Worst-case delay control in multigroup overlay networks

This paper proposes a novel and simple adaptive control algorithm for the effective delay control and resource utilization of end host multicast (EMcast) when the traffic load becomes heavy in a multigroup network with real-time flows constrained by (sigma, rho) regulators. The control algorithm is implemented at the overlay networks and provides more regulations through a novel (sigma, rho, lambda) regulator at each group end host who suffers from heavy input traffic. To our knowledge, it is the first work to incorporate traffic regulators into the end host multicast to control heavy traffic output. Our further contributions include a theoretical analysis and a set of results. We prove the existence and calculate the value of the rate threshold rho* such that for a given set of K groups, when the average rate of traffic entering the group end hosts rho macr > rho*, the ratio of the worst-case multicast delay bound of the proposed (sigma, rho, lambda) regulator over the traditional (sigma, rho) regulator is O(1/Kn) for any integer n. We also prove the efficiency of the novel algorithm and regulator in decreasing worst-case delays by conducting computer simulations

Performance analysis for overlay multicast on tree and M-D mesh topologies (II)

In our previous work, we have analyzed the worst performance for tree-based and mesh-based multicast along the link stress, the number of overlay hops, and the number of shortest paths. In this paper, we extend our research through studying the average performance and the difference between the worst and the average performance for these metrics. We present a set of theoretical results that evaluate the average performance and the performance difference for tree-based multicast and mesh-based multicast in quantity. And also, we program NICE tree and CAN-based multicast in NS2 to evaluate our theoretical prediction and compare tree-based and mesh-based multicast. Simulation results prove our theoretical analysis. We find that tree-based multicast suits to not only real-time but also interactive streaming media applications, and mesh-based multicast holds the promise for the bottleneck-avoidance and reliable transmission in multi-source non-real-time applications

Resource-aware Video Multicasting via Access Gateways in Wireless Mesh Networks

This paper studies video multicasting in large-scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy and low-capacity multihop wireless paths. A set of heuristic-based algorithms is described that together aim to maximize reliable network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link-controlled routing tree algorithm, and the dynamic group management algorithm. These algorithms use different approaches to arrange nodes involved in video multicasting into a clustered and two-tier integrated architecture in which network protocols can make use of multiple gateways to improve system throughput. Simulation results are presented, showing that our multicasting algorithms can achieve up to 40 percent more throughput than other related published approaches

Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness