems with deferred repair
Temsamani, Jamal; Carrasco, Juan A.
Randomization is an attractive alternative for the transient analysis of continuous time Markov
models. The main advantages of the method are numerical stability, well-controlled computation
error and ability to specify the computation error in advance. However, the fact that the
method can be computationally expensive limits its applicability. Recently, a variant of the
(standard) randomization method, called split regenerative randomization has been proposed
for the efficient analysis of reliability-like models of fault-tolerant systems with deferred repair. In this paper, we generalize that method so that it covers more general reward measures: the
expected transient reward rate and the expected averaged reward rate. The generalized method
has the same good properties as the standard randomization method and, for large models and
large values of the time t at which the measure has to be computed, can be significantly less
expensive. The method requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, class C'_2, including
typical failure/repair reliability models with exponential failure and repair time distributions
and deferred repair, natural selections for the subset of states and the regenerative state exist and results are available assessing approximately the computational cost of the method in terms of
“visible” model characteristics. Using a large model class C'_2 example, we illustrate the performance of the method and show that it can be significantly faster than previously proposed
randomization-based methods.
Fri, 25 Jun 2010 15:09:25 GMThttp://hdl.handle.net/2117/78442010-06-25T15:09:25ZTemsamani, JamalCarrasco, Juan A.Randomization is an attractive alternative for the transient analysis of continuous time Markov
models. The main advantages of the method are numerical stability, well-controlled computation
error and ability to specify the computation error in advance. However, the fact that the
method can be computationally expensive limits its applicability. Recently, a variant of the
(standard) randomization method, called split regenerative randomization has been proposed
for the efficient analysis of reliability-like models of fault-tolerant systems with deferred repair. In this paper, we generalize that method so that it covers more general reward measures: the
expected transient reward rate and the expected averaged reward rate. The generalized method
has the same good properties as the standard randomization method and, for large models and
large values of the time t at which the measure has to be computed, can be significantly less
expensive. The method requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, class C'_2, including
typical failure/repair reliability models with exponential failure and repair time distributions
and deferred repair, natural selections for the subset of states and the regenerative state exist and results are available assessing approximately the computational cost of the method in terms of
“visible” model characteristics. Using a large model class C'_2 example, we illustrate the performance of the method and show that it can be significantly faster than previously proposed
randomization-based methods.An efficient and numerically stable method for computing interval availability distribution bounds
http://hdl.handle.net/2117/7843
An efficient and numerically stable method for computing interval availability distribution bounds
Carrasco, Juan A.
The paper develops a method, called bounding regenerative transformation, for the computation
with numerical stability and well-controlled error of bounds for the interval availability
distribution of systems modeled by finite (homogeneous) continuous-time Markov chain models
with a particular structure. The method requires the selection of a regenerative state and is
targeted at a class of models, class C'_1, with a “natural” selection for the regenerative state. For class C'_1 models, bounds tightness can be traded-off with computational cost through a control parameter D_C, with the option D_C = 1 yielding the smallest computational cost. For large class C'_1 models and the selection D_C = 1, the method will often have a small computational
cost relative to the model size and, with additional conditions, seems to yield tight bounds for any time interval or not small time intervals, depending on the initial probability distribution of the model. Class C'_1 models with those additional conditions include both exact and bounding failure/repair models of coherent fault-tolerant systems with exponential failure and repair time distributions and repair in every state with failed components with failure rates much smaller than repair rates.
Fri, 25 Jun 2010 15:01:16 GMThttp://hdl.handle.net/2117/78432010-06-25T15:01:16ZCarrasco, Juan A.The paper develops a method, called bounding regenerative transformation, for the computation
with numerical stability and well-controlled error of bounds for the interval availability
distribution of systems modeled by finite (homogeneous) continuous-time Markov chain models
with a particular structure. The method requires the selection of a regenerative state and is
targeted at a class of models, class C'_1, with a “natural” selection for the regenerative state. For class C'_1 models, bounds tightness can be traded-off with computational cost through a control parameter D_C, with the option D_C = 1 yielding the smallest computational cost. For large class C'_1 models and the selection D_C = 1, the method will often have a small computational
cost relative to the model size and, with additional conditions, seems to yield tight bounds for any time interval or not small time intervals, depending on the initial probability distribution of the model. Class C'_1 models with those additional conditions include both exact and bounding failure/repair models of coherent fault-tolerant systems with exponential failure and repair time distributions and repair in every state with failed components with failure rates much smaller than repair rates.Simulation of steady-state availability models of fault-tolerant systems with deferred repair
http://hdl.handle.net/2117/7842
Simulation of steady-state availability models of fault-tolerant systems with deferred repair
Carrasco, Juan A.
This paper targets the simulation of continuous-time Markov chain models of fault-tolerant systems with deferred repair. We start by stating sufficient conditions for a given importance sampling scheme to satisfy the bounded relative error property. Using those sufficient conditions, it is noted that many previously proposed importance sampling schemes such as failure biasing and balanced failure biasing satisfy that property. Then, we adapt the importance sampling schemes failure transition distance biasing and balanced failure transition distance biasing so as to develop new importance sampling schemes which can be implemented with moderate effort and at the same time can be proved to be more efficient for balanced systems than the simpler failure biasing and balanced failure biasing schemes. The increased efficiency for balanced and unbalanced systems of the new adapted importance sampling schemes is illustrated using examples.
Fri, 25 Jun 2010 14:52:31 GMThttp://hdl.handle.net/2117/78422010-06-25T14:52:31ZCarrasco, Juan A.This paper targets the simulation of continuous-time Markov chain models of fault-tolerant systems with deferred repair. We start by stating sufficient conditions for a given importance sampling scheme to satisfy the bounded relative error property. Using those sufficient conditions, it is noted that many previously proposed importance sampling schemes such as failure biasing and balanced failure biasing satisfy that property. Then, we adapt the importance sampling schemes failure transition distance biasing and balanced failure transition distance biasing so as to develop new importance sampling schemes which can be implemented with moderate effort and at the same time can be proved to be more efficient for balanced systems than the simpler failure biasing and balanced failure biasing schemes. The increased efficiency for balanced and unbalanced systems of the new adapted importance sampling schemes is illustrated using examples.Energy macro-model for on chip interconnection buses
http://hdl.handle.net/2117/1232
Energy macro-model for on chip interconnection buses
Mendoza Vázquez, Raymundo; Pons Solé, Marc; Moll Echeto, Francisco de Borja; Figueras, Joan
This report presents a fast method of evaluating the power consumption of a bus. Given an on-chip bus driver-interconnection-receiver design of N parallel lines,the objective is to develop its energy consumption macro-model. With this model we are be able to evaluate the energy metrics for the bus under a certain traffic and information coding.
Fri, 05 Oct 2007 08:09:54 GMThttp://hdl.handle.net/2117/12322007-10-05T08:09:54ZMendoza Vázquez, RaymundoPons Solé, MarcMoll Echeto, Francisco de BorjaFigueras, JoanThis report presents a fast method of evaluating the power consumption of a bus. Given an on-chip bus driver-interconnection-receiver design of N parallel lines,the objective is to develop its energy consumption macro-model. With this model we are be able to evaluate the energy metrics for the bus under a certain traffic and information coding.