Environmental analysis for application layer networks

Die zunehmende Vernetzung von Rechnern über das Internet lies die Vision von Application Layer Netzwerken aufkommen. Sie umfassen Overlay Netzwerke wie beispielsweise Peer-to-Peer Netzwerke und Grid Infrastrukturen unter Verwendung des TCP/IP Protokolls. Ihre gemeinsame Eigenschaft ist die redundante, verteilte Bereitstellung und der Zugang zu Daten-, Rechen- und Anwendungsdiensten, während sie die Heterogenität der Infrastruktur vor dem Nutzer verbergen. In dieser Arbeit werden die Anforderungen, die diese Netzwerke an ökonomische Allokationsmechanismen stellen, untersucht. Die Analyse erfolgt anhand eines Marktanalyseprozesses für einen zentralen Auktionsmechanismus und einen katallaktischen Markt. --Grid Computing

Non-Cooperative Scheduling of Multiple Bag-of-Task Applications

Multiple applications that execute concurrently on heterogeneous platforms compete for CPU and network resources. In this paper we analyze the behavior of $K$ non-cooperative schedulers using the optimal strategy that maximize their efficiency while fairness is ensured at a system level ignoring applications characteristics. We limit our study to simple single-level master-worker platforms and to the case where each scheduler is in charge of a single application consisting of a large number of independent tasks. The tasks of a given application all have the same computation and communication requirements, but these requirements can vary from one application to another. In this context, we assume that each scheduler aims at maximizing its throughput. We give closed-form formula of the equilibrium reached by such a system and study its performance. We characterize the situations where this Nash equilibrium is optimal (in the Pareto sense) and show that even though no catastrophic situation (Braess-like paradox) can occur, such an equilibrium can be arbitrarily bad for any classical performance measure

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