Delay Distributions in Discrete Time Multiclass Tandem Communication Network Models

An exact computational algorithm for the solution of a discrete time multiclass tandem network with a primary class and cross-traffic at each queue is developed. A sequence of truncated Lindley recursions is defined at each queue relating the delays experienced by the first packet from consecutive batches of a class at that queue. Using this sequence of recursions, a convolve-and-sweep algorithm is developed to compute the stationary distributions of the delay and inter-departure processes of each class at a queue, delays experienced by a typical packet from the primary class along its path as well as the mean end-to-end delay of such a packet. The proposed approach is designed to handle the non-renewal arrival processes arising in the network. The algorithmic solution is implemented as an abstract class which permits its easy adaptation to analyze different network configurations and sizes. The delays of a packet at different queues are shown to be associated random variables from which it follows that the variance of total delay is lower bounded by the sum of variances of delays at the queues along the path. The developed algorithm and the proposed lower bound on the variance of total delay are validated against simulation for a tandem network of two queues with three classes under different batch size distributions

Aggregate matrix-analytic techniques and their applications

The complexity of computer systems affects the complexity of modeling techniques that can be used for their performance analysis. In this dissertation, we develop a set of techniques that are based on tractable analytic models and enable efficient performance analysis of computer systems. Our approach is three pronged: first, we propose new techniques to parameterize measurement data with Markovian-based stochastic processes that can be further used as input into queueing systems; second, we propose new methods to efficiently solve complex queueing models; and third, we use the proposed methods to evaluate the performance of clustered Web servers and propose new load balancing policies based on this analysis.;We devise two new techniques for fitting measurement data that exhibit high variability into Phase-type (PH) distributions. These techniques apply known fitting algorithms in a divide-and-conquer fashion. We evaluate the accuracy of our methods from both the statistics and the queueing systems perspective. In addition, we propose a new methodology for fitting measurement data that exhibit long-range dependence into Markovian Arrival Processes (MAPs).;We propose a new methodology, ETAQA, for the exact solution of M/G/1-type processes, (GI/M/1-type processes, and their intersection, i.e., quasi birth-death (QBD) processes. ETAQA computes an aggregate steady state probability distribution and a set of measures of interest. E TAQA is numerically stable and computationally superior to alternative solution methods. Apart from ETAQA, we propose a new methodology for the exact solution of a class of GI/G/1-type processes based on aggregation/decomposition.;Finally, we demonstrate the applicability of the proposed techniques by evaluating load balancing policies in clustered Web servers. We address the high variability in the service process of Web servers by dedicating the servers of a cluster to requests of similar sizes and propose new, content-aware load balancing policies. Detailed analysis shows that the proposed policies achieve high user-perceived performance and, by continuously adapting their scheduling parameters to the current workload characteristics, provide good performance under conditions of transient overload

On time-to-buffer overflow distribution in a single-machine discrete-time system with finite capacity

A model of a single-machine production system with finite magazine capacity is investigated. The input flow of jobs is organized according to geometric distribution of interarrival times, while processing times are assumed to be generally distributed. The closed-form formula for the generating function of the time to the first buffer overflow distribution conditioned by the initial buffer state is found. The analytical approach based on the idea of embedded Markov chain, the formula of total probability and linear algebra is applied. The corresponding result for next buffer overflows is also given. Numerical examples are attached as well

GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP

Full detector simulation was among the largest CPU consumer in all CERN experiment software stacks for the first two runs of the Large Hadron Collider (LHC). In the early 2010's, the projections were that simulation demands would scale linearly with luminosity increase, compensated only partially by an increase of computing resources. The extension of fast simulation approaches to more use cases, covering a larger fraction of the simulation budget, is only part of the solution due to intrinsic precision limitations. The remainder corresponds to speeding-up the simulation software by several factors, which is out of reach using simple optimizations on the current code base. In this context, the GeantV R&D project was launched, aiming to redesign the legacy particle transport codes in order to make them benefit from fine-grained parallelism features such as vectorization, but also from increased code and data locality. This paper presents extensively the results and achievements of this R&D, as well as the conclusions and lessons learnt from the beta prototype.Comment: 34 pages, 26 figures, 24 table

Control Strategies for Improving Cloud Service Robustness

This thesis addresses challenges in increasing the robustness of cloud-deployed applications and services to unexpected events and dynamic workloads. Without precautions, hardware failures and unpredictable large traffic variations can quickly degrade the performance of an application due to mismatch between provisioned resources and capacity needs. Similarly, disasters, such as power outages and fire, are unexpected events on larger scale that threatens the integrity of the underlying infrastructure on which an application is deployed.First, the self-adaptive software concept of brownout is extended to replicated cloud applications. By monitoring the performance of each application replica, brownout is able to counteract temporary overload situations by reducing the computational complexity of jobs entering the system. To avoid existing load balancers interfering with the brownout functionality, brownout-aware load balancers are introduced. Simulation experiments show that the proposed load balancers outperform existing load balancers in providing a high quality of service to as many end users as possible. Experiments in a testbed environment further show how a replicated brownout-enabled application is able to maintain high performance during overloads as compared to its non-brownout equivalent.Next, a feedback controller for cloud autoscaling is introduced. Using a novel way of modeling the dynamics of typical cloud application, a mechanism similar to the classical Smith predictor to compensate for delays in reconfiguring resource provisioning is presented. Simulation experiments show that the feedback controller is able to achieve faster control of the response times of a cloud application as compared to a threshold-based controller.Finally, a solution for handling the trade-off between performance and disaster tolerance for geo-replicated cloud applications is introduced. An automated mechanism for differentiating application traffic and replication traffic, and dynamically managing their bandwidth allocations using an MPC controller is presented and evaluated in simulation. Comparisons with commonly used static approaches reveal that the proposed solution in overload situations provides increased flexibility in managing the trade-off between performance and data consistency

Introduction to Queueing Theory and Stochastic Teletraffic Models

The aim of this textbook is to provide students with basic knowledge of stochastic models that may apply to telecommunications research areas, such as traffic modelling, resource provisioning and traffic management. These study areas are often collectively called teletraffic. This book assumes prior knowledge of a programming language, mathematics, probability and stochastic processes normally taught in an electrical engineering course. For students who have some but not sufficiently strong background in probability and stochastic processes, we provide, in the first few chapters, background on the relevant concepts in these areas.Comment: 298 page

The evaluation of computer performance by means of state-dependent queueing network models

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Adaptive delay-constrained internet media transport

Reliable transport layer Internet protocols do not satisfy the requirements of packetized, real-time multimedia streams. The available thesis motivates and defines predictable reliability as a novel, capacity-approaching transport paradigm, supporting an application-specific level of reliability under a strict delay constraint. This paradigm is being implemented into a new protocol design -- the Predictably Reliable Real-time Transport protocol (PRRT). In order to predictably achieve the desired level of reliability, proactive and reactive error control must be optimized under the application\u27s delay constraint. Hence, predictably reliable error control relies on stochastic modeling of the protocol response to the modeled packet loss behavior of the network path. The result of the joined modeling is periodically evaluated by a reliability control policy that validates the protocol configuration under the application constraints and under consideration of the available network bandwidth. The adaptation of the protocol parameters is formulated into a combinatorial optimization problem that is solved by a fast search algorithm incorporating explicit knowledge about the search space. Experimental evaluation of PRRT in real Internet scenarios demonstrates that predictably reliable transport meets the strict QoS constraints of high-quality, audio-visual streaming applications.Zuverlässige Internet-Protokolle auf Transport-Layer erfüllen nicht die Anforderungen paketierter Echtzeit-Multimediaströme. Die vorliegende Arbeit motiviert und definiert Predictable Reliability als ein neuartiges, kapazitäterreichendes Transport-Paradigma, das einen anwendungsspezifischen Grad an Zuverlässigkeit unter strikter Zeitbegrenzung unterstützt. Dieses Paradigma wird in ein neues Protokoll-Design implementiert -- das Predictably Reliable Real-time Transport Protokoll (PRRT). Um prädizierbar einen gewünschten Grad an Zuverlässigkeit zu erreichen, müssen proaktive und reaktive Maßnahmen zum Fehlerschutz unter der Zeitbegrenzung der Anwendung optimiert werden. Daher beruht Fehlerschutz mit Predictable Reliability auf der stochastischen Modellierung des Protokoll-Verhaltens unter modelliertem Paketverlust-Verhalten des Netzwerkpfades. Das Ergebnis der kombinierten Modellierung wird periodisch durch eine Reliability Control Strategie ausgewertet, die die Konfiguration des Protokolls unter den Begrenzungen der Anwendung und unter Berücksichtigung der verfügbaren Netzwerkbandbreite validiert. Die Adaption der Protokoll-Parameter wird durch ein kombinatorisches Optimierungsproblem formuliert, welches von einem schnellen Suchalgorithmus gelöst wird, der explizites Wissen über den Suchraum einbezieht. Experimentelle Auswertung von PRRT in realen Internet-Szenarien demonstriert, dass Transport mit Predictable Reliability die strikten Auflagen hochqualitativer, audiovisueller Streaming-Anwendungen erfüllt