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.
The application of the autonomic computing paradigm to the problem of overload control in web systems poses some key problems concerning the design of the monitoring module. The authors of @cite_12 propose a technique for learning dynamic patterns of web user behavior. A finite state machine representing the typical user behavior is constructed on the basis of past history and used for prediction and prefetching techniques. In paper @cite_5 the problem of delay prediction is analyzed on the basis of a learning activity exploiting passive measurements of query executions. Such predictive capability is exploited to enhance traditional query optimizers.
@cite_5 @cite_12
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We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval. 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. We study the problem of optimizing the performance of a system shared by selfish, noncooperative users. We consider the concrete setting of scheduling small jobs on a set of shared machines possessing latency functions that specify the amount of time needed to complete a job, given the machine load. We measure system performance by the total latency of the system. Assigning jobs according to the selfish interests of individual users, who wish to minimize only the latency that their own jobs experience, typically results in suboptimal system performance. However, in many systems of this type there is a mixture of "selfishly controlled" and "centrally controlled" jobs. The congestion due to centrally controlled jobs will influence the actions of selfish users, and we thus aspire to contain the degradation in system performance due to selfish behavior by scheduling the centrally controlled jobs in the best possible way. We formulate this goal as an optimization problem via Stackelberg games, games in which one player acts a leader (here, the centralized authority interested in optimizing system performance) and the rest as followers (the selfish users). The problem is then to compute a strategy for the leader (a Stackelberg strategy) that induces the followers to react in a way that (approximately) minimizes the total latency in the system. In this paper, we prove that it is NP-hard to compute an optimal Stackelberg strategy and present simple strategies with provably good performance guarantees. More precisely, we give a simple algorithm that computes a strategy inducing a job assignment with total latency no more than a constant times that of the optimal assignment of all of the jobs; in the absence of centrally controlled jobs and a Stackelberg strategy, no result of this type is possible. We also prove stronger performance guarantees in the special case where every machine latency function is linear in the machine load. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users.