Data networks
A hop by hop rate-based congestion control scheme
SIGCOMM '92 Conference proceedings on Communications architectures & protocols
Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
Internetworking with TCP/IP (vol. 2, 2nd ed.): design, implementation, and internals
Internetworking with TCP/IP (vol. 2, 2nd ed.): design, implementation, and internals
An engineering approach to computer networking: ATM networks, the Internet, and the telephone network
Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
Scalable flow control for multicast ABR services in ATM networks
IEEE/ACM Transactions on Networking (TON)
Delay analysis of feedback-synchronization signaling for multicast flow control
IEEE/ACM Transactions on Networking (TON)
Performance issues in public ABR service
IEEE Communications Magazine
On max-min fair congestion control for multicast ABR service in ATM
IEEE Journal on Selected Areas in Communications
Statistical QoS provisionings for wireless unicast/multicast of multi-layer video streams
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
Feedback signaling plays a key role in flow control because the traffic source relies on the signaling information to make correct and timely flow-control decisions. However, it is difficult to design an efficient signaling algorithm since a signaling message can tolerate neither error nor latency. Multicast flow-control signaling imposes two additional challenges: scalability and feedback synchronization. Previous research on multicast signaling has mainly focused on the development of algorithms without analyzing their delay performance. To remedy this deficiency, we have previously developed a binary-tree model and an independent-marking statistical model for multicast-signaling delay analysis. This paper considers a general scenario where the congestion markings at different links are dependent--a more accurate but complex case. Specifically, we develop a Markov-chain model defined by the link-marking state on each path in the multicast tree. The Markov chain can not only capture link-marking dependencies, but also yield a tractable analytical model. We also develop a Markov-chain dependency-degree model to evaluate all possible Markov-chain dependency degrees without any prior knowledge of them. Using the above two models, we derive the general probability distributions of each path becoming the multicast-tree bottleneck. Also derived are the first and second moments of multicast signaling delays. The proposed Markov chain is also shown to asymptotically reach an equilibrium, and its limiting distribution converges to the marginal link-marking probabilities when the Markov chain is irreducible. Applying the two models, we analyze and contrast the delay scalability of two representative multicast signaling protocols: Soft-Synchronization Protocol (SSP) and Hop-By-Hop (HBH) algorithms.