1,721,168 research outputs found
On Next-Generation Telco-Managed P2P TV Architectures
Full text linkMeeyoung Cha did this work while visiting University of
Cambridge, as an intern at Telefonica Research. Sue Moon was
supported by KOSEF through AITrc
Network Science, Web Science, and Internet Science
Full text linkIn this paper, we examine the fields of Network Science, Web Science and Internet Science. All three areas are interdisciplinary, and since the Web is based on the Internet, and both the Web and the Internet are networks, there is perhaps confusion about the relationship between them. We study the extent of overlap and ask whether one includes the others, or whether they are all part of the same larger domain. This paper provides an account of the emergence of each of these areas and outlines a framework for comparison. Based on this framework, we discuss these overlaps and propose directions for harmonization of research activities
A survey of opportunistic offloading
No full textThis paper surveys the literature of opportunistic offloading. Opportunistic offloading refers to offloading traffic originally transmitted through the cellular network to opportunistic network, or offloading computing tasks originally executed locally to nearby devices with idle computing resources through opportunistic network. This research direction is recently emerged, and the relevant research covers the period from 2009 to date, with an explosive trend over the last four years. We provide a comprehensive review of the research field from a multi-dimensional view based on application goal, realizing approach, offloading direction, etc. In addition, we pinpoint the major classifications of opportunistic offloading, so as to form a hierarchical or graded classification of the existing works. Specifically, we divide opportunistic offloading into two main categories based on application goal: traffic offloading or computation offloading. Each category is further divided into two smaller categories: with and without offloading node selection, which bridges between subscriber node and the cellular network, or plays the role of computing task executor for other nodes. We elaborate, compare and analyze the literatures in each classification from the perspectives of required information, objective, etc. We present a complete introductory guide to the researches relevant to opportunistic offloading. After summarizing the development of the research direction and offloading strategies of the current state-of-the-art, we further point out the important future research problems and directions.</p
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Distributed virtual environment scalability and security
Full text linkDistributed virtual environments (DVEs) have been an active area of research and engineering for more than 20 years. The most widely deployed DVEs are network games such as Quake, Halo, and World of Warcraft (WoW), with millions of users and billions of dollars in annual revenue. Deployed DVEs remain expensive centralized implementations despite significant research outlining ways to distribute DVE workloads.
This dissertation shows previous DVE research evaluations are inconsistent with deployed DVE needs. Assumptions about avatar movement and proximity - fundamental scale factors - do not match WoW’s workload, and likely the workload of other deployed DVEs. Alternate workload models are explored and preliminary conclusions presented. Using realistic workloads it is shown that a fully decentralized DVE cannot be deployed to today’s consumers, regardless of its overhead.
Residential broadband speeds are improving, and this limitation will eventually disappear. When it does, appropriate security mechanisms will be a fundamental requirement for technology adoption.
A trusted auditing system (“Carbon”) is presented which has good security, scalability, and resource characteristics for decentralized DVEs. When performing exhaustive auditing, Carbon adds 27% network overhead to a decentralized DVE with a WoW-like workload. This resource consumption can be reduced significantly, depending upon the DVE’s risk tolerance.
Finally, the Pairwise Random Protocol (PRP) is described. PRP enables adversaries to fairly resolve probabilistic activities, an ability missing from most decentralized DVE security proposals.
Thus, this dissertations contribution is to address two of the obstacles for deploying research on decentralized DVE architectures. First, lack of evidence that research results apply to existing DVEs. Second, the lack of security systems combining appropriate security guarantees with acceptable overhead
The Fairness Challenge in Computer Networks
Full text linkIn this paper, the concept of fairness in computer networks is investigated. We motivate the need of examining fairness issues by providing example future application scenarios where fairness support is needed in order to experience sufficient service quality. Fairness definitions from political science and their application to computer networks are described and a state-of-the-art overview of research activities in fairness, from issues such a queue management and tcp-friendliness to issues like fairness in layered multi-rate multicast scenarios, is given. We contribute with this paper to the ongoing research activities by defining the fairness challenge with the purpose of helping direct future investigations to with spots on the map of research in fairness
Federated split GANs
No full textMobile devices and the immense amount and variety of data they generate are key enablers of machine learning (ML)-based applications. Traditional ML techniques have shifted toward new paradigms such as federated learning (FL) and split learning (SL) to improve the protection of user's data privacy. However, SL often relies on server(s) located in the edge or cloud to train computationally-heavy parts of an ML model to avoid draining the limited resource on client devices, potentially resulting in exposure of device data to such third parties. This work proposes an alternative approach to train computationally heavy ML models in user's devices themselves, where corresponding device data resides. Specifically, we focus on GANs (generative adversarial networks) and leverage their network architecture to preserve data privacy. We train the discriminative part of a GAN on user's devices with their data, whereas the generative model is trained remotely (e.g., server) for which there is no need to access device true data. Moreover, our approach ensures that the computational load of training the discriminative model is shared among user's devices - proportional to their computation capabilities - by means of SL. We implement our proposed collaborative training scheme of a computationally-heavy GAN model in simulated resource-constrained devices. The results show that our system preserves data privacy, keeps a short training time, and yields the same model accuracy as when the model is trained on devices with unconstrained resources (e.g., cloud)
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Social network support for data delivery infrastructures
Full text linkNetwork infrastructures often need to stage content so that it is accessible to consumers. The standard solution, deploying the content on a centralised server, can be inadequate in several situations.
Our thesis is that information encoded in social networks can be used to tailor content staging decisions to the user base and thereby build better data delivery infrastructures. This claim is supported by two case studies, which apply social information in challenging situations where traditional content staging is infeasible. Our approach works by examining empirical traces to identify relevant social properties, and then exploits them.
The first study looks at cost-effectively serving the ``Long Tail'' of rich-media user-generated content, which need to be staged close to viewers to control latency and jitter. Our traces show that a preference for the unpopular tail items often spreads virally and is localised to some part of the social network. Exploiting this, we propose Buzztraq, which decreases replication costs by selectively copying items to locations favoured by viral spread. We also design SpinThrift, which separates popular and unpopular content based on the relative proportion of viral accesses, and opportunistically spins down disks containing unpopular content, thereby saving energy.
The second study examines whether human face-to-face contacts can efficiently create paths over time between arbitrary users. Here, content is staged by spreading it through intermediate users until the destination is reached. Flooding every node minimises delivery times but is not scalable. We show that the human contact network is resilient to individual path failures, and for unicast paths, can efficiently approximate flooding in delivery time distribution simply by randomly sampling a handful of paths found by it. Multicast by contained flooding within a community is also efficient. However, connectivity relies on rare contacts and frequent contacts are often not useful for data delivery.
Also, periods of similar duration could achieve different levels of connectivity; we devise a test to identify good periods. We finish by discussing how these properties influence routing algorithms
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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