On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge

Reitinsuunnittelu määrätyssä järjestyksessä tehtäville peltotöille usean työkoneen yhteistyönä

Coverage path planning is the task of finding a collision free path that passes over every point of an area or volume of interest. In agriculture, the coverage task is encountered especially in the process of crop cultivation. Several tasks are performed on the field, one after the other, during the cultivation cycle. Cooperation means that multiple agents, in this case vehicles, are working together towards a common goal. Several studies consider the problem where a single task is divided and assigned among the agents. In this thesis, however, the vehicles have different tasks that are sequentially dependent, that is, the first task must be completed before the other. The tasks are performed simultaneously on the same area. The literature review suggests that there is a lack of previous research on this topic. The objective of this thesis was to develop an algorithm to solve the cooperative coverage path planning problem for sequentially dependent tasks. A tool chain that involves Matlab, Simulink and Visual Studio was adapted for the development and testing of the solution. A development and testing architecture was designed including a compatible interface to a simulation and a real-life test environment. Two different algorithms were implemented based on the idea of computing short simultaneous paths at a time and scheduling them in real-time. The results were successfully demonstrated in a real-life test environment with two tractors equipped with a disc cultivator and a seeder. The objective was to sow the test area. The test drives show that with the algorithms that were developed in this thesis it is possible to perform two sequentially dependent agricultural coverage tasks simultaneously on the same area.Kattavassa reitinsuunnittelussa yritetään löytää polku, jonka aikana määritelty ala tai tilavuus tulee käytyä läpi niin että alueen jokainen piste on käsitelty. Maataloudessa tämä tehtävä on merkityksellinen erityisesti peltoviljelyssä. Useita peltotöitä suoritetaan yksi toisensa jälkeen samalla alueella viljelyvuoden aikana. Useissa tutkimuksissa käsitellään yhteistyönä tehtävää reitinsuunnittelua, jossa yksi tehtävä on jaettu osiin ja osat jaetaan useiden tekijöiden kuten robottien kesken. Tässä diplomityössä peltotyökoneilla on kuitenkin omat erilliset tehtävänsä, joilla on määrätty järjestys, eli niiden suorittaminen riippuu työjärjestyksestä. Työkoneet työskentelevät samanaikaisesti samalla alueella. Diplomityössä tehty kirjallisuuskatsaus viittaa siihen, että vastaavaa aihetta ei ole aiemmin tutkittu. Tämän diplomityön tavoitteena on kehittää algoritmi, jolla voidaan toteuttaa reitinsuunnittelu määrätyssä järjestyksessä tehtäville peltotöille usean peltotyökoneen yhteistyönä. Algoritmikehitystä ja testausta varten suunniteltiin yhtenäinen rajapinta, jolla algoritmia voitaisiin testata sekä simulaatiossa että todellisessa testitilanteessa. Algoritmikehityksessä käytettiin työkaluina Matlab, Simulink ja Visual Studio -ohjelmia. Työssä toteutettiin kaksi algoritmia, jotka perustuvat samaan ideaan: suunnitellaan kerrallaan kaksi lyhyttä samanaikaista polkua, jotka ajoitetaan reaaliajassa. Algoritmeja testattiin todellisessa testiympäristössä kahden työkoneen yhteistyönä, kun tavoitteena on kylvää koko testialue. Ensimmäinen työvaihe suoritettiin lautasmuokkaimella ja toinen kylvökoneella. Testiajot osoittavat, että diplomityössä kehitetyillä algoritmeilla voidaan ohjata kahden toisistaan riippuvaisen peltotyön toteutus samanaikaisesti samalla peltoalueella

Routing on the Channel Dependency Graph:: A New Approach to Deadlock-Free, Destination-Based, High-Performance Routing for Lossless Interconnection Networks

In the pursuit for ever-increasing compute power, and with Moore's law slowly coming to an end, high-performance computing started to scale-out to larger systems. Alongside the increasing system size, the interconnection network is growing to accommodate and connect tens of thousands of compute nodes. These networks have a large influence on total cost, application performance, energy consumption, and overall system efficiency of the supercomputer. Unfortunately, state-of-the-art routing algorithms, which define the packet paths through the network, do not utilize this important resource efficiently. Topology-aware routing algorithms become increasingly inapplicable, due to irregular topologies, which either are irregular by design, or most often a result of hardware failures. Exchanging faulty network components potentially requires whole system downtime further increasing the cost of the failure. This management approach becomes more and more impractical due to the scale of today's networks and the accompanying steady decrease of the mean time between failures. Alternative methods of operating and maintaining these high-performance interconnects, both in terms of hardware- and software-management, are necessary to mitigate negative effects experienced by scientific applications executed on the supercomputer. However, existing topology-agnostic routing algorithms either suffer from poor load balancing or are not bounded in the number of virtual channels needed to resolve deadlocks in the routing tables. Using the fail-in-place strategy, a well-established method for storage systems to repair only critical component failures, is a feasible solution for current and future HPC interconnects as well as other large-scale installations such as data center networks. Although, an appropriate combination of topology and routing algorithm is required to minimize the throughput degradation for the entire system. This thesis contributes a network simulation toolchain to facilitate the process of finding a suitable combination, either during system design or while it is in operation. On top of this foundation, a key contribution is a novel scheduling-aware routing, which reduces fault-induced throughput degradation while improving overall network utilization. The scheduling-aware routing performs frequent property preserving routing updates to optimize the path balancing for simultaneously running batch jobs. The increased deployment of lossless interconnection networks, in conjunction with fail-in-place modes of operation and topology-agnostic, scheduling-aware routing algorithms, necessitates new solutions to solve the routing-deadlock problem. Therefore, this thesis further advances the state-of-the-art by introducing a novel concept of routing on the channel dependency graph, which allows the design of an universally applicable destination-based routing capable of optimizing the path balancing without exceeding a given number of virtual channels, which are a common hardware limitation. This disruptive innovation enables implicit deadlock-avoidance during path calculation, instead of solving both problems separately as all previous solutions

Adaptive Route Choice in Stochastic Time-Dependent Networks: Routing Algorithms and Choice Modeling

Transportation networks are inherently uncertain due to random disruptions; meanwhile, real-time information potentially helps travelers adapt to realized traffic conditions and make better route choices under such disruptions. Modeling adaptive route choice behavior is essential in evaluating Advanced Traveler Information Systems (ATIS) and related policies to better provide travelers with real-time information. This dissertation contributes to the state of the art by estimating the first latent-class routing policy choice model using revealed preference (RP) data and providing efficient computer algorithms for routing policy choice set generation. A routing policy is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions on the link to take next. It represents a traveler\u27s ability to look ahead in order to incorporate real-time information not yet available at the time of decision. A case study is conducted in Stockholm, Sweden and data for the stochastic time-dependent network are generated from hired taxi Global Positioning System (GPS) traces through the methods of map-matching and non-parametric link travel time estimation. A latent-class Policy Size Logit model is specified with two additional layers of latency in the measurement equation. The two latent classes of travelers are policy users who follow routing policies and path users who follow fixed paths. For the measurement equation of the policy user class, the choice of a routing policy is latent and only its realized path on a given day can be observed. Furthermore, when GPS traces have relatively long gaps between consecutive readings, the realized path cannot be uniquely identified. Routing policy choice set generation is based on the generalization of path choice set generation methods, and utilizes efficient implementation of an optimal routing policy (ORP) algorithm based on the two-queue data structure for label correcting. Systematic evaluation of the algorithm in random networks as well as in two large scale real-life networks is conducted. The generated choice sets are evaluated based on coverage and adaptiveness. Coverage is the percentage of observed trips included in the generated choice sets based on a certain threshold of overlapping between observed and generated routes, and adaptiveness represents the capability of a routing policy to be realized as different paths over different days. It is shown that using a combination of methods yields satisfactory coverage of 91.2%. Outlier analyses are then carried out for unmatching trips in choice set generation. The coverage achieves 95% for 100% threshold after correcting GPS errors and breaking up trips with intermediate stops, and further achieves 100% for 90% threshold. The latent-class routing policy choice model is estimated against observed GPS traces based on the three different sample sizes resulting from coverage improvement, and the estimates appear consistent across different sample sizes. Estimation results show the policy user class probability increases with trip length, and the latent-class routing policy choice model fits the data better than a single-class path choice model or routing policy choice model. This suggests that travelers are heterogeneous in terms of their ability and willingness to plan ahead and utilize real-time information. Therefore, a fixed path model as commonly used in the literature may lose explanatory power due to its simplified assumptions on network stochasticity and travelers\u27 utilization of real-time information

Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

Adaptive Railway Traffic Control using Approximate Dynamic Programming

Railway networks around the world have become challenging to operate in recent decades, with a mixture of track layouts running several different classes of trains with varying operational speeds. This complexity has come about as a result of the sustained increase in passenger numbers where in many countries railways are now more popular than ever before as means of commuting to cities. To address operational challenges, governments and railway undertakings are encouraging development of intelligent and digital transport systems to regulate and optimise train operations in real-time to increase capacity and customer satisfaction by improved usage of existing railway infrastructure. Accordingly, this thesis presents an adaptive railway traffic control system for realtime operations based on a data-based approximate dynamic programming (ADP) approach with integrated reinforcement learning (RL). By assessing requirements and opportunities, the controller aims to reduce delays resulting from trains that entered a control area behind schedule by re-scheduling control plans in real-time at critical locations in a timely manner. The present data-based approach depends on an approximation to the value function of dynamic programming after optimisation from a specified state, which is estimated dynamically from operational experience using RL techniques. By using this approximation, ADP avoids extensive explicit evaluation of performance and so reduces the computational burden substantially. In this thesis, formulations of the approximation function and variants of the RL learning techniques used to estimate it are explored. Evaluation of this controller shows considerable improvements in delays by comparison with current industry practices

Flood Impacts on Road Transportation

Flood disasters can penetrate every single aspect of human life and road transportation is no exception. However, flood impacts on road transportation is an area that has not been explored in detail in the past. The focus of this PhD study is on the performance assessment of a road network subject to flooding. In this work, several challenges were overcome with original ideas. The first was integrating the flood and the transport systems - both exhibiting strong temporal and spatial variations. This has been successfully achieved by implementing a novel methodology into a tool that modelled flood intensities output into a transport network constraint in a traffic model. The logic of the framework is intuitive – roads with shallow flood depth impose speed limitations, and roads with deep flood depth are closed for traffic. The developed tool enabled a quick and consistent technique to integrate the flood and the transport models in three different ways – static, semi-dynamic and dynamic. The static integration considers only one flood map to determine traffic conditions, whereas the semi-dynamic and the dynamic integrations use multiple maps to mimic the flood propagation in the traffic model. This thesis is the first to achieve semi-dynamic or dynamic integration of the two models. The second challenge was the assessment of the impacts. Intangible impacts such as travel delays propagating as knock-on effects can easily be misrepresented or even misunderstood. Employing a microscopic transport model allows for the assessment of direct effects and the knock-on consequences on individual drivers as well as the whole traffic system. Results in one case study suggest that the average travel time rose with 45% on average for 75% of the vehicles in the most affected hour of the simulation. The monetary value of traffic delays may not be as significant as the flood direct tangible damage, but flood impacts on road transportation may be more straightforward to alleviate if traffic authorities follow contingency plans to reduce traffic demand or mitigate potential interruptions of traffic supply. To analyse how potential interventions affect the transport system performance, three interventions were implemented into the model. The third challenge was the evaluation of the performance of a road transport system and the assessment of its resilience to flooding. Perusing this, a novel rationale to assess the resilience of a transport network has been developed. This method distinguishes reliability from resilience to define the nonlinear bounds of standard dry weather conditions, and any fluctuation beyond these bounds is defined as exceptional conditions. By separating reliability from resilience, the extent of both magnitude and duration is refined and contributes to better understanding of the system performance. This PhD thesis aspires to bridge the gap between flooding and traffic by providing a workable open source tool, which can be applied to other case studies and thus open the potential for further development in that area. As well as practical ideas, the theoretical contribution in assessing system resilience can be applied in other fields.European Commissio

SLA Management in Intent-Driven Service Management Systems: A Taxonomy and Future Directions

Traditionally, network and system administrators are responsible for designing, configuring, and resolving the Internet service requests. Human-driven system configuration and management are proving unsatisfactory due to the recent interest in time-sensitive applications with stringent quality of service (QoS). Aiming to transition from the traditional human-driven to zero-touch service management in the field of networks and computing, intent-driven service management (IDSM) has been proposed as a response to stringent quality of service requirements. In IDSM, users express their service requirements in a declarative manner as intents. IDSM, with the help of closed control-loop operations, perform configurations and deployments, autonomously to meet service request requirements. The result is a faster deployment of Internet services and reduction in configuration errors caused by manual operations, which in turn reduces the service-level agreement (SLA) violations. In the early stages of development, IDSM systems require attention from industry as well as academia. In an attempt to fill the gaps in current research, we conducted a systematic literature review of SLA management in IDSM systems. As an outcome, we have identified four IDSM intent management activities and proposed a taxonomy for each activity. Analysis of all studies and future research directions, are presented in the conclusions.Comment: Extended version of the preprint submitted at ACM Computing Surveys (CSUR

Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks

Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that, rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. While the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, e.g., to increase performance and to make a running system more robust. We instantiate and empirically examine the above idea for software-defined networking -- a key enabling technology for modern data centres and Internet of Things applications. Using genetic programming,(GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open-source synthetic and industrial data, indicates that, compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, reduces the frequency of adaptation interventions over time. In addition, we show that, for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.Comment: arXiv admin note: text overlap with arXiv:2205.0435