While emerging Internet media streaming systems promise to offer viewers an abundant set of user interactivities and controls over the contents, providing streaming services through a large number of concurrent point-to-point connections stresses both server and network because of the large volumes of data and relatively high bandwidth requirement. While the server limitation can be circumvented by deploying server clusters, the network limitation is far less easy to deal with, due to the difficulty in measuring and balancing network load. Therefore, successful deployment of media streaming services requires a scheme that is conducible to mitigate this problem. In this paper, we employ two network load metrics, the worst link stress and the degree of interference, for the measurement of the network load balance. Then, we formulate the user grouping problem and present a greedy algorithm that reduces the network load metrics for each session and also well balances across the sessions. Through the simulation results, we conclude that our algorithm perform better than the existing scheme in the following aspects: (1) load-balancing in the network, (2) total bandwidth used by the connections.

In this paper, we investigate the issue of server selection for P2P IPTV system on fiber to the node networks. To achieve high performance and resilience to failures, a peer can make connections with multiple peers simultaneously and receive different portions of the data in parallel. Prior work mostly focuses on user-centric performance objectives, such as reducing the round-trip time or the completion time of individual download, but tend to ignore the congestion caused by the concurrent connections or the network load balance. The latter performance concerns are important for the network operators who provide and manage IPTV services. In this paper, we consider two performance metrics, the worst-case link stress (WLS) and the degree of interference (DOI), and present a server selection scheme that generates the optimal server set with respect to the WLS criterion. Through experiments on PlanetLab, we show that the proposed scheme has the advantages over the random scheme and the closest scheme in the following aspects. First, it minimizes the level of congestion at the bottleneck link. Second, it consumes less network resources in terms of the total bandwidth usage. Third, the network load is well balanced.

To achieve high performance and resilience to failures, many peer-to-peer (P2P) systems have begun to use striping and collaborative downloading, where each peer can download the file or pieces of the file from a number of peers in parallel, and at the same time, serve them to some other peers. These P2P systems intrinsically involve many-to-many mappings from the servers to the clients (or peers to peers) as well as many-to-one mappings from the clients to the server. In this paper, we present a node selection algorithm that generates an optimal server set with respect to the worst link stress (WLS) criterion for a single client and evaluates its network performance metrics for many- to-many mappings and many-to-one mappings. Simulation results show that our system is simple yet effective in achieving the design goals. In particular, it minimizes the level of congestion at the bottleneck link, and it consumes less network resources in terms of total bandwidth usage. The objectives of this in-depth examination are to raise awareness and concern for these issues and to provide alternatives or improved designs that can be incorporated into many P2P systems.

In many networking applications such as file sharing, structured peer-to-peer networks are increasingly used in dynamic situations with fluctuating load, which require proper load balancing. The relationship between the network structure and its load-balancing properties has not been fully understood. In this paper, we focus on the Plaxton-type networks, which are broad enough to include Pastry, Tapestry, and hypercube. We first use hypercube as an example and demonstrate that replicating files at nodes in decreasing order of the length of the common prefix with the original server leads to perfectly balanced load, and does so fast and efficiently. Moreover, this replication strategy coincides with a simple on-demand replication/caching strategy based on the observed load. One of our main contributions is to show that such desirable properties also exist for a large class of random networks, which are less restrictive and more practical than the hypercube. More importantly, we have discovered a multiple-choice random network, which drastically reduces the statistical fluctuation of the load: The maximum load over all replication servers is at most three times the average load for systems of practical sizes. The main insight is that this algorithm is related to a variant of the multiple-choice balls-in-bins problem.

To achieve high performance and resilience to failures, a client can make connections with multiple servers simultaneously and receive different portions of the data from each server in parallel. However, selecting the best set of servers from the set of all nodes that have the desired data is not a straightforward task, and the obtained performance can dramatically vary depending on the constructed server set. In this paper, we present two server selection schemes that generate optimal server sets with respect to the degree of interference(DOI) criterion and the worst link stress(WLS) criterion in a structured peer-to-peer network. After examining the correctness of the algorithms, we present simulation results demonstrating the benefits of the optimal server selection schemes. Through the simulation results, we conclude that the optimal selection schemes perform better than the random server-selection scheme in the following aspects: (1) load-balancing in the network and the worst-case link stress, (2) network resource used by the connections, including the number of links and total bandwidth, (3) response time, and (4) throughput of TCP connections.