Frameworks for Nonclairvoyant Network Design with Deadlines or Delay

Clairvoyant network design with deadlines or delay has been studied extensively, culminating in an O(log n)-competitive general framework, where n is the number of possible request types (Azar and Touitou, FOCS 2020). In the nonclairvoyant setting, the problem becomes much harder, as ?(?n) lower bounds are known for certain problems (Azar et al., STOC 2017). However, no frameworks are known for the nonclairvoyant setting, and previous work focuses only on specific problems, e.g., multilevel aggregation (Le et al., SODA 2023). In this paper, we present the first nonclairvoyant frameworks for network design with deadlines or delay. These frameworks are nearly optimal: their competitive ratio is O?(?n), which matches known lower bounds up to logarithmic factors

Improved and Deterministic Online Service with Deadlines or Delay

We consider the problem of online service with delay on a general metric space, first presented by Azar, Ganesh, Ge and Panigrahi (STOC 2017). The best known randomized algorithm for this problem, by Azar and Touitou (FOCS 2019), is $O(\log^2 n)$-competitive, where $n$ is the number of points in the metric space. This is also the best known result for the special case of online service with deadlines, which is of independent interest. In this paper, we present $O(\log n)$-competitive deterministic algorithms for online service with deadlines or delay, improving upon the results from FOCS 2019. Furthermore, our algorithms are the first deterministic algorithms for online service with deadlines or delay which apply to general metric spaces and have sub-polynomial competitiveness.Comment: Appears in STOC 202

Online Matching with Set and Concave Delays

We initiate the study of online problems with set delay, where the delay cost at any given time is an arbitrary function of the set of pending requests. In particular, we study the online min-cost perfect matching with set delay (MPMD-Set) problem, which generalises the online min-cost perfect matching with delay (MPMD) problem introduced by Emek et al. (STOC 2016). In MPMD, m requests arrive over time in a metric space of n points. When a request arrives the algorithm must choose to either match or delay the request. The goal is to create a perfect matching of all requests while minimising the sum of distances between matched requests, and the total delay costs incurred by each of the requests. In contrast to previous work we study MPMD-Set in the non-clairvoyant setting, where the algorithm does not know the future delay costs. We first show no algorithm is competitive in n or m. We then study the natural special case of size-based delay where the delay is a non-decreasing function of the number of unmatched requests. Our main result is the first non-clairvoyant algorithms for online min-cost perfect matching with size-based delay that are competitive in terms of m. In fact, these are the first non-clairvoyant algorithms for any variant of MPMD. A key technical ingredient is an analog of the symmetric difference of matchings that may be useful for other special classes of set delay. Furthermore, we prove a lower bound of ?(n) for any deterministic algorithm and ?(log n) for any randomised algorithm. These lower bounds also hold for clairvoyant algorithms. Finally, we also give an m-competitive deterministic algorithm for uniform concave delays in the clairvoyant setting

Online Matching with Set Delay

We initiate the study of online problems with set delay, where the delay cost at any given time is an arbitrary function of the set of pending requests. In particular, we study the online min-cost perfect matching with set delay (MPMD-Set) problem, which generalises the online min-cost perfect matching with delay (MPMD) problem introduced by Emek et al. (STOC 2016). In MPMD, $m$ requests arrive over time in a metric space of $n$ points. When a request arrives the algorithm must choose to either match or delay the request. The goal is to create a perfect matching of all requests while minimising the sum of distances between matched requests, and the total delay costs incurred by each of the requests. In contrast to previous work we study MPMD-Set in the non-clairvoyant setting, where the algorithm does not know the future delay costs. We first show no algorithm is competitive in $n$ or $m$. We then study the natural special case of size-based delay where the delay is a non-decreasing function of the number of unmatched requests. Our main result is the first non-clairvoyant algorithms for online min-cost perfect matching with size-based delay that are competitive in terms of $m$. In fact, these are the first non-clairvoyant algorithms for any variant of MPMD. Furthermore, we prove a lower bound of $\Omega(n)$ for any deterministic algorithm and $\Omega(\log n)$ for any randomised algorithm. These lower bounds also hold for clairvoyant algorithms

Online Facility Location with Linear Delay

In the problem of online facility location with delay, a sequence of n clients appear in the metric space, and they need to be eventually connected to some open facility. The clients do not have to be connected immediately, but such a choice comes with a certain penalty: each client incurs a waiting cost (equal to the difference between its arrival and its connection time). At any point in time, an algorithm may decide to open a facility and connect any subset of clients to it. That is, an algorithm needs to balance three types of costs: cost of opening facilities, costs of connecting clients, and the waiting costs of clients. We study a natural variant of this problem, where clients may be connected also to an already open facility, but such action incurs an extra cost: an algorithm pays for waiting of the facility (a cost incurred separately for each such "late" connection). This is reminiscent of online matching with delays, where both sides of the connection incur a waiting cost. We call this variant two-sided delay to differentiate it from the previously studied one-sided delay, where clients may connect to a facility only at its opening time. We present an O(1)-competitive deterministic algorithm for the two-sided delay variant. Our approach is an extension of the approach used by Jain, Mahdian and Saberi [STOC 2002] for analyzing the performance of offline algorithms for facility location. To this end, we substantially simplify the part of the original argument in which a bound on the sequence of factor-revealing LPs is derived. We then show how to transform our O(1)-competitive algorithm for the two-sided delay variant to O(log n / log log n)-competitive deterministic algorithm for one-sided delays. This improves the known O(log n) bound by Azar and Touitou [FOCS 2020]. We note that all previous online algorithms for problems with delays in general metrics have at least logarithmic ratios

Online Facility Location with Linear Delay

In the problem of online facility location with delay, a sequence of n clients appear in the metric space, and they need to be eventually connected to some open facility. The clients do not have to be connected immediately, but such a choice comes with a certain penalty: each client incurs a waiting cost (equal to the difference between its arrival and its connection time). At any point in time, an algorithm may decide to open a facility and connect any subset of clients to it. That is, an algorithm needs to balance three types of costs: cost of opening facilities, costs of connecting clients, and the waiting costs of clients. We study a natural variant of this problem, where clients may be connected also to an already open facility, but such action incurs an extra cost: an algorithm pays for waiting of the facility (a cost incurred separately for each such "late" connection). This is reminiscent of online matching with delays, where both sides of the connection incur a waiting cost. We call this variant two-sided delay to differentiate it from the previously studied one-sided delay, where clients may connect to a facility only at its opening time. We present an O(1)-competitive deterministic algorithm for the two-sided delay variant. Our approach is an extension of the approach used by Jain, Mahdian and Saberi [STOC 2002] for analyzing the performance of offline algorithms for facility location. To this end, we substantially simplify the part of the original argument in which a bound on the sequence of factor-revealing LPs is derived. We then show how to transform our O(1)-competitive algorithm for the two-sided delay variant to O(log n / log log n)-competitive deterministic algorithm for one-sided delays. This improves the known O(log n) bound by Azar and Touitou [FOCS 2020]. We note that all previous online algorithms for problems with delays in general metrics have at least logarithmic ratios

Online Metric Matching with Delay

Traditionally, an online algorithm must service a request upon its arrival. In many practical situations, one can delay the service of a request in the hope of servicing it more efficiently in the near future. As a result, the study of online algorithms with delay has recently gained considerable traction. For most online problems with delay, competitive algorithms have been developed that are independent of properties of the delay functions associated with each request. Interestingly, this is not the case for the online min-cost perfect matching with delays (MPMD) problem, introduced by Emek et al.(STOC 2016). In this thesis we show that some techniques can be modified to extend to larger classes of delay functions, without affecting the competitive ratio. In the interest of designing competitive solutions for the problem in a more general setting, we introduce the study of online problems with set delay. Here, the delay cost at any time is given by an arbitrary function of the set of pending requests, rather than the sum over individual delay functions associated with each request. In particular, we study the online min-cost perfect matching with set delay (MPMD-Set) problem, which provides a generalisation of MPMD. In contrast to previous work, the new model allows us to study the problem in the non-clairvoyant setting, i.e. where the future delay costs are unknown to the algorithm. We prove that for MPMD-Set in the most general non-clairvoyant setting, there exists no competitive algorithm. Motivated by this impossibility, we introduce a new class of delay functions called sizebased and prove that for this version of the problem, there exist both non-clairvoyant deterministic and randomised algorithms that are competitive in the number of requests. Our results reveal that the quality of an online matching depends both on the algorithm's access to information about future delay costs, and the properties of the delay function

MACHINE LEARNING IN THE DESIGN SPACE EXPLORATION OF TSN NETWORKS

Real-time systems are systems that have specific timing requirements. They are critical systems that play an important role in modern societies, be it for instance control systems in factories or automotives. In recent years, Ethernet has been increasingly adopted as layer 2 protocol in real-time systems. Indeed, the adoption of Ethernet provides many benefits, including COTS and cost-effective components, high data rates and flexible topology. The main drawback of Ethernet is that it does not offer "out-of-the-box" mechanisms to guarantee timing and reliability constraints. This is the reason why time-sensitive networking (TSN) mechanisms have been introduced to provide Quality-of-Service (QoS) on top of Ethernet and satisfy the requirements of real-time communication in critical systems. The promise of Ethernet TSN is the possibility to use a single network for different criticality levels, e.g, critical control traffic and infotainment traffic sharing the same network resources. This thesis is about the design of Ethernet TSN networks, and specifically about techniques that help quantify the extent to which a network can support current and future communication needs. The context of this work is the increasing use of design-space exploration (DSE) in the industry to master the complexity of designing (e.g. in terms of architectural and technological choices) and configuring a TSN network. One of the main steps in DSE is performing schedulability analysis to conclude about the feasibility of a network configuration, i.e., whether all traffic streams satisfy their timing constraints. This step can take weeks of computations for a large set of candidate solutions with the simplest TSN mechanisms, while more complicated TSN mechanisms will require even longer time. This thesis explores the use of Artificial Intelligence (AI) techniques to assist in the design of TSN networks by speeding up the DSE. Specifically, the thesis proposes the use of machine learning (ML) as an alternative approach to schedulability analysis. The application of ML involves two steps. In the first step, ML algorithms are trained with a large set of TSN configurations labeled as feasible or non-feasible. Due to its pattern recognition ability, ML algorithms can predict the feasibility of unseen configurations with a good accuracy. Importantly, the execution time of an ML model is only a fraction of conventional schedulability analysis and remains constant whatever the complexity of the network configurations. Several contributions make up the body of the thesis. In the first contribution, we observe that the topology and the traffic of a TSN network can be used to derive simple features that are relevant to the network feasibility. Therefore, standard and simple machine learning (ML) algorithms such as k-Nearest Neighbors are used to take these features as inputs and predict the feasibility of TSN networks. This study suggests that ML algorithms can provide a viable alternative to conventional schedulability analysis due to fast execution time and high prediction accuracy. A hybrid approach combining ML and schedulability analyses is also introduced to control the prediction uncertainty. In the next studies, we aim at further automating the feasibility prediction of TSN networks with the Graph Neural Network (GNN) model. GNN takes as inputs the raw data from the TSN configurations and encodes them as graphs. Synthetic features are generated by GNN, thus the manual feature selection step is eliminated. More importantly, the GNN model can generalize to a wide range of topologies and traffic patterns, in contrast to the standard ML algorithms tested before that can only work with a fixed topology. An ensemble of individual GNN models shows high prediction accuracies on many test cases containing realistic automotive topologies. We also explore possibilities to improve the performance of GNN with more advanced deep learning techniques. In particular, semi-supervised learning and self-supervised learning are experimented. Although these learning paradigms provide modest improvements, we consider them promising techniques due to the ability to leverage the massive amount of unlabeled training data. While this thesis focuses on the feasibility prediction of TSN configurations, AI techniques have huge potentials to automate other tasks in real-time systems. A natural follow-up work of this thesis is to apply GNN to multiple TSN mechanisms and predict which mechanism can provide the best scheduling solution for a given configuration. Although we need distinct ML models for each TSN mechanism, this research direction is promising as TSN mechanisms may share similar feasibility features and thus transfer learning techniques can be applied to facilitate the training process. Furthermore, GNN can be used as a core block in deep reinforcement learning to find the feasible priority assignment of TSN configurations. This thesis aims to make a contribution towards DSE of TSN networks with AI

Principles of Security and Trust

This open access book constitutes the proceedings of the 8th International Conference on Principles of Security and Trust, POST 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2019. The 10 papers presented in this volume were carefully reviewed and selected from 27 submissions. They deal with theoretical and foundational aspects of security and trust, including on new theoretical results, practical applications of existing foundational ideas, and innovative approaches stimulated by pressing practical problems