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 problem of designing adaptive component-level thresholds is analyzed in @cite_17 for a general context of autonomic computing. The mechanism proposed in the paper consists in monitoring the threshold values in use by keeping track of false alarms with respect to possible violations of service level agreements. A regression model is used to to fit the observed history. When a sufficiently confident fit is attained the thresholds are calculated accordingly. On the contrary if the required confidence is not attained, the thresholds are set to random values as if there was no history. A critical problem of this proposal is the fact that the most common threshold policies cause on off behaviors that often result in unacceptable performance. Our proposal is instead based on a probabilistic approach and on a learning technique, that dynamically creates a knowledge basis for the online evaluation of the best decision to make even for traffic situations that never occurred in the past history.
@cite_17
1875956924
Threshold violations reported for system components signal undesirable conditions in the system. In complex computer systems, characterized by dynamically changing workload patterns and evolving business goals, the pre-computed performance thresholds on the operational values of performance metrics of individual system components are not available. This paper focuses on a fundamental enabling technology for performance management: automatic computation and adaptation of statistically meaningful performance thresholds for system components. We formally define the problem of adaptive threshold setting with controllable accuracy of the thresholds and propose a novel algorithm for solving it. Given a set of Service Level Objectives (SLOs) of the applications executing in the system, our algorithm continually adapts the per-component performance thresholds to the observed SLO violations. The purpose of this continual threshold adaptation is to control the average amounts of false positive and false negative alarms to improve the efficacy of the threshold-based management. We implemented the proposed algorithm and applied it to a relatively simple, albeit non-trivial, storage system. In our experiments we achieved a positive predictive value of 92 and a negative predictive value of 93 for component level performance thresholds Structure from Motion (SfM) algorithms take as input multi-view stereo images (along with internal calibration information) and yield a 3D point cloud and camera orientations poses in a common 3D coordinate system. In the case of an incremental SfM pipeline, the process requires repeated model estimations based on detected feature points: homography, fundamental and essential matrices, as well as camera poses. These estimations have a crucial impact on the quality of 3D reconstruction. We propose to improve these estimations using the a contrario methodology. While SfM pipelines usually have globally-fixed thresholds for model estimation, the a contrario principle adapts thresholds to the input data and for each model estimation. Our experiments show that adaptive thresholds reach a significantly better precision. Additionally, the user is free from having to guess thresholds or to optimistically rely on default values. There are also cases where a globally-fixed threshold policy, whatever the threshold value is, cannot provide the best accuracy, contrary to an adaptive threshold policy. In this paper we present a novel method for foreground segmentation. Our proposed approach follows a non-parametric background modeling paradigm, thus the background is modeled by a history of recently observed pixel values. The foreground decision depends on a decision threshold. The background update is based on a learning parameter. We extend both of these parameters to dynamic per-pixel state variables and introduce dynamic controllers for each of them. Furthermore, both controllers are steered by an estimate of the background dynamics. In our experiments, the proposed Pixel-Based Adaptive Segmenter (PBAS) outperforms most state-of-the-art methods. Convolutional low-density parity-check (LDPC) ensembles, introduced by Felstrom and Zigangirov, have excellent thresholds and these thresholds are rapidly increasing functions of the average degree. Several variations on the basic theme have been proposed to date, all of which share the good performance characteristics of convolutional LDPC ensembles. We describe the fundamental mechanism that explains why “convolutional-like” or “spatially coupled” codes perform so well. In essence, the spatial coupling of individual codes increases the belief-propagation (BP) threshold of the new ensemble to its maximum possible value, namely the maximum a posteriori (MAP) threshold of the underlying ensemble. For this reason, we call this phenomenon “threshold saturation.” This gives an entirely new way of approaching capacity. One significant advantage of this construction is that one can create capacity-approaching ensembles with an error correcting radius that is increasing in the blocklength. Although we prove the “threshold saturation” only for a specific ensemble and for the binary erasure channel (BEC), empirically the phenomenon occurs for a wide class of ensembles and channels. More generally, we conjecture that for a large range of graphical systems a similar saturation of the “dynamical” threshold occurs once individual components are coupled sufficiently strongly. This might give rise to improved algorithms and new techniques for analysis.