4 research outputs found
On the Robustness of Deep Learning-predicted Contention Models for Network Calculus
The network calculus (NC) analysis takes a simple model consisting of a
network of schedulers and data flows crossing them. A number of analysis
"building blocks" can then be applied to capture the model without imposing
pessimistic assumptions like self-contention on tandems of servers. Yet, adding
pessimism cannot always be avoided. To compute the best bound on a single
flow's end-to-end delay thus boils down to finding the least pessimistic
contention models for all tandems of schedulers in the network - and an
exhaustive search can easily become a very resource intensive task. The
literature proposes a promising solution to this dilemma: a heuristic making
use of machine learning (ML) predictions inside the NC analysis.
While results of this work were promising in terms of delay bound quality and
computational effort, there is little to no insight on when a prediction is
made or if the trained algorithm can achieve similarly striking results in
networks vastly differing from its training data. In this paper, we address
these pending questions. We evaluate the influence of the training data and its
features on accuracy, impact and scalability. Additionally, we contribute an
extension of the method by predicting the best contention model
alternatives in order to achieve increased robustness for its application
outside the training data. Our numerical evaluation shows that good accuracy
can still be achieved on large networks although we restrict the training to
networks that are two orders of magnitude smaller
Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis enabled by ML
The derivation of upper bounds on data flows' worst-case traversal times is
an important task in many application areas. For accurate bounds, model
simplifications should be avoided even in large networks. Network Calculus (NC)
provides a modeling framework and different analyses for delay bounding. We
investigate the analysis of feedforward networks where all queues implement
First-In First-Out (FIFO) service. Correctly considering the effect of data
flows onto each other under FIFO is already a challenging task. Yet, the
fastest available NC FIFO analysis suffers from limitations resulting in
unnecessarily loose bounds. A feature called Flow Prolongation (FP) has been
shown to improve delay bound accuracy significantly. Unfortunately, FP needs to
be executed within the NC FIFO analysis very often and each time it creates an
exponentially growing set of alternative networks with prolongations. FP
therefore does not scale and has been out of reach for the exhaustive analysis
of large networks. We introduce DeepFP, an approach to make FP scale by
predicting prolongations using machine learning. In our evaluation, we show
that DeepFP can improve results in FIFO networks considerably. Compared to the
standard NC FIFO analysis, DeepFP reduces delay bounds by 12.1% on average at
negligible additional computational cost
Comparaison de strategies de calcul de bornes sur NoC
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 some hardware mechanisms (egress traffic limiters) that can be configured to offer bounded latencies. This paper presents how network calculus can be used to bound these latencies while computing the routes of data flows, using linear programming. Then, its shows how other approaches can also be used and adapted to analyze this NoC. Their performances are then compared on three case studies: two small coming from previous studies, and one realistic with 128 or 256 flows. On theses cases studies, it shows that modeling the shaping introduced by links is of major importance to get accurate bounds. And when packets are of constant size, the Total Flow Analysis gives, on average, bounds 20%-25% smaller than all other methods