Search Trajectory Networks Applied to the Cyclic Bandwidth Sum Problem

Search trajectory networks (STNs) were proposed as a tool to analyze the behavior of metaheuristics in relation to their exploration ability and the search space regions they traverse. The technique derives from the study of fitness landscapes using local optima networks (LONs). STNs are related to LONs in that both are built as graphs, modelling the transitions among solutions or group of solutions in the search space. The key difference is that STN nodes can represent solutions or groups of solutions that are not necessarily locally optimal. This work presents an STN-based study for a particular combinatorial optimization problem, the cyclic bandwidth sum minimization. STNs were employed to analyze the two leading algorithms for this problem: a memetic algorithm and a hyperheuristic memetic algorithm. We also propose a novel grouping method for STNs that can be generally applied to both continuous and combinatorial spaces

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Contextual Bandit Modeling for Dynamic Runtime Control in Computer Systems

Modern operating systems and microarchitectures provide a myriad of mechanisms for monitoring and affecting system operation and resource utilization at runtime. Dynamic runtime control of these mechanisms can tailor system operation to the characteristics and behavior of the current workload, resulting in improved performance. However, developing effective models for system control can be challenging. Existing methods often require extensive manual effort, computation time, and domain knowledge to identify relevant low-level performance metrics, relate low-level performance metrics and high-level control decisions to workload performance, and to evaluate the resulting control models. This dissertation develops a general framework, based on the contextual bandit, for describing and learning effective models for runtime system control. Random profiling is used to characterize the relationship between workload behavior, system configuration, and performance. The framework is evaluated in the context of two applications of progressive complexity; first, the selection of paging modes (Shadow Paging, Hardware-Assisted Page) in the Xen virtual machine memory manager; second, the utilization of hardware memory prefetching for multi-core, multi-tenant workloads with cross-core contention for shared memory resources, such as the last-level cache and memory bandwidth. The resulting models for both applications are competitive in comparison to existing runtime control approaches. For paging mode selection, the resulting model provides equivalent performance to the state of the art while substantially reducing the computation requirements of profiling. For hardware memory prefetcher utilization, the resulting models are the first to provide dynamic control for hardware prefetchers using workload statistics. Finally, a correlation-based feature selection method is evaluated for identifying relevant low-level performance metrics related to hardware memory prefetching

Multichannel power line communication

Power line communication (PLC) is the technology in which the data signals of a communication system are transmitted through the conductors of a power delivery infrastructure. The unique environment of the PLC channels create specific challenges and requirements, which need to be modeled and analyzed properly in order to obtain a clear understanding of the communication system as well as attaining the ability to further improve the performance and reliability of the transmission. Moreover, the demand for increased data throughput as well as increased reliability and robustness of the transmission is of fundamental importance in any communication system as it is in PLC systems. In order to address these challenges and demands, the concept of multichannel PLC is studied and developed in this thesis. Multichannel PLC in this context is referred to the transmission of multiple information-carrying signals though the power line channel from one source to one destination. We study multiple scenarios of multichannel data transmission in order to cover the diverse situations and requirements of a PLC transmission. One of the multichannel scenarios discussed in this thesis is the multiple-input multiple-output (MIMO) transmission, in which multiple data signals are transmitted via spatially separated PLC channels. Another scenario discussed in this thesis is the cooperative transmission between the source and destination of a PLC system by means of intermediate relay nodes in the network. Finally, the multiband transmission by utilizing different parts of the available PLC spectrum is studied. The core objective of this thesis is to develop and study novel algorithms and models to address the challenges and problems introduced in different scenarios of the multichannel PLC. These problems can be categorized as the channel selection problem for MIMO transmission, the relay selection problem for the cooperative communication, and the spectrum assignment problem for the multiband transmission. The basis of all these problems is a decision making problem, which can greatly influence the performance of the system. To address these decision making problems, a powerful mathematical tool, namely the multi-armed bandit model, is used to model the different problems emerging in different scenarios of the multichannel PLC. This modeling approach is then used as a building block for developing machine learning algorithms in order to solve the aforementioned selection problems. Finally, novel machine learning algorithms are developed and their performances are analyzed and assessed. It is shown that the machine learning approach can considerably improve the performance of the multichannel PLC systems compared to the existing state of the art approaches, by enabling the selecting agent, i.e. the PLC transmitter, to perform intelligent decisions which improves the overall performance.Die Power-Line-Communication (PLC) ist die Technologie, bei der die Datensignale eines Kommunikationssystems über die Leiter einer Energieversorgungsinfrastruktur übertragen werden. Die einzigartige Umgebung der PLC-Kanäle stellt konkrete Herausforderungen und Anforderungen dar, die modelliert und analysiert werden müssen, um ein klares Verständnis des Kommunikationssystems zu erhalten und die Fähigkeit zur Verbesserung der Leistung und Zuverlässigkeit der Übertragung zu erreichen. Darüber hinaus ist in Kommunikationssystem die Nachfrage nach erhöhtem Datendurchsatz, sowie erhöhter Zuverlässigkeit und Robustheit der Übertragung von grundlegender Bedeutung. Um diesen Herausforderungen und Anforderungen gerecht zu werden, wird in dieser Arbeit das Konzept der Mehrkanal-PLC untersucht und weiterentwickelt. Die Mehrkanal-PLC wird in diesem Zusammenhang auf die Übertragung mehrerer informationstragenden Signale über den PLC-Kanal von einer Quelle zu einem Ziel bezogen. Wir untersuchen mehrere Szenarien der Mehrkanal-Datenübertragung, um die vielfältigen Anforderungen einer PLC-Übertragung zu behandeln. Eines der in dieser Arbeit besprochenen Mehrkanal-Szenarien ist die Multiple-Input-Multiple-Output-Übertragung (MIMO), bei der mehrere Datensignale über räumlich getrennte PLC-Kanäle übertragen werden. Ein weiteres Szenario, das in dieser Arbeit diskutiert wird, ist die kooperative Übertragung zwischen der Quelle und dem Ziel eines PLC-Systems mittels Zwischenrelais als Knoten im Netzwerk. Schließlich wird die Multiband-Übertragung unter Verwendung unterschiedlicher Teile des verfügbaren PLC-Spektrums untersucht. Das Kernziel dieser Arbeit ist es, neuartige Algorithmen und Modelle zu entwickeln und zu untersuchen, um die Herausforderungen und Probleme zu lösen, die in verschiedenen Szenarien der Mehrkanal-PLC existieren. Diese Probleme sind als das Kanalauswahlproblem für die MIMO-Übertragung, das Relaiauswahlproblem für die kooperative Kommunikation und das Spektrum-Zuweisungsproblem für die Multibandübertragung kategorisiert werden. Die Basis all dieser Probleme ist ein Entscheidungsproblem, das die Leistungsfähigkeit des Systems stark beeinflussen kann. Um diese Probleme lösen zu können, wird ein mathematisches Werkzeug, nämlich das mehrarmige Bandit-Modell, verwendet, um die verschiedenen Probleme zu modellieren, die sich in verschiedenen Szenarien der Mehrkanal-PLC ergeben. Dieser Modellierungsansatz wird als Baustein für die Entwicklung von maschinellen Lernalgorithmen verwendet, um die zuvor beschriebenen Auswahlprobleme zu lösen. Schließlich werden neuartige maschinelle Lernalgorithmen entwickelt und ihre Leistungen analysiert sowie bewertet. Es zeigt sich, dass der maschinelle Lernansatz die Leistungsfähigkeit der Mehrkanal-PLC-Systeme im Vergleich zu den bestehenden Ans\"atzen des Standes der Technik erheblich verbessern kann, indem es dem Auswahlagenten, d.h. dem PLC-Sender, ermöglicht, intelligente Entscheidungen durchzuführen, die die Gesamtleistung verbessern