Gossip, Sexual Recombination and the El Farol Bar: modelling the emergence of heterogeneity

Brian Arthur's `El Farol Bar' model is extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process acting upon a population of mental models inside each agent. The evolutionary process is based on a Genetic Programming algorithm. Each gene is composed of two tree-structures: one to control its action and one to determine its communication. A detailed case-study from the simulations show how the agents have differentiated so that by the end of the run they had taken on very different roles. Thus the introduction of a flexible learning process and an expressive internal representation has allowed the emergence of heterogeneity

AcTinG: Accurate Freerider Tracking in Gossip

Abstract-Gossip-based content dissemination protocols are a scalable and cheap alternative to centralised content sharing systems. However, it is well known that these protocols suffer from rational nodes, i.e., nodes that aim at downloading the content without contributing their fair share to the system. While the problem of rational nodes that act individually has been well addressed in the literature, colluding rational nodes is still an open issue. Indeed, LiFTinG, the only existing gossip protocol addressing this issue, yields a high ratio of false positive accusations of correct nodes. In this paper, we propose AcTinG, a protocol that prevents rational collusions in gossip-based content dissemination protocols, while guaranteeing zero false positive accusations. We assess the performance of AcTinG on a testbed comprising 400 nodes running on 100 physical machines, and compare its behaviour in the presence of colluders against two state-of-the-art protocols: BAR Gossip that is the most robust protocol handling non-colluding rational nodes, and LiFTinG, the only existing gossip protocol that handles colluding nodes. The performance evaluation shows that AcTinG is able to deliver all messages despite the presence of colluders, whereas both LiFTinG and BAR Gossip suffer heavy message loss. It also shows that AcTinG is resilient to massive churn. Finally, using simulations involving up to a million nodes, we show that AcTinG exhibits similar scalability properties as standard gossip-based dissemination protocols

Robust peer-to-peer systems

textPeer-to-peer (p2p) approaches are an increasingly effective way to deploy services. Popular examples include BitTorrent, Skype, and KaZaA. These approaches are attractive because they can be highly fault-tolerant, scalable, adaptive, and less expensive than a more centralized solution. Cooperation lies at the heart of these strengths. Yet, in settings where working together is crucial, a natural question is: "What if users stop cooperating?" After all, cooperative services are typically deployed over multiple administrative domains, and thus vulnerable to Byzantine failures and users who may act selfishly. This dissertation explores how to construct p2p systems to tolerate Byzantine participants while also incentivizing selfish participants to contribute resources. We describe how to balance obedience against choice in building a robust p2p live streaming system. Imposing obedience is desirable as it leaves little room for peers to attack or cheat the system. However, providing choice is also attractive as it allows us to engineer flexible and efficient solutions. We first focus on obedience by using Nash equilibria to drive the design of BAR Gossip, the first gossip protocol that is resilient to Byzantine and selfish nodes. BAR Gossip relies on verifiable pseudo-random partner selection to eliminate non-determinism, which can be used to game the system, while maintaining the robustness and rapid convergence of traditional gossip. A novel fair enough exchange primitive entices cooperation among selfish peers on short timescales, thereby avoiding the need for distributed reputation schemes. We next focus on tempering obedience with choice by using approximate equilibria to guide the construction of a novel p2p live streaming system. These equilibria allow us to design incentives to limit selfish behavior rigorously, yet provide sufficient flexibility to build practical systems. We show the advantages of using an [element of]-Nash equilibrium, instead of an exact Nash, to design and implement FlightPath, our live streaming system that uses bandwidth efficiently, absorbs flash crowds, adapts to sudden peer departures, handles churn, and tolerates malicious activity.Computer Science

Visibility, Gossip and Intimate Neighbourly Knowledges (Findings paper no. 7)

Findings papers associated with ESRC-funded research project, 'Social Geographies of Rural Mental Health' (R000 23 8453)

Distributed Data Summarization in Well-Connected Networks

We study distributed algorithms for some fundamental problems in data summarization. Given a communication graph G of n nodes each of which may hold a value initially, we focus on computing sum_{i=1}^N g(f_i), where f_i is the number of occurrences of value i and g is some fixed function. This includes important statistics such as the number of distinct elements, frequency moments, and the empirical entropy of the data. In the CONGEST~ model, a simple adaptation from streaming lower bounds shows that it requires Omega~(D+ n) rounds, where D is the diameter of the graph, to compute some of these statistics exactly. However, these lower bounds do not hold for graphs that are well-connected. We give an algorithm that computes sum_{i=1}^{N} g(f_i) exactly in {tau_{G}} * 2^{O(sqrt{log n})} rounds where {tau_{G}} is the mixing time of G. This also has applications in computing the top k most frequent elements. We demonstrate that there is a high similarity between the GOSSIP~ model and the CONGEST~ model in well-connected graphs. In particular, we show that each round of the GOSSIP~ model can be simulated almost perfectly in O~({tau_{G}}) rounds of the CONGEST~ model. To this end, we develop a new algorithm for the GOSSIP~ model that 1 +/- epsilon approximates the p-th frequency moment F_p = sum_{i=1}^N f_i^p in O~(epsilon^{-2} n^{1-k/p}) roundsfor p >= 2, when the number of distinct elements F_0 is at most O(n^{1/(k-1)}). This result can be translated back to the CONGEST~ model with a factor O~({tau_{G}}) blow-up in the number of rounds

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Gossip-based service monitoring platform for wireless edge cloud computing

Edge cloud computing proposes to support shared services, by using the infrastructure at the network's edge. An important problem is the monitoring and management of services across the edge environment. Therefore, dissemination and gathering of data is not straightforward, differing from the classic cloud infrastructure. In this paper, we consider the environment of community networks for edge cloud computing, in which the monitoring of cloud services is required. We propose a monitoring platform to collect near real-time data about the services offered in the community network using a gossip-enabled network. We analyze and apply this gossip-enabled network to perform service discovery and information sharing, enabling data dissemination among the community. We implemented our solution as a prototype and used it for collecting service monitoring data from the real operational community network cloud, as a feasible deployment of our solution. By means of emulation and simulation we analyze in different scenarios, the behavior of the gossip overlay solution, and obtain average results regarding information propagation and consistency needs, i.e. in high latency situations, data convergence occurs within minutes.Peer ReviewedPostprint (author's final draft