Model-Based Performance Anticipation in Multi-tier Autonomic Systems: Methodology and Experiments

http://www.thinkmind.org/download.php?articleid=netser_v3_n34_2010_3International audienceThis paper advocates for the introduction of perfor- mance awareness in autonomic systems. Our goal is to introduce performance prediction of a possible target configuration when a self-* feature is planning a system reconfiguration. We propose a global and partially automated process based on queues and queuing networks modelling. This process includes decomposing a distributed application into black boxes, identifying the queue model for each black box and assembling these models into a queuing network according to the candidate target configuration. Finally, performance prediction is performed either through simulation or analysis. This paper sketches the global process and focuses on the black box model identification step. This step is automated thanks to a load testing platform enhanced with a workload control loop. Model identification is based on statistical tests. The identified models are then used in performance prediction of autonomic system configurations. This paper describes the whole process through a practical experiment with a multi-tier application

Model-Based Performance Anticipation in Multi-tier Autonomic Systems: Methodology and Experiments

http://www.thinkmind.org/download.php?articleid=netser_v3_n34_2010_3International audienceThis paper advocates for the introduction of perfor- mance awareness in autonomic systems. Our goal is to introduce performance prediction of a possible target configuration when a self-* feature is planning a system reconfiguration. We propose a global and partially automated process based on queues and queuing networks modelling. This process includes decomposing a distributed application into black boxes, identifying the queue model for each black box and assembling these models into a queuing network according to the candidate target configuration. Finally, performance prediction is performed either through simulation or analysis. This paper sketches the global process and focuses on the black box model identification step. This step is automated thanks to a load testing platform enhanced with a workload control loop. Model identification is based on statistical tests. The identified models are then used in performance prediction of autonomic system configurations. This paper describes the whole process through a practical experiment with a multi-tier application

Approximations fluides pour des modèles stochastiques en télécommunications

When modeling systems for their performance evaluation, one privileged tool is some form of Markov process, because of the rich set of results and associated algorithms. The drawback is that sometimes, the process has a huge number of states, or an infinite state space. In these situations, since analytical results are rare, almost always the solution to analyze the models is simulation. In this thesis we explore another possibility, called fluid limits, where a sequence of models is built with some parameter N associated with the individual model's size, in such a way that the performances of the Nth model gets close to that of the original system when N goes to infinity. We consider three families of systems/models and we explore this approach, obtaining results focused on understanding the meaning of this convergence phenomenon, and on the properties of the limiting models.Lorsqu'on modélise un système pour évaluer ses performances, l'un des outils principaux est le processus de Markov, pour la richesse des résultats et des algorithmes associés. L'inconvénient est que parfois, le modèle résultant a une énorme quantité d'états, voire un espace d'état infini. Dans ces situations, dans la mesure où les résultats analytiques sont rares, presque toujours la seule solution disponible pour l'analysis des modèles est la simulation. Dans cette thèse nous explorons une autre possibilité, appelée limites fluides, où une séquence de modèles est construite, avec un paramètre N relié à la taille de chaque modèle de la séquence, de telle sorte que les performances du Nème modèle sont proches de celles du système d'origine, quand N tends vers l'infini. Nous considérons 3 familles de systèmes/modèles et nous explorons cette approche, en obtenant des résultats focalisés sur la compréhension de ce phénomène de convergence et sur les propriétés des modèles limites

Age of Information in Multi-Hop Connections with Tributary Traffic and no Preemption

Age of Information (AoI) has gained significant attention from the research community because of its applications to Internet of Things (IoT) monitoring and control. In this work, we treat multihop connections over queuing networks with tributary flows and non-preemptive service: packets cannot be discarded because they are utilized for other system objectives, such as data analytics. Without preemption, the key tool for optimizing AoI is then the scheduling policy between the different data flows at each intermediate node. This is the subject of our analysis, along with the impact of packet erasure on the age. We derive upper and lower bounds for the average AoI considering several queuing policies in arbitrary network topologies, and present the results in different scenarios. Network topology, tributary traffic load, and link characteristics such as packet erasure generate complex trade-offs, which affect the optimal operation point and the age performance. The scheduling strategy at each node can also affect performance and fairness among users, particularly at critical bottleneck links, which have a significant impact on the overall performance of the whole network.acceptedVersionPeer reviewe

Workforce behaviour in healthcare systems

This thesis investigates the behavioural dynamics that emerge at the interface of Emergency Departments (EDs) and the Emergency Medical Service (EMS). The focus is on the impact that time-targets may have on staff behaviour and patient well-being. This research is structured into two main parts: the first part is the development of a queueing theoretic representation of an ED and the second part is the development of a game theoretic model between two EDs and the EMS that distributes ambulances to them. This thesis uses a variety of mathematical and computational fields such as linear algebra, game theory, queueing theory, graph theory, optimisation, probability theory, agent-based simulation and reinforcement learning. The queueing model is developed using both a discrete event simulation and a Markov chain approach. The queueing network consists of two queueing nodes where there is some strategic managerial behaviour that relates to how two types of individuals are routed between the two nodes. The first node acts as a buffer for one type of individuals before moving to the second node, while the second node consists of a waiting room and a service centre. Both approaches are used to obtain performance measures of the queueing system and explicit formulas are derived for the mean waiting time, the mean blocking time and the proportion of individuals within a given target time. In addition, some numeric results are presented that compare the Markov chain and discrete event simulation approaches. Consequently, this thesis describes the development and application of a 3-player game theoretic model between two such queueing networks and a service that distributes individuals to them. In particular the game is then reduced to a 2-player normal-form game. The resultant model is used to explore dynamics between all players. A backwards induction technique is used to get the utilities of the normal-form game between the two queueing systems. The particular game is then applied to a healthcare scenario to capture the emergent behaviour between the EMS and two EDs. The results and outcomes that are produced ii by various instances of the game are then analysed and discussed. The learning algorithm replicator dynamics is used to explore the evolutionary behaviours that emerge in the game. In particular, the behaviour that naturally emerges from the game seems to be one that causes more blockage and includes less cooperation. Several ways to escape this learned inefficient behaviour are discussed. Finally, the thesis explores an extension of the queueing theoretic model that allows servers to choose their own service speed. This is implemented using an agent-based simulation approach. The agent-based model is then used in conjunction with a reinforcement learning algorithm to explore the effect that the servers’ behaviour has on the overall performance of the system