Related papers: Academic Torrents: Scalable Data Distribution
Volunteer computing has been known as an alternative solution to solve complex problems. It is acknowledged for its simplicity and its ability to work on multiple operating systems. Nonetheless, setting up a server for volunteer computing…
The increasing popularity of academic social networking sites (ASNSs) requires studies on the usage of ASNSs among scholars and evaluations of the effectiveness of these ASNSs. However, it is unclear whether current ASNSs have fulfilled…
The proliferation of peer-to-peer (P2P) file sharing protocols is due to their efficient and scalable methods for data dissemination to numerous users. But many of these networks have no provisions to provide users with long term access to…
Peer-to-Peer (P2P) file-sharing is becoming increasingly popular in recent years. In 2012, it was reported that P2P traffic consumed over 5,374 petabytes per month, which accounted for approximately 20.5% of consumer internet traffic. TV is…
A text stream is an ordered sequence of text documents generated over time. A massive amount of such text data is generated by online social platforms every day. Designing an algorithm for such text streams to extract useful information is…
Peer-to-peer (P2P) locality has recently raised a lot of interest in the community. Indeed, whereas P2P content distribution enables financial savings for the content providers, it dramatically increases the traffic on inter-ISP links. To…
Academic data sharing is a way for researchers to collaborate and thereby meet the needs of an increasingly complex research landscape. It enables researchers to verify results and to pursuit new research questions with "old" data. It is…
Transfer learning, in which a network is trained on one task and re-purposed on another, is often used to produce neural network classifiers when data is scarce or full-scale training is too costly. When the goal is to produce a model that…
It is now well known that deep neural networks (DNNs) are vulnerable to adversarial attack. Adversarial samples are similar to the clean ones, but are able to cheat the attacked DNN to produce incorrect predictions in high confidence. But…
Peer-to-peer (P2P) locality has recently raised a lot of interest in the community. Indeed, whereas P2P content distribution enables financial savings for the content providers, it dramatically increases the traffic on inter-ISP links. To…
Deep networks are frequently tuned to novel tasks and continue learning from ongoing data streams. Such sequential training requires consolidation of new and past information, a challenge predominantly addressed by retaining the most…
A few months ago, the BitTorrent developers announced that the transfer of torrent data in the official client was about to switch to uTP, an application-layer congestion-control protocol using UDP at the transport-layer. This announcement…
Distributed storage systems are mainly justified due to the limited amount of storage capacity and improving the reliability through distributing data over multiple storage nodes. On the other hand, it may happen the data is stored in…
Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving…
BitTorrent is undoubtedly the most popular P2P file sharing application on today's Internet. The widespread popularity of BitTorrent has attracted a great deal of attention from networking researchers who conducted various performance…
Transferring a deep neural network trained on one problem to another requires only a small amount of data and little additional computation time. The same behaviour holds for ensembles of deep learning models typically superior to a single…
Several major Internet service providers (e.g., Level-3, AT&T, Verizon) today also offer content distribution services. The emergence of such "Network-CDNs" (NCDNs) are driven by market forces that place more value on content services than…
Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient memory and storage, as well as independent and identically…
Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, then averaging the gradients. Downloading models and uploading…
Despite the tremendous success of BitTorrent, its swarming system suffers from a fundamental limitation: lower or no availability of unpopular contents. Recently, Menasche et al. has shown that bundling is a promising solution to mitigate…