Abstract— during recent years, the Internet
has witnessed a rapid growth in deployment of data-driven (or swarming based)
peer-to-peer (P2P) media streaming. In these applications, each node
independently selects some other nodes as its neighbors (i.e., gossip style
overlay construction) and exchanges streaming data with the neighbors (i.e.,
data scheduling). To improve the performance of such protocol, many existing works
focus on the gossip-style overlay construction issue. However, few of them
concentrate on optimizing the streaming data scheduling to maximize the
throughput of a constructed overlay. In this paper, we analytically study the
scheduling problem in data-driven streaming system and model it as a classical
min-cost network flow problem. We then propose both the global optimal
scheduling scheme and distributed heuristic algorithm to optimize the system
throughput. Furthermore, we introduce layered video coding into data-driven
protocol and extend our algorithm to deal with the end-host heterogeneity. The
results of simulation with the real-world traces indicate that our distributed
algorithm significantly outperforms conventional ad hoc scheduling strategies especially
in stringent buffer and bandwidth constraints.
Existing system:
The basic idea of
data-driven streaming protocol is very simple and similar to that of
Bit-Torrent. The protocol contains two steps. In the first step, each node
independently selects its neighbors so as to form an unstructured overlay
network, called the gossip-style overlay construction or
membership management. The second step is named block scheduling: The live media content is divided into blocks (or segments or packets),
and every node announces what blocks it has to its neighbors. Then each node explicitly
requests the blocks of interest from its neighbors according to their
announcement. Obviously, the performance of data-driven protocol directly
relies on the algorithms in these two steps To improve the performance of
data-driven protocol, most of the recent papers focused on the construction problem
of the first step. Researchers proposed different schemes to build unstructured
overlays to improve its efficiency or robustness. However, the second step,
i.e., the block scheduling has not been well discussed in the literature yet.
The scheduling methods used in most
of the pioneering works
with respect to the data-driven/ swarming-based streaming are somewhat ad hoc.
These conventional scheduling strategies mainly include pure random strategy,
local rarest first (LRF) strategy and round-robin strategy.
Proposed System:
In this paper, we present our
analytical model and corresponding solutions to tackle the block scheduling problem
in data-driven protocol. We first model this scheduling problem as a classical
min-cost network flow problem and propose a global optimal solution in order to
find out the ideal throughput improvement in theory. Since this solution is
centralized and requires global knowledge, based on its basic idea, we then
propose a heuristic algorithm that is fully distributed and asynchronous with
only local information exchange. Furthermore, we employ layered video coding to
encode the video into multiple rates and extend our algorithm to improve the
satisfaction of the users with heterogeneous access bandwidth. Simulation
results indicate that our distributed algorithm significantly outperforms other
different conventional ad hoc scheduling strategy gains in both of the single
rate and multirate scenarios.
SYSTEM
REQUIREMENT
Hardware
Requirements
Processor : Pentium III / IV
Hard
Disk : 40 GB
Ram : 256 MB
Monitor : 15VGA Color
Mouse : Ball / Optical
Keyboard : 102 Keys
Software Requirements
Operating System : Windows
XP professional
Front End : Microsoft Visual Studio .Net 2005
Language : Visual C#.Net
Back End : SQL Server 2000
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