Extended Abstracts: PMCCS3: Third International Workshop on Performability Modeling of Computer and Communication Systems

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryThe pages of the front matter that are missing from the PDF were blank

Methodologies synthesis

This deliverable deals with the modelling and analysis of interdependencies between critical infrastructures, focussing attention on two interdependent infrastructures studied in the context of CRUTIAL: the electric power infrastructure and the information infrastructures supporting management, control and maintenance functionality. The main objectives are: 1) investigate the main challenges to be addressed for the analysis and modelling of interdependencies, 2) review the modelling methodologies and tools that can be used to address these challenges and support the evaluation of the impact of interdependencies on the dependability and resilience of the service delivered to the users, and 3) present the preliminary directions investigated so far by the CRUTIAL consortium for describing and modelling interdependencies

Scalable Performance Analysis of Massively Parallel Stochastic Systems

The accurate performance analysis of large-scale computer and communication systems is directly inhibited by an exponential growth in the state-space of the underlying Markovian performance model. This is particularly true when considering massively-parallel architectures such as cloud or grid computing infrastructures. Nevertheless, an ability to extract quantitative performance measures such as passage-time distributions from performance models of these systems is critical for providers of these services. Indeed, without such an ability, they remain unable to offer realistic end-to-end service level agreements (SLAs) which they can have any confidence of honouring. Additionally, this must be possible in a short enough period of time to allow many different parameter combinations in a complex system to be tested. If we can achieve this rapid performance analysis goal, it will enable service providers and engineers to determine the cost-optimal behaviour which satisfies the SLAs. In this thesis, we develop a scalable performance analysis framework for the grouped PEPA stochastic process algebra. Our approach is based on the approximation of key model quantities such as means and variances by tractable systems of ordinary differential equations (ODEs). Crucially, the size of these systems of ODEs is independent of the number of interacting entities within the model, making these analysis techniques extremely scalable. The reliability of our approach is directly supported by convergence results and, in some cases, explicit error bounds. We focus on extracting passage-time measures from performance models since these are very commonly the language in which a service level agreement is phrased. We design scalable analysis techniques which can handle passages defined both in terms of entire component populations as well as individual or tagged members of a large population. A precise and straightforward specification of a passage-time service level agreement is as important to the performance engineering process as its evaluation. This is especially true of large and complex models of industrial-scale systems. To address this, we introduce the unified stochastic probe framework. Unified stochastic probes are used to generate a model augmentation which exposes explicitly the SLA measure of interest to the analysis toolkit. In this thesis, we deploy these probes to define many detailed and derived performance measures that can be automatically and directly analysed using rapid ODE techniques. In this way, we tackle applicable problems at many levels of the performance engineering process: from specification and model representation to efficient and scalable analysis

A formalism for describing and simulating systems with interacting components.

This thesis addresses the problem of descriptive complexity presented by systems involving a high number of interacting components. It investigates the evaluation measure of performability and its application to such systems. A new description and simulation language, ICE and it's application to performability modelling is presented. ICE (Interacting ComponEnts) is based upon an earlier description language which was first proposed for defining reliability problems. ICE is declarative in style and has a limited number of keywords. The ethos in the development of the language has been to provide an intuitive formalism with a powerful descriptive space. The full syntax of the language is presented with discussion as to its philosophy. The implementation of a discrete event simulator using an ICE interface is described, with use being made of examples to illustrate the functionality of the code and the semantics of the language. Random numbers are used to provide the required stochastic behaviour within the simulator. The behaviour of an industry standard generator within the simulator and different methods of number allocation are shown. A new generator is proposed that is a development of a fast hardware shift register generator and is demonstrated to possess good statistical properties and operational speed. For the purpose of providing a rigorous description of the language and clarification of its semantics, a computational model is developed using the formalism of extended coloured Petri nets. This model also gives an indication of the language's descriptive power relative to that of a recognised and well developed technique. Some recognised temporal and structural problems of system event modelling are identified. and ICE solutions given. The growing research area of ATM communication networks is introduced and a sophisticated top down model of an ATM switch presented. This model is simulated and interesting results are given. A generic ICE framework for performability modelling is developed and demonstrated. This is considered as a positive contribution to the general field of performability research

Methodology for the Accelerated Reliability Analysis and Prognosis of Underground Cables based on FPGA

Dependable electrical power distribution systems demand high reliability levels that cause increased maintenance costs to the utilities. Often, the extra costs are the result of unnecessary maintenance procedures, which can be avoided by monitoring the equipment and predicting the future system evolution by means of statistical methods (prognostics). The present thesis aims at designing accurate methods for predicting the degradation of high and medium voltage underground Cross-Linked Polyethylene (XLPE) cables within an electrical power distribution grid, and predicting their remaining useful life, in order inform maintenance procedures. However, electric power distribution grids are large, components interact with each other, and they degrade with time and use. Solving the statistics of the predictive models of the power grids currently requires long numerical simulations that demand large computational resources and long simulation times even when using advanced parallel architectures. Often, approximate models are used in order to reduce the simulation time and the required resources. In this context, Field Programmable Gate Arrays (FPGAs) can be employed to accelerate the simulation of these stochastic processes. However, the adaptation of the physicsbased degradation models of underground cables for FPGA simulation can be complex. Accordingly, this thesis proposes an FPGA-based framework for the on-line monitoring and prognosis of underground cables based on an electro-thermal degradation model that is adapted for its accelerated simulation in the programmable logic of an FPGA.Energia elektrikoaren banaketa-sare konfidagarriek fidagarritasun maila altuak eskatzen dituzte, eta honek beraien mantenketa kostuen igoera dakar. Kostu hauen arrazoia beraien bizitzan goizegi egiten diren mantenketa prozesuei dagokie askotan, eta hauek eragoztea posible da, ekipamenduaren monitorizazioa eginez eta sistemaren etorkizuneko eboluzioa aurrez estimatuz (prognosia). Tesi honen helburua lurpeko tentsio altu eta ertaineko Cross-Linked Polyethylene (XLPE) kable sistemen eboluzioa eta geratzen zaien bizitza aurreikusiko duten metodo egokiak definitzea izango da, banaketa-sare elektriko baten barruan, ondoren mantenketa prozesu optimo bat ahalbidetuko duena. Hala ere, sistema hauek oso jokaera dinamikoa daukate. Konponente ezberdinek beraien artean elkar eragiten dute eta degradatu egiten dira denboran eta erabileraren ondorioz. Estatistika hauen soluzio analitikoa lortzea ezinezkoa da gaur egun, eta errekurtso asko eskatzen dituen simulazio luzeak behar ditu zenbakizko erantzun bat lortzeko, arkitektura paralelo aurreratuak erabili arren. Field Programmable Gate Array (FPGA)k prozesu estokastiko hauen simulazioa azkartzeko erabil daitezke, baina lurpeko kableen degradazio prozesuen modelo fisikoak FPGA exekuziorako egokitzea konplexua izan daiteke. Beraz, tesi honek FPGA baten logika programagarrian azeleratu ahal izateko egokitua izan den degradazio elektrotermiko modelo baten oinarritutako monitorizazio eta prognosi metodologia bat proposatzen du.Las redes de distribución de energía eléctrica confiables requieren de altos niveles de fiabilidad, que causan un mayor coste de mantenimiento a las empresas distribuidoras. Frecuentemente los costes adicionales son el resultado de procedimientos de mantenimiento innecesarios, que se pueden evitar por medio de la monitorización de los equipos y la predicción de la evolución futura del sistema, por medio de métodos estadísticos (prognosis). La presente tesis pretende desarrollar métodos adecuados para la predicción de la degradación futura de cables de alta y media tensión Cross-Linked Polyethylene (XLPE) soterrados, dentro de una red de distribución eléctrica, y predecir su tiempo de vida restante, para definir una secuencia de mantenimiento óptima. Sin embargo, las redes de distribución eléctrica son grandes, y compuestas por componentes que interactúan entre sí y se degradan con el tiempo y el uso. En la actualidad, resolver estas estadísticas predictivas requieren grandes simulaciones numéricas que requieren de grandes recursos computacionales y largos tiempos de simulación, incluso utilizando arquitecturas paralelas avanzadas. Las Field Programmable Gate Array (FPGA) pueden ser utilizadas para acelerar las simulaciones de estos procesos estocásticos, pero la adaptación de los modelos físicos de degradación de cables soterrados para su simulación en una FPGA puede ser complejo. Así, esta tesis propone el desarrollo de una metodología de monitorización y prognosis cables soterrados, basado en un modelo de degradación electro-térmico que está adaptado para su simulación acelerada en la lógica programable de una FPGA