A Content-based Centrality Metric for Collaborative Caching in Information-Centric Fogs

Information-Centric Fog Computing enables a multitude of nodes near the end-users to provide storage, communication, and computing, rather than in the cloud. In a fog network, nodes connect with each other directly to get content locally whenever possible. As the topology of the network directly influences the nodes' connectivity, there has been some work to compute the graph centrality of each node within that network topology. The centrality is then used to distinguish nodes in the fog network, or to prioritize some nodes over others to participate in the caching fog. We argue that, for an Information-Centric Fog Computing approach, graph centrality is not an appropriate metric. Indeed, a node with low connectivity that caches a lot of content may provide a very valuable role in the network. To capture this, we introduce acontent-based centrality (CBC) metric which takes into account how well a node is connected to the content the network is delivering, rather than to the other nodes in the network. To illustrate the validity of considering content-based centrality, we use this new metric for a collaborative caching algorithm. We compare the performance of the proposed collaborative caching with typical centrality based, non-centrality based, and non-collaborative caching mechanisms. Our simulation implements CBC on three instances of large scale realistic network topology comprising 2,896 nodes with three content replication levels. Results shows that CBC outperforms benchmark caching schemes and yields a roughly 3x improvement for the average cache hit rate

Stochastic characterization of the spectrum sharing game in ad-hoc networks

Abstract This work focuses on infrastructure-less ad hoc wireless networks where multiple transmitter/receiver pairs share the same radio resources (spectrum); transmitters have to choose how to split a total power budget across orthogonal spectrum bands with the goal to maximize their sum rate under cumulative interference from concurrent transmissions. We start off by introducing and characterizing the non-cooperative game among transmitter/receiver pairs when the network topology is deterministically given. The corresponding Nash equilibria are derived, highlighting their dependency on the topological parameters (distances between wireless nodes, propagation model, and background noise power). The analysis is then extended to the case of random network topologies drawn from a given spatial stochastic process. Tools of stochastic geometry are leveraged to derive a statistical characterization of the equilibria of the spectrum sharing game. Finally, a distributed algorithm is proposed to let the players of the spectrum sharing game converge to equilibria conditions. Numerical simulations show that the proposed algorithm drives the users to stable points that are close to the equilibria of the game requiring limited information exchange among nodes

Network Selection and Resource Allocation Games for Wireless Access Networks

Wireless access networks are often characterized by the interaction of different end users, communication technologies, and network operators. This paper analyzes the dynamics among these "actors" by focusing on the processes of wireless network selection, where end users may choose among multiple available access networks to get connectivity, and resource allocation, where network operators may set their radio resources to provide connectivity. The interaction among end users is modeled as a non-cooperative congestion game where players (end users) selfishly select the access network that minimizes their perceived selection cost. A method based on mathematical programming is proposed to find Nash equilibria and characterize their optimality under three cost functions, which are representative of different technological scenarios. System level simulations are then used to evaluate the actual throughput and fairness of the equilibrium points. The interaction among end users and network operators is then assessed through a two-stage multi-leader/multi-follower game, where network operators (leaders) play in the first stage by properly setting the radio resources to maximize their users, and end users (followers) play in the second stage the aforementioned network selection game. The existence of exact and approximated subgame perfect Nash equilibria of the two-stage game is thoroughly assessed and numerical results are provided on the "quality" of such equilibria

Formal methods for analysing, coordinating, and controlling decisions in multi-agent systems

Multiagentensysteme sind verteilte (Computer)Systeme, die sich aus autonomen interagierenden Systemkomponenten, bezeichnet als Agenten, zusammensetzen. Sie bieten ein flexibles Framework zur Modellierung und Analyse von interaktiven Systemen, in denen Kooperation, Eigeninteresse und Autonomie eine entscheidende Rolle spielen. Dies ist zum Beispiel der Fall in Smart Grids. Eine Herausforderung in solchen Systemen ist die Kontrolle und die Koordination von Systemausführungen. Agenten handeln autonom und lassen sich daher oftmals nicht direkt kontrollieren, sondern bestenfalls beeinflussen. Aufgrund der Autonomie und des Selbstinteresses, ist es schwierig, angemessene Kontrollmechanismen zu finden. Die vorliegende Arbeit behandelt formale Grundlagen zu den Themen Entscheidungsfindung, Koordination und Kontrolle in Multiagentensystemen. Insbesondere werden in diesem Zusammenhang Logiken zur Analyse und Spezifikation von strategischen Fähigkeiten von Agenten, unter diversen Restriktionen, untersucht. Es werden formale Ansätze zur Beeinflussung und Überwachung von Systemausführungen eingeführt. In einem weiteren Teil der Arbeit wird mittels spieltheoretischer Verfahren analysiert, wie rationale Agenten interagieren und Entscheidungen treffen. Es wird argumentiert, dass formale Methoden und Werkzeuge zur Analyse und Kontrolle von autonomen Systemen entscheidend für deren verlässliche Entwicklung sind.Multi-agent systems (MASs) are distributed (computer) systems composed of autonomously (inter-)acting system components referred to as agents. MASs offer a flexible framework to model and analyse many real world settings in which cooperation, self-interest, and autonomy are crucial elements. A key challenge in such settings is the control and coordination of behavior. However, due to the agents' autonomy behavior can often not be controlled, but at best be influenced in some way or another. For example, agents can be given incentives in order to affect their decision-making in such a way that the emergent behavior of all actors is desirable from the system's perspective. The properties of self-interest and autonomy make it challenging to find appropriate control mechanisms. Existing coordination and control approaches from the distributed system literature are often not applicable due to the lack of direct control on the system components of MASs. New methods and tools are needed. In this thesis formal foundations related to the subjects of decision making, coordination and control in MASs are studied. In particular, we investigate (extensions of) temporal and strategic logics which capture specific capabilities of agents that influence their decision making. We also propose formal approaches to control, coordinate and monitor the emergent behavior in MASs. In the last part of the thesis we analyse how rational agents interact and make decisions using game theoretical methods. We argue that such formal approaches and tools to analyse and control autonomous systems are crucial for the development of reliable and flexible systems and will become even more crucial in the near future