4,673 research outputs found
Identifying immersive environments’ most relevant research topics: an instrument to query researchers and practitioners
This paper provides an instrument for ascertaining researchers’ perspectives on the relative relevance of technological challenges facing immersive environments in view of their adoption in learning contexts, along three dimensions: access, content production, and deployment. It described its theoretical grounding and expert-review process, from a set of previously-identified challenges and expert feedback cycles. The paper details the motivation, setup, and methods employed, as well as the issues detected in the cycles and how they were addressed while developing the instrument. As a research instrument, it aims to be employed across diverse communities of research and practice, helping direct research efforts and hence contribute to wider use of immersive environments in learning, and possibly contribute towards the development of news
and more adequate systems.The work presented herein has been partially funded under the European H2020 program H2020-ICT-2015, BEACONING project, grant agreement nr. 687676.info:eu-repo/semantics/publishedVersio
Decentralized Adaptive Helper Selection in Multi-channel P2P Streaming Systems
In Peer-to-Peer (P2P) multichannel live streaming, helper peers with surplus
bandwidth resources act as micro-servers to compensate the server deficiencies
in balancing the resources between different channel overlays. With deployment
of helper level between server and peers, optimizing the user/helper topology
becomes a challenging task since applying well-known reciprocity-based choking
algorithms is impossible due to the one-directional nature of video streaming
from helpers to users. Because of selfish behavior of peers and lack of central
authority among them, selection of helpers requires coordination. In this
paper, we design a distributed online helper selection mechanism which is
adaptable to supply and demand pattern of various video channels. Our solution
for strategic peers' exploitation from the shared resources of helpers is to
guarantee the convergence to correlated equilibria (CE) among the helper
selection strategies. Online convergence to the set of CE is achieved through
the regret-tracking algorithm which tracks the equilibrium in the presence of
stochastic dynamics of helpers' bandwidth. The resulting CE can help us select
proper cooperation policies. Simulation results demonstrate that our algorithm
achieves good convergence, load distribution on helpers and sustainable
streaming rates for peers
The evaluation of an active networking approach for supporting the QOS requirements of distributed virtual environments
This paper describes work that is part of a more general investigation into how Active Network ideas
might benefit large scale Distributed-Virtual-Environments (DVEs). Active Network approaches have been
shown to offer improved solutions to the Scalable Reliable Multicast problem, and this is in a sense the lowest
level at which Active Networks might benefit DVEs in supporting the peer-to-peer architectures considered
most promising for large scale DVEs. To go further than this, the key benefit of Active Networking is the ability
to take away from the application the need to understand the network topology and delegate the execution of
certain actions, for example intelligent message pruning, to the network itself. The need to exchange geometrical
information results in a type of traffic that can place occasional, short-lived, but heavy loads on the network.
However, the Level of Detail (LoD) concept provides the potential to reduce this loading in certain circumstances.
This paper introduces the performance modelling approach being used to evaluate the effectiveness of
active network approaches for supporting DVEs and presents an evaluation of messages filtering mechanisms,
which are based on the (LoD) concept. It describes the simulation experiment used to carry out the evaluation,
presents its results and discusses plans for future work
EGOIST: Overlay Routing Using Selfish Neighbor Selection
A foundational issue underlying many overlay network applications ranging from routing to P2P file sharing is that of connectivity management, i.e., folding new arrivals into an existing overlay, and re-wiring to cope with changing network conditions. Previous work has considered the problem from two perspectives: devising practical heuristics for specific applications designed to work well in real deployments, and providing abstractions for the underlying problem that are analytically tractable, especially via game-theoretic analysis. In this paper, we unify these two thrusts by using insights gleaned from novel, realistic theoretic models in the design of Egoist – a prototype overlay routing system that we implemented, deployed, and evaluated on PlanetLab. Using measurements on PlanetLab and trace-based simulations, we demonstrate that Egoist's neighbor selection primitives significantly outperform existing heuristics on a variety of performance metrics, including delay, available bandwidth, and node utilization. Moreover, we demonstrate that Egoist is competitive with an optimal, but unscalable full-mesh approach, remains highly effective under significant churn, is robust to cheating, and incurs minimal overhead. Finally, we discuss some of the potential benefits Egoist may offer to applications.National Science Foundation (CISE/CSR 0720604, ENG/EFRI 0735974, CISE/CNS 0524477, CNS/NeTS 0520166, CNS/ITR 0205294; CISE/EIA RI 0202067; CAREER 04446522); European Commission (RIDS-011923
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