text: Unexpected increases in demand and most of all flash crowds are considered the bane of every web application as they may cause intolerable delays or even service unavailability. Proper quality of service policies must guarantee rapid reactivity and responsiveness even in such critical situations. Previous solutions fail to meet common performance requirements when the system has to face sudden and unpredictable surges of traffic. Indeed they often rely on a proper setting of key parameters which requires laborious manual tuning, preventing a fast adaptation of the control policies. We contribute an original Self-* Overload Control (SOC) policy. This allows the system to self-configure a dynamic constraint on the rate of admitted sessions in order to respect service level agreements and maximize the resource utilization at the same time. Our policy does not require any prior information on the incoming traffic or manual configuration of key parameters. We ran extensive simulations under a wide range of operating conditions, showing that SOC rapidly adapts to time varying traffic and self-optimizes the resource utilization. It admits as many new sessions as possible in observance of the agreements, even under intense workload variations. We compared our algorithm to previously proposed approaches highlighting a more stable behavior and a better performance.
  @cite_5: The rapid growth of the Internet and support for interoperability protocols has increased the number of Web accessible sources, WebSources. Current wrapper mediator architectures need to be extended with a wrapper cost model (WCM) for WebSources that can estimate the response time (delays) to access sources as well as other relevant statistics. In this paper, we present a Web prediction tool (WebPT), a tool that is based on learning using query feedback from WebSources. The WebPT uses dimensions time of day, day, and quantity of data, to learn response times from a particular WebSource, and to predict the expected response time (delay) for some query. Experiment data was collected from several sources, and those dimensions that were significant in estimating the response time were determined. We then trained the WebPT on the collected data, to use the three dimensions mentioned above, and to predict the response time, as well as a confidence in the prediction. We describe the WebPT learning algorithms, and report on the WebPT learning for WebSources. Our research shows that we can improve the quality of learning by tuning the WebPT features, e.g., training the WebPT using a logarithm of the input training data; including significant dimensions in the WebPT; or changing the ordering of dimensions. A comparison of the WebPT with more traditional neural network (NN) learning has been performed, and we briefly report on the comparison. We then demonstrate how the WebPT prediction of delay may be used by a scrambling enabled optimizer. A scrambling algorithm identifies some critical points of delay, where it makes a decision to scramble (modify) a plan, to attempt to hide the expected delay by computing some other part of the plan that is unaffected by the delay. We explore the space of real delay at a WebSource, versus the WebPT prediction of this delay, with respect to critical points of delay in specific plans. We identify those cases where WebPT overestimation or underestimation of the real delay results in a penalty in the scrambling enabled optimizer, and those cases where there is no penalty. Using the experimental data and WebPT learning, we test how good the WebPT is in minimizing these penalties.
  @cite_12: Autonomics or self-reorganization becomes pertinent for web-sites serving a large number of users with highly varying workloads. An important component of self-adaptation is to model the behaviour of users and adapt accordingly. This paper proposes a learning-automata based technique for model discovery. User access patterns are used to construct an FSM model of user behaviour that in turn is used for prediction and prefetching. The proposed technique uses a generalization algorithm to classify behaviour patterns into a small number of generalized classes. It has been tested on both synthetic and live data-sets and has shown a prediction hit-rate of up to 89 on a real web-site.
summary: The cited proposals @cite_5 @cite_12 can partially contribute to improve the QoS of web systems, but differently from our work, none of them directly formulate a complete autonomic solution that at the same time gives directions on how to take measures, and make corresponding admission control decisions for web cluster architectures.