The Metaverse project aims to develop technology for low-cost, high-resolution networked immersive display environments that can be used for distributed collaboration, exploration of 3D data models, scientific visualization, and other Grid-related applications. Such applications often deal with massive data sets and need a high-capacity, low-latency transport service to effectively connect distant locations across the wide-area (best-effort) Internet. This work presents the initial design of such an end-to-end transport service for Metaverse applications, along with results of a simulation study evaluating its effectiveness. The transport service features an application programming interface providing enhanced control over the way resources are allocated to data objects, and uses multiple overlay-based end-to-end paths to increase bandwidth delivered to the application.

Overlays have been proposed as a means to improve application performance in many areas, including multimedia streaming and content distribution. Some overlays use parallel transmission to increase aggregate throughput or use backup paths to improve reliability. For such applications, an important consideration is whether the "virtual links" at the overlay level (i.e. paths between overlay nodes) share links in the underlying network. In particular, choosing parallel or backup paths without any information about path correlation can reduce the effectiveness of the overlay.In this paper we show how to use passive measurement of TCP throughput to provide information about path correlation, for use in overlay routing decisions. Our methods have the advantage that they send no probe traffic to collect path information. We present results of experimental evaluation in both controlled testbed (Emulab) and real wide area network (Planetlab). Our results demonstrate that the methods together work well across a wide range of operating conditions.

Multicast services can be provided either as a basic network service or as an application-layer service. Higher level multicast implementations often provide more sophisticated features, and can provide multicast services, where no network layer support is available. Overlay multicast networks offer an intermediate option, potentially combining the flexibility and advanced features of application layer multicast with the greater efficiency of network layer multicast. Overlay multicast networks play an important role in the Internet. Indeed, since Internet service providers have been slow to enable IP multicast in their networks, Internet multicast is only widely available as an overlay service. This paper introduces several routing algorithms that are suitable for overlay multicast networks and evaluates their performance. The algorithms seek to optimize the end-to-end delay and the interface bandwidth usage at the routing sites within the overlay network. The interface bandwidth is typically a key resource for an overlay network provider, and needs to be carefully managed in order to maximize the number of sessions that can be served. The simultaneous optimization of both delay and bandwidth is an NP-hard problem. We propose several heuristic algorithms and simulate their performance under various traffic conditions and on various network topologies.

Multicast services can be provided either as a basic network service or as an application-layer service. Higher level multicast implementations often provide more sophisticated features and can provide multicast services at places where no network layer support is available. Overlay multicast networks offer an intermediate option, potentially combining the flexibility and advanced features of application layer multicast with the greater efficiency of network layer multicast. In this paper, we introduce the multicast routing problem specific to the overlay network environment and the related capacity assignment problem for overlay network planning. Our main contributions are the design of several routing algorithms that optimize the end-to-end delay and the interface bandwidth usage at the multicast service nodes within the overlay network. The interface bandwidth is typically a key resource for an overlay network provider, and needs to be carefully managed in order to maximize the number of users that can be served. Through simulations, we evaluate the performance of these algorithms under various traffic conditions and on various network topologies. The results show that our approach is cost-effective and robust under traffic variations.

Application-level multicast is a new mechanism for enabling multicast in the Internet. Driven by the fast growth of network audio/video streams, application-level multicast has become increasingly important for its efficiency of data delivery and its ability of providing value-added services to satisfy application specific requirements. From a network design perspective, application-level multicast differs drastically from traditional IP multicast in its network cost model and routing strategies. We present these differences and formulate them as a network design problem consisting of two parts: one is bandwidth assignment in the overlay network, the other is load-balancing multicast routing with delay constraints. We use analytical methods and simulations to show that our design solution is a valid and cost-effective approach. Simulation results show that we are able to achieve network utilization within 10% of the best possible utilization while keeping the session rejection rate low.

Current reliable multicast protocols do not have scalable congestion control mechanisms and this deficiency leads to concerns that multicast deployment may endanger stability of the network. We present a sender-based approach for multicast congestion control targeted towards reliable bulk data transfer. We assume that there are a few bottleneck links in a large scale multicast group at any time period and these bottlenecks persist long enough to be identified and adapted to. Our work focuses on dynamically identifying the worst congested path in the multicast tree and obtaining TCP-friendly throughput on this selected path. We use the network simulator (NS2) to validate and evaluate our congestion control algorithm with both drop-tail and RED gateways.