Estimating multiclass service demand distributions using Markovian arrival processes

Building performance models for software services in DevOps is costly and error-prone. Accurate service demand distribution estimation is critical to precisely modeling queueing behaviors and performance prediction. However, current estimation methods focus on capturing the mean service demand, disregarding higher-order moments of the distribution that still can largely affect prediction accuracy. To address this limitation, we propose to estimate higher moments of the service demand distribution for a microservice from monitoring traces. We first generate a closed queueing model to abstract software performance and use it to model the departure process of requests completed by the software service as a Markovian arrival process. This allows formulating the estimation of service demand into an optimization problem, which aims to find the first multiple moments of the service demand distribution that maximize the likelihood of the MAP using generated the measured inter-departure times. We then estimate the service demand distribution for different classes of service with a maximum likelihood algorithm and novel heuristics to mitigate the computational cost of the optimization process for scalability. We apply our method to real traces from a microservice-based application and demonstrate that its estimations lead to greater prediction accuracy than exponential distributions assumed in traditional service demand estimation approaches for software services

Process algebra approach to parallel DBMS performance modelling

Abstract unavailable please refer to PD

Event-based estimation of interacting Markov chains with applications to electrocardiogram analysis

Bibliography: p. 36-37.Supported in part by the Air Force Office of Scientific Research under grant AFOSR-82-0258 Supported by fellowships from the Fannie and John Gertz Foundation and M.D.-Ph.D. Program at Harvard University, funded in part by Public Health Service, National Research Award 2T 32 GM07753-06Peter C. Doerschuk, Robert R. Tenney, Alan S. Willsky

A parallel Branch-and-Fix Coordination based matheuristic algorithm for solving large sized multistage stochastic mixed 0-1 problems

A parallel matheuristic algorithm is presented as a spin-off from the exact Branch-and-Fix Coordination (BFC) algorithm for solving multistage stochastic mixed 0-1 problems. Some steps to guarantee the solution’s optimality are relaxed in the BFC algorithm, such that an incomplete backward branching scheme is considered for solving large sized problems. Additionally, a new branching criterion is considered, based on dynamically-guided and stage-wise ordering schemes, such that fewer Twin Node Families are expected to be visited during the execution of the so-called H-DBFC algorithm. The inner parallelization IH-DBFC of the new approach, allows to solve in parallel scenario clusters MIP submodels at different steps of the algorithm. The outer parallel version, OH-DBFC, considers independent paths and allows iterative incumbent solution values exchanges to obtain tighter bounds of the solution value of the original problem. A broad computational experience is reported for assessing the quality of the matheuristic solution for large sized instances. The instances dimensions that are considered are up to two orders of magnitude larger than in some other works that we are aware of. The optimality gap of the H-DBFC solution value versus the one obtained by a state-of-the-artMIP solver is very small, if any. The new approach frequently outperforms it in terms of solution’s quality and computing time. A comparison with our Stochastic Dynamic Programming algorithm is also reported. The use of parallel computing provides, on one hand, a perspective for solving very large sized instances and, on the other hand, an expected large reduction in elapsed time.MTM2015-65317-P, MTM2015-63710-P, IT928-16; UFI BETS 2011; IZO-SGI SGIke

Queueing networks: solutions and applications

During the pasttwo decades queueing network models have proven to be a versatile tool for computer system and computer communication system performance evaluation. This chapter provides a survey of th field with a particular emphasis on applications. We start with a brief historical retrospective which also servesto introduce the majr issues and application areas. Formal results for product form queuenig networks are reviewed with particular emphasis on the implications for computer systems modeling. Computation algorithms, sensitivity analysis and optimization techniques are among the topics covered. Many of the important applicationsof queueing networks are not amenableto exact analysis and an (often confusing) array of approximation methods have been developed over the years. A taxonomy of approximation methods is given and used as the basis for for surveing the major approximation methods that have been studied. The application of queueing network to a number of areas is surveyed, including computer system cpacity planning, packet switching networks, parallel processing, database systems and availability modeling.Durante as últimas duas décadas modelos de redes de filas provaram ser uma ferramenta versátil para avaliação de desempenho de sistemas de computação e sistemas de comunicação. Este capítulo faz um apanhado geral da área, com ênfase em aplicações. Começamos com uma breve retrospectiva histórica que serve também para introduzir os pontos mais importantes e as áreas de aplicação. Resultados formais para redes de filas em forma de produto são revisados com ênfase na modelagem de sistemas de computação. Algoritmos de computação, análise de sensibilidade e técnicas de otimização estão entre os tópicos revistos. Muitas dentre importantes aplicações de redes de filas não são tratáveis por análise exata e uma série (frequentemente confusa) de métodos de aproximação tem sido desenvolvida. Uma taxonomia de métodos de aproximação é dada e usada como base para revisão dos mais importantes métodos de aproximação propostos. Uma revisão das aplicações de redes de filas em um número de áreas é feita, incluindo planejamento de capacidade de sistemas de computação, redes de comunicação por chaveamento de pacotes, processamento paralelo, sistemas de bancos de dados e modelagem de confiabilidade

Availability modeling and evaluation on high performance cluster computing systems

Cluster computing has been attracting more and more attention from both the industrial and the academic world for its enormous computing power, cost effective, and scalability. Beowulf type cluster, for example, is a typical High Performance Computing (HPC) cluster system. Availability, as a key attribute of the system, needs to be considered at the system design stage and monitored at mission time. Moreover, system monitoring is a must to help identify the defects and ensure the system\u27s availability requirement. In this study, novel solutions which provide availability modeling, model evaluation, and data analysis as a single framework have been investigated. Three key components in the investigation are availability modeling, model evaluation, and data analysis. The general availability concepts and modeling techniques are briefly reviewed. The system\u27s availability model is divided into submodels based upon their functionalities. Furthermore, an object oriented Markov model specification to facilitate availability modeling and runtime configuration has been developed. Numerical solutions for Markov models are examined, especially on the uniformization method. Alternative implementations of the method are discussed; particularly on analyzing the cost of an alternative solution for small state space model, and different ways for solving large sparse Markov models. The dissertation also presents a monitoring and data analysis framework, which is responsible for failure analysis and availability reconfiguration. In addition, the event logs provided from the Lawrence Livermore National Laboratory have been studied and applied to validate the proposed techniques

Integrated Modeling of Complex Systems with Applications to Public Health and Sustainability

Understanding the dynamics of a changing world are of great interest to policy-makers, nonprofit organizations, governments, and businesses since society largely operates as a system. We develop system models to capture the complexity of the world in a logical and quantitative manner. Specifically, we use methods such as network analysis, time series analysis, system dynamics, and Markov Chains to explore systemic issues. These methods are applied to a socio-technical system related to public health and sustainability. We will also explore ways to capture this complexity by first identifying and analyzing the system with an interdisciplinary perspective then propose a method to integrate system models. We begin by identifying the complexity of large-scale systems, such as Research & Development (R&D) of pharmaceutical treatments. In this project, we utilize a network representation to investigate collaboration among pharmaceutical companies and other stakeholders to determine the causes that enable success in developing a regulatory-approved therapeutic treatment. Secondly, we propose an integrated multi-component model to capture the feedback loops that couples global population growth, environmental sustainability, and health systems. Finally, we investigate a system dynamics integration of a Markov Chain that describes migration patterns of the United States with respect to climate change