Active Reliable Multicast Research Articles

Dynamic buffer partitioning and replacement for reliable multicast

ACP is a buffer management protocol applied to shared environment.However,when multicast group number or group member changes,ACP can hardly adjust its allocation quota in time and this will lead to the decrease of cache utilization ratio.To address these drawbacks,a new buffer management scheme called Adaptive and Active Reliable Multicast Protocol(AARM) was proposed,for large-scale multicast network.Simulation results show,AARM outperforms ACP in reducing recovery latency and bandwidth consumption,and raising network throughput.

Dynamic buffer partitioning and replacement for reliable multicast

ACP is a buffer management protocol applied to shared environment.However,when multicast group number or group member changes,ACP can hardly adjust its allocation quota in time and this will lead to the decrease of cache utilization ratio.To address these drawbacks,a new buffer management scheme called Adaptive and Active Reliable Multicast Protocol(AARM) was proposed,for large-scale multicast network.Simulation results show,AARM outperforms ACP in reducing recovery latency and bandwidth consumption,and raising network throughput.

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.

DyRAM: An Active Reliable Multicast Framework for Data Distribution

Group communications (multicast) are foreseen to be one of the most critical yet challenging technologies to meet the exponentially growing demands for data distribution in a large variety of applications of the Internet (such as grid computing, web applications and distributed simulations). When reliability is required, there is no straightforward solutions and meeting the objectives of reliable multicast is not an easy task. Active networks open a new perspective in providing more efficient solutions for the problem of reliability. In this context, routers are able to perform customized computations on the packets flowing through them. In this paper, we propose a receiver-based (replier) local recovery multicast protocol with dynamic repliers elected on a per-packet basis. Designed to provide an efficient reliable multicast service without any cache facilities inside the network, our approach, uses low-overhead active services in routers. The current paper addresses the design, evaluation and the implementation of an efficient and scalable reliable multicast protocol noted DyRAM standing for Dynamic Replier Active reliable multicast.

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.