Large-scale Reliable Multicast Research Articles

An autonomic framework for reliable multicast

A major challenge in wireless terrestrial networks is to provide large-scale reliable multicast and broadcast services. The main problem limiting the scalability of such networks is feedback implosion, a problem arising when a large number of users transmit their feedback messages through the network, occupying a significant portion of system resources. Inspired by social psychology, specifically from the bystander effect phenomenon, an autonomic framework for large-scale reliable multicast services is presented. The self-configuring and self-optimizing procedures of the proposed autonomic scheme are modeled using game theory. Through appropriate modeling and simulations of the proposed scheme carried out to evaluate its performance, it is found that the new approach suppresses feedback messages very effectively, while at the same time, it does not degrade the timely data transfer.

Cache partitioning for multiple sessions in local loss recovery of reliable multicast

Local loss recovery for reliable multicast can provide significant performance improvement in terms of loss recovery latency, bandwidth consumption and network throughput. Active reliable multicast (ARM) is a novel loss recovery scheme for large-scale reliable multicast. In ARM, local loss recovery is realised by retrieving repairs at near active routers. In practice, active resources are limited and active resources allocation strategies are required for optimising performance in terms of the loss recovery and/or network bandwidth consumption. An active router may need to support the local loss recovery for multiple multicast sessions simultaneously. How to partition efficiently the cache resource to these sessions is crucial to the performance of local loss recovery. In the paper, the authors propose a cache partitioning method called ‘layered greedy cache partitioning’ to deal with this dilemma. For this method, the active routers are first grouped into different layers according to the tree topologies and the design begins from the lowest layer. The cache partitioning of an upper layer can be performed separately by making use of relevant design information of the lower layer. An optimisation for cache partitioning at each layer can then be conducted and a ‘local’ optimal solution can be achieved. On a global scale, however, the solution is only suboptimal. The performance of using the proposed cache partitioning method is compared with that using uniform cache partitioning and proportional partitioning. A significant performance improvement is found.

Caching policy design and cache allocation in active reliable multicast

Active reliable multicast (ARM) is a novel loss recovery scheme for large-scale reliable multicast. It uses active routers to perform NACK (negative-acknowledgement) consolidation and scoped retransmission. For optimizing the loss recovery performance of ARM under limited cache resources, the work in this paper aims at designing efficient and robust cache allocation schemes and caching policies. When a packet arrives at an active router, a caching policy determines if this packet should be cached and if cached, for how long. Three caching policies, timer-based, simple FIFO and probabilistic FIFO, are investigated in this paper. For a given amount of cache, the cache allocation problem is to find the best way to allocate the cache to each active router such that the overall network loss recovery performance can be optimized. Three cache allocation schemes, equal sharing, least requirement first and proportional allocation, are proposed. Among all the combinations of cache allocations and caching policies, we found that the proportional cache allocation with probabilistic FIFO caching policy gives the best overall performance.

Active resource allocation for active reliable multicast

Local loss recovery for reliable multicast can provide significant performance improvement in terms of loss recovery latency, bandwidth consumption and network throughput. Active reliable multicast (ARM) is a novel loss recovery scheme for large-scale reliable multicast. When sufficient active resources of active routers (ARs) and associated cache are available, ARM gives good loss recovery latency performance. The authors build an analytical model for studying the optimal active resource allocation for ARM. They propose a top-down layering analysis (TDLA) approach to derive the expected loss recovery latency (ELRL). For a given multicast tree, the ELRL is derived as a function of the active resource allocation pattern. Simulation experiments reveal that high performance gains can be attained with limited active resources, which are properly allocated. It is also shown that simulation results have a good match with the analytical results. The analytical framework developed can be used for further investigation of various active resource allocation schemes and caching policies.

MTCP: scalable TCP-like congestion control for reliable multicast

We present MTCP, a congestion control scheme for large-scale reliable multicast. Congestion control for reliable multicast is important, because of its wide applications in multimedia and collaborative computing, yet non-trivial, because of the potentially large number of receivers involved. Many schemes have been proposed to handle the recovery of lost packets in a scalable manner, but there is little work on the design and implementation of congestion control schemes for reliable multicast. We propose new techniques that can effectively handle instances of congestion occurring simultaneously at various parts of a multicast tree. Our protocol incorporates several novel features: (1) hierarchical congestion status reports that distribute the load of processing feedback from all receivers across the multicast group, (2) the relative time delay concept which overcomes the difficulty of estimating round-trip times in tree-based multicast environments, (3) window-based control that prevents the sender from transmitting faster than packets leave the bottleneck link on the multicast path through which the sender's traffic flows, (4) a retransmission window that regulates the flow of repair packets to prevent local recovery from causing congestion, and (5) a selective acknowledgment scheme that prevents independent (i.e., non-congestion-related) packet loss from reducing the sender's transmission rate. We have implemented MTCP both on UDP in SunOS 5.6 and on the simulator ns, and we have conducted extensive Internet experiments and simulation to test the scalability and inter-fairness properties of the protocol. The encouraging results we have obtained support our confidence that TCP-like congestion control for large-scale reliable multicast is within our grasp.