Adaptive Routing of QoS-constrained Media Streams over Scalable Overlay Topologies

Current research on Internet-based distributed systems emphasizes the scalability of overlay topologies for efficient search and retrieval of data items, as well as routing amongst peers. However, most existing approaches fail to address the transport of data across these logical networks in accordance with quality of service (QoS) constraints. Consequently, this paper investigates the use of scalable overlay topologies for routing real-time media streams between publishers and potentially many thousands of subscribers. Specifically, we analyze the costs of using k-ary n-cubes for QoS-constrained routing. Given a number of nodes in a distributed system, we calculate the optimal k-ary n-cube structure for minimizing the average distance between any pair of nodes. Using this structure, we describe a greedy algorithm that selects paths between nodes in accordance with the real-time delays along physical links. We show this method improves the routing latencies by as much as 67%, compared to approaches that do not consider physical link costs. We are in the process of developing a method for adaptive node placement in the overlay topology, based upon the locations of publishers, subscribers, physical link costs and per-subscriber QoS constraints. One such method for repositioning nodes in logical space is discussed, to improve the likelihood of meeting service requirements on data routed between publishers and subscribers. Future work will evaluate the benefits of such techniques more thoroughly.

Cache-and-Relay Streaming Media Delivery for Asynchronous Clients

We consider the problem of delivering popular streaming media to a large number of asynchronous clients. We propose and evaluate a cache-and-relay end-system multicast approach, whereby a client joining a multicast session caches the stream, and if needed, relays that stream to neighboring clients which may join the multicast session at some later time. This cache-and-relay approach is fully distributed, scalable, and efficient in terms of network link cost. In this paper we analytically derive bounds on the network link cost of our cache-and-relay approach, and we evaluate its performance under assumptions of limited client bandwidth and limited client cache capacity. When client bandwidth is limited, we show that although finding an optimal solution is NP-hard, a simple greedy algorithm performs surprisingly well in that it incurs network link costs that are very close to a theoretical lower bound. When client cache capacity is limited, we show that our cache-and-relay approach can still significantly reduce network link cost. We have evaluated our cache-and-relay approach using simulations over large, synthetic random networks, power-law degree networks, and small-world networks, as well as over large real router-level Internet maps.

Scalable Overlay Multicast Tree Construction for QoS-Constrained Media Streaming

Overlay networks have become popular in recent times for content distribution and end-system multicasting of media streams. In the latter case, the motivation is based on the lack of widespread deployment of IP multicast and the ability to perform end-host processing. However, constructing routes between various end-hosts, so that data can be streamed from content publishers to many thousands of subscribers, each having their own QoS constraints, is still a challenging problem. First, any routes between end-hosts using trees built on top of overlay networks can increase stress on the underlying physical network, due to multiple instances of the same data traversing a given physical link. Second, because overlay routes between end-hosts may traverse physical network links more than once, they increase the end-to-end latency compared to IP-level routing. Third, algorithms for constructing efficient, large-scale trees that reduce link stress and latency are typically more complex. This paper therefore compares various methods to construct multicast trees between end-systems, that vary in terms of implementation costs and their ability to support per-subscriber QoS constraints. We describe several algorithms that make trade-offs between algorithmic complexity, physical link stress and latency. While no algorithm is best in all three cases we show how it is possible to efficiently build trees for several thousand subscribers with latencies within a factor of two of the optimal, and link stresses comparable to, or better than, existing technologies.