Graph-Based Approach for Buffer-Aware Timing Analysis of Heterogeneous Wormhole NoCs Under Bursty Traffic

Abstract

This paper addresses the problem of worst-case timing analysis of heterogeneous wormhole NoCs, i.e., routers with different buffer sizes and transmission speeds, when consecutivepacket queuing (CPQ) occurs. The latter means that there are several consecutive packets of one flow queuing in the network. This scenario happens in the case of bursty traffic but also for non-schedulable traffic. Conducting such an analysis is known to be a challenging issue due to the sophisticated congestion patterns when enabling backpressure mechanisms. We tackle this problem through extending the applicability domain of our previous work for computing maximum delay bounds using Network Calculus, called Buffer-aware worst-case Timing Analysis (BATA). We propose a new Graph-based approach to improve the analysis of indirect blocking due to backpressure, while capturing the CPQ effect and keeping the information about dependencies between flows. Furthermore, the introduced approach improves the computation of indirect-blocking delay bounds in terms of complexity and ensures the safety of these bounds even for nonschedulable traffic. We provide further insights into the tightness and complexity issues of worst-case delay bounds yielded by the extended BATA with the Graph-based approach, denoted G-BATA. Our assessments show that the complexity has decreased by up to 100 times while offering an average tightness ratio of 71%, with reference to the basic BATA. Finally, we evaluate the yielded improvements with G-BATA for a realistic use case against a recent state-of-the-art approach. This evaluation shows the applicability of G-BATA under more general assumptions and the impact of such a feature on the tightness and computation time.

Centralized Routing Protocol for Detecting Wormhole Attacks in Wireless Sensor Networks

“…This mechanism shows efficient performance; however, it needs to go through several evaluation processes to detect the wormhole attack and this in turn slows down the response of the algorithm. Authors in [26] presented a technique based on a graph approach where if the assumptions of the proposed solution parameters are not valid in a real deployment, that would result in wormhole detection failure. Also, some other recent solutions presented in [27]- [29] use nodes connectivity considering ideal parameter presumptions that won't be applicable in real networks.…”

Network-Cognitive Traffic Control: A Fluidity-Aware On-Chip Communication

“…Consequently, when the congestion information reaches the transmission sources, packets have seriously congested the network. This phenomenon is distinct in wormhole switching and so-called a macro-pipeline of flits in [32] and consecutive-packet queuing in [10].…”

“…For example, Melo et al [5] analyzed the router behavior for detecting signal upset in order to minimize error propagations, Chang et al [6] provided a contention prediction scheme for better adaptive routing path decisions, and Tang et al [7] implemented a congestion avoidance method for NoC, based on the speculative reservation of network resource that was proposed by Jiang et al [8]. Recently, Mehranzadeh et al [9], designed a congestion-aware output selection strategy based on calculations of congestion levels of neighboring nodes, Giroudot et al [10], realized a buffer-aware worst-case timing analysis of wormhole routers with different buffer sizes when consecutive-packet queuing occurs.…”

“…For congestion control, adaptive routing schemes [14,15], can be used to forward packets around the contentious regions. In NoCs, most congestion control schemes select profitable routing paths by monitoring some forms of buffer fill level [7,9,10,[16][17][18][19][20]. NoC link-layer (node-to-node) flow control schemes [6,8,[20][21][22], are developed to prevent buffers overfill between neighboring nodes.…”

Bounding the delays of the MPPA network-on-chip with network calculus: Models and benchmarks

“…The back-pressure mechanism is also modeled in Giroudot and Mifdaoui (2020). It first computes the latency introduced by wormhole back-pressure (called "blocking latency"), and then build a service curve β R,T where the T is the back-pressure latency.…”