Minimum-delay overlay multicast

Abstract

Delivering delay-sensitive data to a group of receivers with minimum latency is a fundamental problem for various applications. In this paper, we study multicast routing with minimum end-to-end delay to the receivers. The delay to each receiver in a multicast tree consist of the time that the data spends in overlay links as well as the latency incurred at each overlay node, which has to send out a piece of data several times over a finite-capacity network connection. The latter portion of the delay, which is proportional to the degree of nodes in the tree, can be a significant portion of the total delay as we show in the paper. Yet, it is often ignored or only partially addressed by previous multicast algorithms. We formulate the actual delay to the receivers in a multicast tree and consider minimizing the average and the maximum delay in the tree. We show the NP-hardness of these problems and prove that they cannot be approximated in polynomial time to within any reasonable approximation ratio.We then propose a number of efficient algorithms that heuristically build a multicast tree in which the average or the maximum delay is minimized. These algorithms together cover a wide range of overlay sizes and both versions of our problem. The effectiveness of our algorithms is demonstrated through comprehensive experiments on different real-world datasets and using various overlay network models.The results confirm that our algorithms can achieve much lower delays (up to 60% less) and up to orders of magnitude faster running times (hence supporting larger scales) than previous minimum-delay multicast approaches.

Minimum-Delay Multicast Algorithms for Mesh Overlays

“…We have presented an earlier version of this work in [32]. In this paper, we complete the work in [32] by presenting a new algorithm for updating multicast trees, rather than having to rebuild them every time from scratch, which reduces the calculation time for multicast trees to nearly 0 while producing trees with high delay efficiency.…”

“…In this paper, we complete the work in [32] by presenting a new algorithm for updating multicast trees, rather than having to rebuild them every time from scratch, which reduces the calculation time for multicast trees to nearly 0 while producing trees with high delay efficiency. We also demonstrate how to best employ our multiple algorithms together to always yield the best running time and tree efficiency.…”

Media Streams Planning with Uncertain Link Capacities

“…Most of the approaches rely on computational intelligence algorithms, namely genetic algorithms [21], ACO [22], or quantum particle swarm optimization [23]. The MMDOM algorithm [24] minimizes latency of multicast tree considering both link latencies and delays in queues on the network nodes. However, the MMDOM is not oriented on multimedia streaming and it considers neither typical QoS constraints nor multiple tree building.…”

Distributed optimization of event dissemination exploiting interest clustering

“…Other possible components of the utility function include a prediction on the reliability of forwarding nodes and the projected remaining duration of a subscription. Furthermore, queuing delays could be modeled more explicitly than only by the bandwidth utilization [9]. We assume that for each factor there is a cost function c f .…”