Improving Cross-Traffic Bounds in Feed-Forward Networks – There is a Job for Everyone

Bondorf, Steffen; Schmitt, Jens

doi:10.1007/978-3-319-31559-1_3

Cited by 14 publications

(13 citation statements)

References 16 publications

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“…Moreover, as claimed in Bondorf and Schmitt (2016), "there is a job for everyone": on the small case studies, no method always gives better results than the others in case of packets of variables sizes (fluid modeling), but in case of packets of constant size, the SFA and TFA algorithms with "packet-accurate" arrival and service curves give the best results. The same results appear on the large case studies: in case of packets of constant size, the TFA algorithms with "packet-accurate" arrival and service curve currently gives bounds 20%-25% smaller than any other, on average.…”

Section: Resultsmentioning

confidence: 99%

“…The analytical expression of this phenomenon is not based on the minplus operators , and computable expressions have been given only for systems with token-bucket arrival curves and rate-latency service curves and blind multiplexing Schmitt et al (2008b,a). The performances of the different strategies may depend on the network characteristics Bondorf and Schmitt (2016), and it may even be the case that adding pessimism to the network of interest can lead to better (i.e. smaller) upper bounds Bisti et al (2008); Bondorf (2017).…”

Section: Network Calculus Based Approachesmentioning

confidence: 99%

“…SFA and TFA Almost all published studies in network calculus report that the TFA is the less effective approach, except in a few specific cases, as presented in Bondorf and Schmitt (2016). But all these studies consider blind multiplexing, and the residual service computed in this case with Theorem 3 is known to be tight, whereas for the FIFO policy, SFA requires the choice of a θ parameter in each server.…”

Section: Conclusion On the Case Studiesmentioning

confidence: 99%

See 2 more Smart Citations

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

Boyer

Graillat

Dinechin

et al. 2020

Performance Evaluation

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The Kalray MPPA2-256 processor integrates 256 processing cores and 32 management cores on a chip. Theses cores are grouped into clusters and clusters are connected by a high-performance network on chip (NoC). This NoC provides hardware mechanisms (ingress traffic limiters) that can be configured to offer service guarantees.This paper introduces a network calculus formulation, designed to configure the NoC traffic limiters, that also computes guarantee upper bounds on the NoC traversal latencies. This network calculus formulation accounts for the traffic shaping performed by the NoC links, and can be solved using linear programming. This paper then shows how existing network calculus approaches (the Separated Flow Analysis -SFA ; the Total Flow Analysis -TFA ; the Linear Programming approach -LP) can be adapted to analyze this NoC. The delay bounds obtained by the four approaches are then compared on two case studies: a small configuration coming from a previous study, and a realistic configuration with 128 or 256 flows.From theses cases studies, it appears that modeling the shaping introduced by NoC links is of major importance to get accurate bounds. And when all packets have the same size, modeling it reduces the bound by 20%-25% on average.

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Section: Resultsmentioning

confidence: 99%

Section: Network Calculus Based Approachesmentioning

confidence: 99%

Section: Conclusion On the Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

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

Boyer

Graillat

Dinechin

et al. 2020

Performance Evaluation

View full text Add to dashboard Cite

show abstract

“…Moreover, the presented mcastFFA has the flexibility to be combined with any DNC tandem analysis and improvement thereof. For instance, [7], [3], FIFO multiplexing service analysis [2] or packetization [8] can tighten guarantees in AFDX networks.…”

Section: Resultsmentioning

confidence: 99%

Generalizing Network Calculus Analysis to Derive Performance Guarantees for Multicast Flows

Bondorf¹,

Geyer²

2017

Proceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools

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Guaranteeing performance bounds of data flows is an essential part of network engineering and certification of networks with real-time constraints. A prevalent analytical method to derive guarantees for end-to-end delay and bu↵er size is Deterministic Network Calculus (DNC). Due to the DNC system model, one decisive restriction is that only unicast flows can be analyzed. Previous attempts to analyze networks with multicast flows circumvented this restriction instead of overcoming it. E.g., they replaced the system model with an overly-pessimistic one that consists of unicast flows only. Such approaches impair modeling accuracy and thus inevitably result in inaccurate performance bounds. In this paper, we approach the problem of multicast flows di↵erently. We start from existing DNC analyses and generalize them to handle multicast flows. We contribute a novel analysis procedure that leaves the network model unaltered, preserves its accuracy, allows for DNC principles such as pay multiplexing only once, and therefore derives more accurate performance bounds than existing approaches.

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“…Alternatives either implement the pay bursts only once principle (Section 2) or the PMOO principle. They either aggregate the interfering flows or execute a per-flow computation during the computation [27], and all alternatives can be augmented with an additional output burstiness restriction [28]. Thus, the DiscoDNC comprehensively covers a wide range of alternative analyses proceeding from generic network calculus.…”

Section: Tool Supportmentioning

confidence: 99%

The Sensor Network Calculus as Key to the Design of Wireless Sensor Networks with Predictable Performance

Schmitt

Bondorf

Poe

2017

JSAN

Self Cite

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Abstract:In this article, we survey the sensor network calculus (SensorNC), a framework continuously developed since 2005 to support the predictable design, control and management of large-scale wireless sensor networks with timing constraints. It is rooted in the deterministic network calculus, which it instantiates for WSNs, as well as it generalizes it in some crucial aspects, as for instance in-network processing. Besides presenting these core concepts of the SensorNC, we also discuss the advanced concept of self-modeling of WSNs and efficient tool support for the SensorNC. Furthermore, several applications of the SensorNC methodology, like sink and node placement, as well as TDMA design, are displayed.

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Improving Cross-Traffic Bounds in Feed-Forward Networks – There is a Job for Everyone

Cited by 14 publications

References 16 publications

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

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

Generalizing Network Calculus Analysis to Derive Performance Guarantees for Multicast Flows

The Sensor Network Calculus as Key to the Design of Wireless Sensor Networks with Predictable Performance

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