With significant increases in mobile device traffic slated for the foreseeable future, numerous technologies must be embraced to satisfy such demand. Notably, one of the more intriguing approaches has been blending on-device caching and device-to-device (D2D) communications. While various past research has pointed to potentially significant gains (30 +) via redundancy elimination (RE), some skepticism has emerged to whether or not such gains are truly harnessable in practice. The premise of this paper is to explore whether or not significant potential for redundancy elimination exists and whether the rise of video and encryption might blunt said efforts. Critically, we find that absent significant synchronized interests of mobile users, the actual redundancy falls well short of the promising values from the literature. In our paper, we investigate the roots for said shortcomings by exploring RE savings with regards to cache hit characteristics and to what extent client and domain diversity contribute to the realized redundancy savings.
Research efforts in network redundancy detection and elimination can be categorized along two dimensions: (1) the granularity at which redundancy is captured and (2) the location where RE components are deployed. For the first dimension, web proxies emerged in the early nineties as a solution to reduce bandwidth consumption by eliminating retransmissions of the same object (e.g. web objects). Conventional approaches tended to index objects by name, thereby suffering from the drawback that documents with different URIs but the same content would be treated as different objects. To overcome this problem, later efforts such as Bahn et. al @cite_6 and @cite_16 applied similarity detection on object content for improved efficiency. While web proxies operated on objects, the notion of detecting content similarity was extended to packet-level approaches in the work by Spring @cite_8 . Compared to proxy-based techniques, packet-level solutions bear the advantages of operating in a protocol-independent manner and being able to detect similarity across web objects that are not cacheable.
@cite_16 @cite_6 @cite_8
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We present a technique for identifying repetitive information transfers and use it to analyze the redundancy of network traffic. Our insight is that dynamic content, streaming media and other traffic that is not caught by today's Web caches is nonetheless likely to derive from similar information. We have therefore adapted similarity detection techniques to the problem of designing a system to eliminate redundant transfers. We identify repeated byte ranges between packets to avoid retransmitting the redundant data. We find a high level of redundancy and are able to detect repetition that Web proxy caches are not. In our traces, after Web proxy caching has been applied, an additional 39 of the original volume of Web traffic is found to be redundant. Moreover, because our technique makes no assumptions about HTTP protocol syntax or caching semantics, it provides immediate benefits for other types of content, such as streaming media, FTP traffic, news and mail. This paper addresses two unresolved issues about Web caching. The first issue is whether Web requests from a fixed user community are distributed according to Zipf's (1929) law. The second issue relates to a number of studies on the characteristics of Web proxy traces, which have shown that the hit-ratios and temporal locality of the traces exhibit certain asymptotic properties that are uniform across the different sets of the traces. In particular, the question is whether these properties are inherent to Web accesses or whether they are simply an artifact of the traces. An answer to these unresolved issues will facilitate both Web cache resource planning and cache hierarchy design. We show that the answers to the two questions are related. We first investigate the page request distribution seen by Web proxy caches using traces from a variety of sources. We find that the distribution does not follow Zipf's law precisely, but instead follows a Zipf-like distribution with the exponent varying from trace to trace. Furthermore, we find that there is only (i) a weak correlation between the access frequency of a Web page and its size and (ii) a weak correlation between access frequency and its rate of change. We then consider a simple model where the Web accesses are independent and the reference probability of the documents follows a Zipf-like distribution. We find that the model yields asymptotic behaviour that are consistent with the experimental observations, suggesting that the various observed properties of hit-ratios and temporal locality are indeed inherent to Web accesses observed by proxies. Finally, we revisit Web cache replacement algorithms and show that the algorithm that is suggested by this simple model performs best on real trace data. The results indicate that while page requests do indeed reveal short-term correlations and other structures, a simple model for an independent request stream following a Zipf-like distribution is sufficient to capture certain asymptotic properties observed at Web proxies. This paper presents an efficient approach for copies detection in a large videos archive consisting of several hundred of hours. The video content indexing method consists of extracting the dynamic behavior on the local description of interest points and further on the estimation of their trajectories along the video sequence. Analyzing the low-level description obtained allows to highlight trends of behaviors and then to assign a label of behavior to each local descriptor. Such an indexing approach has several interesting properties: it provides a rich, compact and generic description, while labels of behavior provide a high-level description of the video content. Here, we focus on video Content Based Copy Detection (CBCD). Copy detection is problematic as similarity search problem but with prominent differences. To be efficient, it requires a dedicated on-line retrieval method based on a specific voting function. This voting function must be robust to signal transformations and discriminating versus high similarities which are not copies. The method we propose in this paper is a dedicated on-line retrieval method based on a combination of the different dynamic contexts computed during the off-line indexing. A spatio-temporal registration based on the relevant combination of detected labels is then applied. This approach is evaluated using a huge video database of 300 hours with different video tests. The method is compared to a state-of-the art technique in the same conditions. We illustrate that taking labels into account in the specific voting process reduces false alarms significantly and drastically improves the precision. In recent years, we have seen tremendous progress in the field of object detection. Most of the recent improvements have been achieved by targeting deeper feedforward networks. However, many hard object categories such as bottle, remote, etc. require representation of fine details and not just coarse, semantic representations. But most of these fine details are lost in the early convolutional layers. What we need is a way to incorporate finer details from lower layers into the detection architecture. Skip connections have been proposed to combine high-level and low-level features, but we argue that selecting the right features from low-level requires top-down contextual information. Inspired by the human visual pathway, in this paper we propose top-down modulations as a way to incorporate fine details into the detection framework. Our approach supplements the standard bottom-up, feedforward ConvNet with a top-down modulation (TDM) network, connected using lateral connections. These connections are responsible for the modulation of lower layer filters, and the top-down network handles the selection and integration of contextual information and low-level features. The proposed TDM architecture provides a significant boost on the COCO testdev benchmark, achieving 28.6 AP for VGG16, 35.2 AP for ResNet101, and 37.3 for InceptionResNetv2 network, without any bells and whistles (e.g., multi-scale, iterative box refinement, etc.).