Network traffic measurement and workload characterization are key steps in the workload modeling process. Much has been learned through network measurement and workload modeling in the last ten years, but new challeng...
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作者：
Peng, KunyangDong, Qunfeng
School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China
Regular expression matching as the core packet inspection engine of network systems has long been striving to be both fast in matching speed (like DFA) and scalable in storage space (like NFA). Recently, ternary conte...
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The computer vision approach involves a lot of modeling problems in preventing noise caused by sensing units such as cameras and projectors. In order to improve computer vision modeling performance, a robust modeling ...
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ISBN:
(纸本)9781509026784
The computer vision approach involves a lot of modeling problems in preventing noise caused by sensing units such as cameras and projectors. In order to improve computer vision modeling performance, a robust modeling technique must be developed for essential models in the system. The RANSAC and least median of squares (LMedS) algorithms have been widely applied in such issues. However, the performance deteriorates as the noise ratio increases and the modeling time for algorithms tends to increase in actual applications. In this study, we propose a new LMedS method based on fuzzy reinforcement learning concept for modeling of computer vision applications. The performance of the algorithm is evaluated by modeling synthetic data and camera homography experiments. Their results found the method to be effective in improving calculation time, model optimality, and robustness in modeling performance.
We address the question of strategic pricing of inter-domain traffic forwarding services provided by ISPs, which is also closely coupled with the question of how ISPs route their traffic towards their neighboring ISPs...
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In the context of network traffic analysis, we address the problem of estimating the tail index of flow (or more generally of any group) size distribution from the observation of a, sampled population of packets (indi...
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ISBN:
(纸本)9781605585116
In the context of network traffic analysis, we address the problem of estimating the tail index of flow (or more generally of any group) size distribution from the observation of a, sampled population of packets (individuals). We give an exhaustive bibliography of the existing methods and show the relations between them. The main contribution of this work is then to propose a new method to estimate the tail index from sampled data, based on the resolution of the maximum likelihood problem. To assess the performance of our method, we present a full performance evaluation based on numerical simulations, and also on a real traffic trace corresponding to internet traffic recently acquired.
We present stationary and regenerative form estimates for the gradients of the cycle variables with respect to a thinning parameter in the arrival prolcess of G/G/l queueing systems. Our estimates belong to the catego...
The performance of interactive cloud services depends heavily on which data centers handle client requests, and which wide-area paths carry traffic. While making these decisions, cloud service providers also need to w...
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We present a model, based on a network of Dx/D/1 queues, to predict the communication performance of static interconnection networks under various communication patterns. Our model predicts delay time distributions in...
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In this paper we study the behavior of a continuous time random walk (CTRW) on a stationary and ergodic time varying dynamic graph. We establish conditions under which the CTRW is a stationary and ergodic process. In ...
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The parallel scientific computing community is placing increasing emphasis on portability and scalability of programs, languages, and architectures. This creates new challenges for developers of parallel performance a...
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