1,607 research outputs found
vSkyConf: Cloud-assisted Multi-party Mobile Video Conferencing
As an important application in the busy world today, mobile video
conferencing facilitates virtual face-to-face communication with friends,
families and colleagues, via their mobile devices on the move. However, how to
provision high-quality, multi-party video conferencing experiences over mobile
devices is still an open challenge. The fundamental reason behind is the lack
of computation and communication capacities on the mobile devices, to scale to
large conferencing sessions. In this paper, we present vSkyConf, a
cloud-assisted mobile video conferencing system to fundamentally improve the
quality and scale of multi-party mobile video conferencing. By novelly
employing a surrogate virtual machine in the cloud for each mobile user, we
allow fully scalable communication among the conference participants via their
surrogates, rather than directly. The surrogates exchange conferencing streams
among each other, transcode the streams to the most appropriate bit rates, and
buffer the streams for the most efficient delivery to the mobile recipients. A
fully decentralized, optimal algorithm is designed to decide the best paths of
streams and the most suitable surrogates for video transcoding along the paths,
such that the limited bandwidth is fully utilized to deliver streams of the
highest possible quality to the mobile recipients. We also carefully tailor a
buffering mechanism on each surrogate to cooperate with optimal stream
distribution. We have implemented vSkyConf based on Amazon EC2 and verified the
excellent performance of our design, as compared to the widely adopted unicast
solutions.Comment: 10 page
A note on the data-driven capacity of P2P networks
We consider two capacity problems in P2P networks. In the first one, the
nodes have an infinite amount of data to send and the goal is to optimally
allocate their uplink bandwidths such that the demands of every peer in terms
of receiving data rate are met. We solve this problem through a mapping from a
node-weighted graph featuring two labels per node to a max flow problem on an
edge-weighted bipartite graph. In the second problem under consideration, the
resource allocation is driven by the availability of the data resource that the
peers are interested in sharing. That is a node cannot allocate its uplink
resources unless it has data to transmit first. The problem of uplink bandwidth
allocation is then equivalent to constructing a set of directed trees in the
overlay such that the number of nodes receiving the data is maximized while the
uplink capacities of the peers are not exceeded. We show that the problem is
NP-complete, and provide a linear programming decomposition decoupling it into
a master problem and multiple slave subproblems that can be resolved in
polynomial time. We also design a heuristic algorithm in order to compute a
suboptimal solution in a reasonable time. This algorithm requires only a local
knowledge from nodes, so it should support distributed implementations.
We analyze both problems through a series of simulation experiments featuring
different network sizes and network densities. On large networks, we compare
our heuristic and its variants with a genetic algorithm and show that our
heuristic computes the better resource allocation. On smaller networks, we
contrast these performances to that of the exact algorithm and show that
resource allocation fulfilling a large part of the peer can be found, even for
hard configuration where no resources are in excess.Comment: 10 pages, technical report assisting a submissio
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