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

Dataflow Analysis for Multiprocessor Systems with Non-Starvation-Free Schedulers

Dataflow analysis techniques are suitable for the temporal analysis of real-time stream processing applications. However, the applicability of these models is currently limited to systems with starvation-free schedulers, such as Time-Division Multiplexing (TDM) schedulers. Removal of this limitation would broaden the application domain of dataflow analysis techniques significantly. In this paper we present a temporal analysis technique for Homogeneous Synchronous Dataflow (HSDF) graphs, that is also applicable for systems with non-starvation-free schedulers. Unlike existing dataflow analysis techniques, the proposed analysis technique makes use of an enabling-jitter characterization and iterative fixed-point computation. The presented approach is applicable for arbitrary (cyclic) graph topologies. Buffer capacity constraints are taken into account during the analysis and sufficient buffer capacities can be determined afterwards. The approach presented in this paper is the first approach that considers non-starvation-free schedulers in combination with arbitrary HSDF graphs. The proposed dataflow analysis technique is implemented in a tool. This tool is used to evaluate the analysis technique using examples that illustrate some important differences with other temporal analysis methods. The case-study discusses how the method presented in this paper can be used to solve a problem with the inaccuracy of the temporal analysis results of a real-time stream processing system. This stream processing system consists of an FM receiver together with a DAB receiver application which both share a Digital Signal Processor (DSP)

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 $n$ 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

On the schedulability of deadline-constrained traffic in TDMA Wireless Mesh Networks

In this paper, we evaluate the schedulability of traffic with arbitrary end-to-end deadline constraints in Wireless Mesh Networks (WMNs). We formulate the problem as a mixed integer linear optimization problem, and show that, depending on the flow aggregation policy used in the network, the problem can be either convex or non-convex. We optimally solve the problem in both cases, and prove that the schedulability does depend on the aggregation policy. This allows us to derive rules of thumb to identify which policy improves the schedulability with a given traffic. Furthermore, we propose a heuristic solution strategy that allows good suboptimal solutions to the scheduling problem to be computed in relatively small times, comparable to those required for online admission control in relatively large WMNs

Theories and Models for Internet Quality of Service

We survey recent advances in theories and models for Internet Quality of Service (QoS). We start with the theory of network calculus, which lays the foundation for support of deterministic performance guarantees in networks, and illustrate its applications to integrated services, differentiated services, and streaming media playback delays. We also present mechanisms and architecture for scalable support of guaranteed services in the Internet, based on the concept of a stateless core. Methods for scalable control operations are also briefly discussed. We then turn our attention to statistical performance guarantees, and describe several new probabilistic results that can be used for a statistical dimensioning of differentiated services. Lastly, we review recent proposals and results in supporting performance guarantees in a best effort context. These include models for elastic throughput guarantees based on TCP performance modeling, techniques for some quality of service differentiation without access control, and methods that allow an application to control the performance it receives, in the absence of network support

Advances in Internet Quality of Service

We describe recent advances in theories and architecture that support performance guarantees needed for quality of service networks. We start with deterministic computations and give applications to integrated services, differentiated services, and playback delays. We review the methods used for obtaining a scalable integrated services support, based on the concept of a stateless core. New probabilistic results that can be used for a statistical dimensioning of differentiated services are explained; some are based on classical queuing theory, while others capitalize on the deterministic results. Then we discuss performance guarantees in a best effort context; we review: methods to provide some quality of service in a pure best effort environment; methods to provide some quality of service differentiation without access control, and methods that allow an application to control the performance it receives, in the absence of network support

Worst-Case Timing Analysis of AeroRing- A Full Duplex Ethernet Ring for Safety-critical Avionics

Avionics implementation with less cables will clearly improve the efficiency of aircraft while reducing weight and maintenance costs. To fulfill these emerging needs, an innovative avionics communication architecture, based on Gigabit Full Duplex Ethernet ring, is proposed in this paper. To adapt this COTS technology to safety-critical avionics, an adequate tuning process of the communication protocol and the choice of reliability mechanisms to achieve timely and reliable communications are first detailed. Then, efficient timing analyses of such a proposal based on Network Calculus are conducted, accounting the impact of a ring topology and the specified reliability mechanisms. Third, these general analyses are illustrated in the case of a realistic avionic application, to replace the AFDX backup network with AeroRing, to reduce wires, while guaranteeing timely communications