Geoffrey Canright

Design patterns from biology for distributed computing

Recent developments in information technology have brought about important changes in distributed computing. New environments such as massively large-scale, wide-area computer networks and mobile ad hoc networks have emerged. Common characteristics of these environments include extreme dynamicity, unreliability, and large scale. Traditional approaches to designing distributed applications in these environments based on central control, small scale, or strong reliability assumptions are not suitable for exploiting their enormous potential. Based on the observation that living organisms can effectively organize large numbers of unreliable and dynamically-changing components (cells, molecules, individuals, etc.) into robust and adaptive structures, it has long been a research challenge to characterize the key ideas and mechanisms that make biological systems work and to apply them to distributed systems engineering. In this article we propose a conceptual framework that captures several basic biological processes in the form of a family of design patterns. Examples include plain diffusion, replication, chemotaxis, and stigmergy. We show through examples how to implement important functions for distributed computing based on these patterns. Using a common evaluation methodology, we show that our bio-inspired solutions have performance comparable to traditional, state-of-the-art solutions while they inherit desirable properties of biological systems including adaptivity and robustness.

Spreading on Networks: A Topographic View

We apply our previously developed method of ‘topographic’ analysis of networks to the problem of epidemic spreading. We consider the simplest form of epidemic spreading, namely the ‘SI’ model. We argue that the eigenvector centrality of a node is a good indicator of that node’s spreading power. From this we develop seven specific predictions. In particular, we predict that each region (as defined by our approach) will have its own S curve for cumulative adoption over time, and we describe the various phases of the S curve in terms of motion of the infection over the region. Our predictions are well supported by simulations. In particular, the significance of regions to epidemic spreading is clear. Finally, we develop a mathematical theory, giving partial support to our picture. The theory includes a precise quantitative definition of the spreading power of a node, and some approximate analytical results for epidemic spreading.

Asynchronous distributed power iteration with gossip-based normalization

The dominant eigenvector of matrices defined by weighted links in overlay networks plays an important role in many peer-to-peer applications. Examples include trust management, importance ranking to support search, and virtual coordinate systems to facilitate managing network proximity. Robust and efficient asynchronous distributed algorithms are known only for the case when the dominant eigenvalue is exactly one. We present a fully distributed algorithm for a more general case: non-negative square matrices that have an arbitrary dominant eigenvalue. The basic idea is that we apply a gossip-based aggregation protocol coupled with an asynchronous iteration algorithm, where the gossip component controls the iteration component. The norm of the resulting vector is an unknown finite constant by default; however, it can optionally be set to any desired constant using a third gossip control component. Through extensive simulation results on artificially generated overlay networks and real web traces we demonstrate the correctness, the performance and the fault tolerance of the protocol.

Design of an Efficient Search Algorithm for P2P Networks Using Concepts from Natural Immune Systems

In this paper we report a novel and efficient algorithm for searching p2p networks. The algorithm, termed ImmuneSearch, draws its basic inspiration from natural immune systems. It is implemented independently by each individual peer participating in the network and is totally decentralized in nature. ImmuneSearch avoids query message flooding; instead it uses an immune systems inspired concept of affinity-governed proliferation and mutation for message movement. In addition, a protocol is formulated to change the neighborhoods of the peers based upon their proximity with the queried item. This results in topology evolution of the network whereby similar contents cluster together. The topology evolution coupled with proliferation and mutation help the p2p network to develop ‘memory’, as a result of which the search efficiency of the network improves as more and more individual peers perform search. Moreover, the algorithm is extremely robust and its performance is stable in face of the transient nature of the constituent peers.

Chemotaxis-Inspired Load Balancing

We present an approach to the problem of load balancing on networks of nodes. Our approach is inspired by the phenomenon of negative chemotaxis in living systems. We use a diffusing signal (which is emitted by load, and moves faster than the load) to guide the movement of load towards the balanced state. Our reference system (for comparison) is unguided, diffusing load, moving at the same speed. Our tests show that the chemotaxis system can give large improvements over the reference system in convergence speed, as well as showing much reduced sensitivity to variations in network topology and in initial load distribution.

Eigenvector Centrality in Highly Partitioned Mobile Networks: Principles and Applications

Telenor R&ISnaroyveien 30N-1331 Fornebu (Norway)name.surname@telenor.comSummary. In this chapter we introduce a model for analyzing the spread of epidemics in adisconnected mobile network. The work is based on an extension, to a dynamic setting, ofthe eigenvector centrality principle introduced by two of the authors for the case of static net-works. The extension builds on a new definition of

Scalability of Peer Configuration Management in Logically Ad Hoc Networks

Current interest in ad hoc and peer-to-peer networking technologies prompts a re-examination of models for configuration management within these frameworks. In the future, network management methods may have to scale to millions of nodes within a single organization, with complex social constraints. In this paper, we discuss whether it is possible to manage the configuration of large numbers of network devices using well known and not so well known configuration models, and we discuss how the special characteristics of ad hoc and peer-to-peer networks are reflected in this problem.

A graph-theoretical model of computer security: From file sharing to social engineering

We describe a model of computer security that applies results from the statistical properties of graphs to human-computer systems. The model attempts to determine a safe threshold of interconnectivity in a human-computer system by ad hoc network analyses. The results can be applied to physical networks, social networks and networks of clues in a forensic analysis. Access control, intrusions and social engineering can also be discussed as graph- and information-theoretical relationships. Groups of users and shared objects, such as files or conversations, provide communication channels for the spread of both authorized and unauthorized information. We present numerical criteria for measuring the security of such systems and algorithms for finding the vulnerable points.

Scalability of peer configuration management in partially reliable and ad hoc networks

Current interest in ad hoc and peer-to-peer networking technologies prompts a re-examination of models for configuration management, within these frameworks. In the future, network management methods may have to scale to millions of nodes within a single organization, with complex social constraints. In this paper, we discuss whether it is possible to manage the configuration of large numbers of network devices using well-known and not-so-well-known configuration models, and we discuss how the special characteristics of ad hoc and peer-to-peer networks are reflected in this problem.

Product Adoption Networks and Their Growth in a Large Mobile Phone Network

To understand the diffusive spreading of a product in a telecom network, whether the product is a service, handset, or subscription, it can be very useful to study the structure of the underlying social network. By combining mobile traffic data and product adoption history from one of Telenor’s markets, we can define and measure an adoption network—roughly, the social network of adopters. By studying the time evolution of adoption networks, we can observe how different products diffuses through the network, and measure potential social influence. This paper presents an empirical and comparative study of three adoption networks evolving over time in a large telecom network. We believe that the strongest spreading of adoption takes place in the dense core of the underlying network, and gives rise to a dominant largest connected component (LCC) in the adoption network, which we call “the social network monster”. We believe that the size of the monster is a good indicator for whether or not a product is taking off. We show that the evolution of the LCC, and the size distribution of the other components, vary strongly with different products. The products studied in this article illustrate three distinct cases: that the social network monsters can grow or break down over time, or fail to occur at all. Some of the reasons a product takes off are intrinsic to the product; there are also aspects of the broader social context that can play in. Tentative explanations are offered for these phenomena. Also, we present two statistical tests which give an indication of the strength of the spreading over the social network. We find evidence that the spreading is dependent on the underlying social network, in particular for the early adopters.