Reliable Multicast Based on Congestion-Aware Cache in ICN

Abstract

Reliable multicast distribution is essential for some applications such as Internet of Things (IoT) alarm information and important file distribution. Traditional IP reliable multicast usually relies on multicast source retransmission for recovery losses, causing huge recovery delay and redundancy. Moreover, feedback implosion tends to occur towards multicast source as the number of receivers grows. Information-Centric Networking (ICN) is an emerging network architecture that is efficient in content distribution by supporting multicast and in-network caching. Although ubiquitous in-network caching provides nearby retransmission, the design of cache strategy greatly affects the performance of loss recovery. Therefore, how to recover losses efficiently and quickly is an urgent problem to be solved in ICN reliable multicast. In this paper, we first propose an overview architecture of ICN-based reliable multicast and formulate a problem using recovery delay as the optimization target. Based on the architecture, we present a Congestion-Aware Probabilistic Cache (CAPC) strategy to reduce recovery delay by caching recently transmitted chunks during multicast transmission. Then, we propose NACK feedback aggregation and recovery isolation scheme to decrease recovery overhead. Finally, experimental results show that our proposal can achieve fully reliable multicast and outperforms other approaches in recovery delay, cache hit ratio, transmission completion time, and overhead.

A Dynamic Cache Allocation Mechanism (DCAM) for Reliable Multicast in Information-Centric Networking

“…In ICN, each multicast group is regard as an entity and is assigned with a globally unique multicast service identifier (GUMSID). As shown in Figure 1, based on the reliable multicast architecture proposed in [20], we illustrate the shortcomings of the existing cache allocation mechanisms through a three-layer multicast tree model. There are two reliable multicast groups in Figure 1, namely GUMSID1 and GUMSID2.…”

“…Table 1 shows the parameter settings of our simulation experiments. We compared DCAM with AARM [18] and Equal [19] with regard to loss recovery delay, cache hit ratio, transmission completion time, and overhead under different cache strategies (LCE [9], CAPC [20], ProbCache [21], and Prob [22]). The parameter settings of CAPC, ProbCache, and Prob refer to [20].…”

“…We compared DCAM with AARM [18] and Equal [19] with regard to loss recovery delay, cache hit ratio, transmission completion time, and overhead under different cache strategies (LCE [9], CAPC [20], ProbCache [21], and Prob [22]). The parameter settings of CAPC, ProbCache, and Prob refer to [20]. One of the most important performance indicators of reliable multicast is the loss recovery delay.…”

“…This is because CAPC takes into account the congestion condition and the location of cache nodes on the multicast tree, so that CAPC can provide better loss recovery services. The advantages of CAPC have been demonstrated in [20]. However, LCE caches all chunks along the way.…”

“…In order to optimize the overall loss recovery performance of all multicast groups and improve the utilization of cache resources in the network, this paper comprehensively considers the packet loss probability, node depth, and transmission rate to design a dynamic cache allocation mechanism (DCAM). We also combine the three cache allocation mechanisms (DCAM, AARM [18], Equal [19]) with the four cache strategies (LCE [9], CAPC [20], ProbCache [21], Prob [22]), and conduct experiments to compare their performance. Experiments show that, among all combinations of cache allocation mechanisms and cache strategies, the combination of DCAM and CAPC performs best in terms of loss recovery delay, cache hit ratio, transmission completion time, and overhead.…”