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Lixiang Xu

Bio: Lixiang Xu is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Cache & Cache algorithms. The author has an hindex of 3, co-authored 8 publications receiving 27 citations.

Papers
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Journal ArticleDOI
TL;DR: An approach to choose the cached videos under the time-varying user behavior and a new segmentation approach, which makes the storage granularity independent from the management granularity and can make a better use of the cache space are proposed.
Abstract: With the fast progresses of network technology, Video-On-Demand (VOD) service has found more and more applications. The transmission of multimedia files places heavy burdens on the Internet owing to their large sizes. To resolve this issue, caching servers are deployed at the edge of the Internet to meet most needs of local users by caching some popular videos. This paper provides an approach to choose the cached videos under the time-varying user behavior. Our approach estimates the average access intervals of a video with an Exponential Weighted Moving Average (EWMA) approach and furthermore predicts the video's future popularity based on its historical access intervals. The forgetting and predicting operations enable the algorithm to not only track the change of the time-varying user accesses, but also reduce the effects of the randomness of a single user access on the caching performance. In addition, we propose a new segmentation approach, which makes the storage granularity independent from the management granularity and can make a better use of the cache space. Simulation results show that our segmentation approach has a higher Byte-Hit Ratio than uniform segmentation and chunk segmentation, and our caching algorithm outperforms Least Recently Used (LRU), Least Frequently Used (LFU) and EWMA.

13 citations

Journal ArticleDOI
TL;DR: A Feedback-based Adaptive Data Migration (FADM) method is proposed, which can utilize the real-time feedback of the write load of SSDs to adjust the rule of moving data between HDDs and SSDs.
Abstract: Nowadays Video-On-Demand (VOD) caching systems are often equipped with hybrid storage devices, which have been designed to combine the high read speed of Solid State Disks (SSDs) and the large capacity of Hard Disk Drives (HDDs). However, the number of erase cycles of SSDs is limited. So it is important to control the write load of SSDs in real applications. This paper proposes a Feedback-based Adaptive Data Migration (FADM) method, which can utilize the real-time feedback of the write load of SSDs to adjust the rule of moving data between HDDs and SSDs. More specifically, a video in HDDs is allowed to be moved into SSDs when its popularity is higher than that of the least popular video in SSDs by a threshold. This threshold is adaptively adjusted according to the feedback of the write load of SSDs. With FADM, the desired lifetime of SSDs can be well guaranteed even under various user behaviors while good read performance can be maintained. Simulations are done to demonstrate the effectiveness of FADM.

7 citations

Patent
18 Sep 2013
TL;DR: In this article, a moped detection method based on multi-feature and multi-frame information fusion comprises two stages, the first stage includes detecting objects and extracting feature information of moving objects in single-frame images for fusion to obtain target classification judgments of single frames, and the second stage includes fusing the judgments of multiframe images to obtain overall target classification judgment results.
Abstract: A moped detection method based on multi-feature and multi-frame information fusion comprises two stages. The first stage includes detecting objects and extracting feature information of moving objects in single-frame images for fusion to obtain target classification judgments of single frames, and the second stage includes fusing the judgments of multi-frame images to obtain overall target classification judgment results. The moped detection method is independent of extra hardware equipment and capable of processing poor-quality small-size scene-mixed images obtained in actual video monitoring. By means of the method, the detection rate of a system is improved, the omission ratio is reduced, the detection speed is increased, and real-time detection requirements are met.

5 citations

Patent
15 Jan 2014
TL;DR: In this paper, an image processing method based on fed-back moving object segmentation is proposed, which comprises the steps that a background is modeled, a model is updated through the dual-layer background, the background is updated with the low updating rate in the first layer to adapt to the slow change of the background, and acceleration and compensation operations are carried out on the background according to the feedback of the high-layer information in the second layer.
Abstract: The invention discloses an image processing method based on fed back moving object segmentation. The method comprises the steps that a background is modeled, a model is updated through the dual-layer background, the background is updated with the low updating rate in the first layer to adapt to the slow change of the background, and acceleration and compensation operations are carried out on the background according to the feedback of the high-layer information in the second layer to adapt to the sudden change of the object movement in a scene; a foreground is segmented, predicated moving object blocks are combined according to the feedback of the high-layer information, the segmentation threshold values are adjusted in a self-adaptation mode in a predicated object area, and the purposes of restraining noise and preventing the segmented foreground object from forming a cavity and separation are achieved. According to the processing method, the robustness of the model can be kept and the sensibility of the model to the foreground object abnormal movement can be kept through the background modeling, the noise can be restrained well and foreground cavity and segmentation can be prevented through the foreground segmentation.

3 citations

Patent
18 Sep 2013
TL;DR: In this article, a moped detection method based on multiple Gaussian models was proposed, where the size and the speed of the moving objects are two important characteristics, but the sizes and speed vary along with changes of the angle and the distance of cameras.
Abstract: The invention provides a moped detection method based on multiple Gaussian models. According to the method, mopeds are recognized by extracting external characteristics and motion characteristics of moving objects. The size and the speed of the moving objects are two important characteristics, but the size and the speed vary along with changes of the angle and the distance of cameras. Therefore, two groups of Gaussian models are established at different pixels to reflect size and speed distribution of different kinds of motion objects. According to the method, recognition of the mopeds can still be performed when the video quality is not high and the motion objects are not large enough. The method is high in universality due to classification threshold self-adaption and low in false detection rate due to the fact that the uniform classification threshold is not adopted. A majority principle is used twice, so that interference of errors and random factors to model establishment can be suppressed.

1 citations


Cited by
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Journal Article
TL;DR: In this paper, the LFU-K page replacement algorithm is proposed, which is an improvement to the Least Frequently Used (LFU) algorithm for database disk buffering.
Abstract: This paper introduces a new approach to database disk buffering, called the LFU-K method. The LFU-K page replacement algorithm is an improvement to the Least Frequently Used (LFU) algorithm. The paper proposes a theoretical-probability model for formal description of LFU-K algorithm. Using this model we evaluate estimations for the LFU-K parameters. This paper also describes an implementation of LFU-2 policy. As we demonstrate by trace-driven simulation experiments, the LFU-2 algorithm provides significant improvement over conventional buffering algorithms for the shared-nothing database systems.

36 citations

01 Jan 2016
TL;DR: The modern control systems 10th edition is universally compatible with any devices to read and will help you to enjoy a good book with a cup of tea in the afternoon instead of coping with some harmful virus inside their desktop computer.
Abstract: Thank you for reading modern control systems 10th edition. As you may know, people have look hundreds times for their chosen novels like this modern control systems 10th edition, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some harmful virus inside their desktop computer. modern control systems 10th edition is available in our book collection an online access to it is set as public so you can get it instantly. Our digital library hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern control systems 10th edition is universally compatible with any devices to read.

21 citations

Patent
27 Jul 2016
TL;DR: In this paper, a characteristic selection based SVM cascade classifier method is proposed to identify the data types by each level of the SVM classifier and determine a best characteristic combination that effectively distinguishes the data type from others.
Abstract: The invention discloses a characteristic selection based SVM cascade classifier method comprising the following steps: accessing business data associated with games or internet videos including in an open internet environment and calculating the characteristics of the basic flow statistics of the data; determining the data type required to be identified by each level of the SVM classifier based on the method and determining a best characteristic combination that effectively distinguishes the data type from others; and finally, performing classification experiments to the original internet data flows through the use of the designed SVM cascade classifier and obtaining the classification result after a plurality of experiments. The method fully considers information gain rate and Pearson correlation coefficient indexes in characteristics selection so as to accurately decide a best characteristic combination and increase the classification performance. In addition to that, with the concept of selecting a best characteristic combination for each type of data and cooperated with an effective selection method, the method is capable of achieving higher classification accuracy.

9 citations

Proceedings ArticleDOI
01 Jul 2017
TL;DR: Simulations confirm that the adaptive EWMA method significantly outperforms conventional popularity tracking methods and can achieve nearly the best performance without prior knowledge regarding the shifting speed of hot objects requested by users.
Abstract: The users' requests for online objects in mobile networks are characterized by fast varying dynamics caused by user mobility and diversity of users who produce contents. Conventional popularity methods cannot efficiently handle such strong dynamic requests of users, because they pay little attention to the time-varying user behavior which contributes to the growth and fading of online media files. To handle this issues, this paper proposes two schemes, including sensing the popularity variation speed of the hot contents and adaptively adjusting the popularity tracking speed of the cache algorithm. For the first one, we make use of the pseudo popularity acceleration in a time window to calculate the shifting speed of hot objects. With the obtained shifting speed can we adaptively adjust the popularity tracking speed of our previous EWMA method. Note that our adaptive EWMA method can achieve nearly the best performance without prior knowledge regarding the shifting speed of hot objects requested by users. Simulations confirm that our adaptive EWMA method significantly outperforms conventional popularity tracking methods.

9 citations

Journal ArticleDOI
TL;DR: An online algorithm that decides which file to remove from cache in order to allocate capacity to the newly-requested file is proposed and it is proved that for a cache that can store up to k files, the algorithm achieves a competitive ratio of $\mathcal {O}(\log (k)$ , which is the best competitive ratio achieved by any online algorithm as shown in the literature.
Abstract: In recent years, 5G cellular networks utilization has rapidly increased and is expected to grow even more in the near future This will put the current cellular networks operators in a challenge to overcome the network’s limits to satisfy the increasing mobile data traffic and the proliferation of user demands in deploying mobile applications The deployment of cache-enabled small base stations (Femtocells) is a promising solution to reduce the backhaul traffic loading and the file-access latency and therefore decrease the cellular network operational costs Due to the limited cache capacity when compared with the number of files that can be requested by users, in this paper, we formulate the problem of minimizing the cost paid by the cellular network while satisfying the cache capacity as an integer linear program (ILP) Due to the NP-completeness of the ILP formulation and the difficulty of obtaining the file request sequence apriori in real-life scenarios, we propose an online algorithm that decides which file to remove from cache in order to allocate capacity to the newly-requested file The algorithm works on a per-request basis and does not require the knowledge of the file request sequence in advance We prove that for a cache that can store up to $k$ files, the algorithm achieves a competitive ratio of $\mathcal {O}(\log (k))$ , which is the best competitive ratio achieved by any online algorithm as shown in the literature The simulations conducted considering a single cache show that while the proposed algorithm achieves a similar hit ratio compared with widely-used replacement schemes, it can reduce the cost of the cellular network by 25%

5 citations