Statistical Delay Bound for WirelessHART Networks

In this paper we provide a performance analysis framework for wireless industrial networks by deriving a service curve and a bound on the delay violation probability. For this purpose we use the (min,x) stochastic network calculus as well as a recently presented recursive formula for an end-to-end delay bound of wireless heterogeneous networks. The derived results are mapped to WirelessHART networks used in process automation and were validated via simulations. In addition to WirelessHART, our results can be applied to any wireless network whose physical layer conforms the IEEE 802.15.4 standard, while its MAC protocol incorporates TDMA and channel hopping, like e.g. ISA100.11a or TSCH-based networks. The provided delay analysis is especially useful during the network design phase, offering further research potential towards optimal routing and power management in QoS-constrained wireless industrial networks.Comment: Accepted at PE-WASUN 201

Qualitative Properties of alpha-Weighted Scheduling Policies

We consider a switched network, a fairly general constrained queueing network model that has been used successfully to model the detailed packet-level dynamics in communication networks, such as input-queued switches and wireless networks. The main operational issue in this model is that of deciding which queues to serve, subject to certain constraints. In this paper, we study qualitative performance properties of the well known $\alpha$-weighted scheduling policies. The stability, in the sense of positive recurrence, of these policies has been well understood. We establish exponential upper bounds on the tail of the steady-state distribution of the backlog. Along the way, we prove finiteness of the expected steady-state backlog when $\alpha<1$, a property that was known only for $\alpha\geq 1$. Finally, we analyze the excursions of the maximum backlog over a finite time horizon for $\alpha \geq 1$. As a consequence, for $\alpha \geq 1$, we establish the full state space collapse property.Comment: 13 page

Non-stationary service curves : model and estimation method with application to cellular sleep scheduling

In today’s computer networks, short-lived flows are predominant. Consequently, transient start-up effects such as the connection establishment in cellular networks have a significant impact on the performance. Although various solutions are derived in the fields of queuing theory, available bandwidths, and network calculus, the focus is, e.g., about the mean wake-up times, estimates of the available bandwidth, which consist either out of a single value or a stationary function and steady-state solutions for backlog and delay. Contrary, the analysis during transient phases presents fundamental challenges that have only been partially solved and is therefore understood to a much lesser extent. To better comprehend systems with transient characteristics and to explain their behavior, this thesis contributes a concept of non-stationary service curves that belong to the framework of stochastic network calculus. Thereby, we derive models of sleep scheduling including time-variant performance bounds for backlog and delay. We investigate the impact of arrival rates and different duration of wake-up times, where the metrics of interest are the transient overshoot and relaxation time. We compare a time-variant and a time-invariant description of the service with an exact solution. To avoid probabilistic and maybe unpredictable effects from random services, we first choose a deterministic description of the service and present results that illustrate that only the time-variant service curve can follow the progression of the exact solution. In contrast, the time-invariant service curve remains in the worst-case value. Since in real cellular networks, it is well known that the service and sleep scheduling procedure is random, we extend the theory to the stochastic case and derive a model with a non-stationary service curve based on regenerative processes. Further, the estimation of cellular network’s capacity/ available bandwidth from measurements is an important topic that attracts research, and several works exist that obtain an estimate from measurements. Assuming a system without any knowledge about its internals, we investigate existing measurement methods such as the prevalent rate scanning and the burst response method. We find fundamental limitations to estimate the service accurately in a time-variant way, which can be explained by the non-convexity of transient services and their super-additive network processes. In order to overcome these limitations, we derive a novel two-phase probing technique. In the first step, the shape of a minimal probe is identified, which we then use to obtain an accurate estimate of the unknown service. To demonstrate the minimal probing method’s applicability, we perform a comprehensive measurement campaign in cellular networks with sleep scheduling (2G, 3G, and 4G). Here, we observe significant transient backlogs and delay overshoots that persist for long relaxation times by sending constant-bit-rate traffic, which matches the findings from our theoretical model. Contrary, the minimal probing method shows another strength: sending the minimal probe eliminates the transient overshoots and relaxation times

Sharp Bounds in Stochastic Network Calculus

The practicality of the stochastic network calculus (SNC) is often questioned on grounds of potential looseness of its performance bounds. In this paper it is uncovered that for bursty arrival processes (specifically Markov-Modulated On-Off (MMOO)), whose amenability to \textit{per-flow} analysis is typically proclaimed as a highlight of SNC, the bounds can unfortunately indeed be very loose (e.g., by several orders of magnitude off). In response to this uncovered weakness of SNC, the (Standard) per-flow bounds are herein improved by deriving a general sample-path bound, using martingale based techniques, which accommodates FIFO, SP, EDF, and GPS scheduling. The obtained (Martingale) bounds gain an exponential decay factor of ${\mathcal{O}}(e^{-\alpha n})$ in the number of flows $n$. Moreover, numerical comparisons against simulations show that the Martingale bounds are remarkably accurate for FIFO, SP, and EDF scheduling; for GPS scheduling, although the Martingale bounds substantially improve the Standard bounds, they are numerically loose, demanding for improvements in the core SNC analysis of GPS

A Timely Journey Through the Cloud

This thesis treats the intersection between two of the largest transformations we are seeing within our society today; the cloud and the Internet-of-Things (IoT). The aim of this thesis is to investigate different ways to model and control a network of cloud services so that timing-critical IoT applications can make use of them. Examples of such applications can be autonomous and mobile robots, smart production plants, or massive multi-player augmented-reality games. The main motivational use-case, however, comes from the industrial side, and their digitalization, the drive towards industrial IoT (IIoT). We wish to enable smart robots to offload some of their computations to the cloud in order to allow for better and smarter control and collaboration. For instance, using the cloud, it would become possible for them to collaborate and make use of smarter analytics, artificial intelligence, and machine learning, in order to improve efficiency and safety. To address this problem the thesis combines concepts and theory from different fields, most notably from control theory, real-time systems, and network calculus. Examples are: modeling of dynamic systems and the use of feedback and feedforward control from control theory, the goal of ensuring that end-to-end deadlines are met, from real-time systems, and finally the principles of modeling traffic from network calculus. The thesis begins with an introduction to provide some background on cloud, IIoT, and to set the scope of the thesis. Following this, we begin by treating the problem of controlling a single cloud service with the goal of ensuring that the traffic flowing through the node is guaranteed to meet a deadline. Following this, we study a chain of connected cloud nodes, investigating how to provide end-to-end deadline guarantees for the traffic flowing through the chain. The chain is finally generalized to a network of cloud nodes, with multiple flows traversing it. For this problem we study how to ensure that the end-to-end deadline of every single flow in the network is guaranteed. We also provide a set of protocols controlling how cloud nodes and flows are allowed to dynamically join and leave the network, such that no end-to-end deadline is violated

Non-asymptotic performance evaluation and sampling-based parameter estimation for communication networks with long memory traffic

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