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ABSTRACT
As computer networks increase in size, become more heterogeneous and span greater geographic distances, applications must be designed to cope with the very large scale, poor reliability, and often, with the extreme dynamism of the underlying network. Aggregation is a key functional building block for such applications: it refers to a set of functions that provide components of a distributed system access to global information including network size, average load, average uptime, location and description of hotspots, and so on. Local access to global information is often very useful, if not indispensable for building applications that are robust and adaptive. For example, in an industrial control application, some aggregate value reaching a threshold may trigger the execution of certain actions; a distributed storage system will want to know the total available free space; load-balancing protocols may benefit from knowing the target average load so as to minimize the load they transfer. We propose a gossip-based protocol for computing aggregate values over network components in a fully decentralized fashion. The class of aggregate functions we can compute is very broad and includes many useful special cases such as counting, averages, sums, products, and extremal values. The protocol is suitable for extremely large and highly dynamic systems due to its proactive structure---all nodes receive the aggregate value continuously, thus being able to track any changes in the system. The protocol is also extremely lightweight, making it suitable for many distributed applications including peer-to-peer and grid computing systems. We demonstrate the efficiency and robustness of our gossip-based protocol both theoretically and experimentally under a variety of scenarios including node and communication failures.
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CITED BY 37
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Ozalp Babaoglu , Geoffrey Canright , Andreas Deutsch , Gianni A. Di Caro , Frederick Ducatelle , Luca M. Gambardella , Niloy Ganguly , Márk Jelasity , Roberto Montemanni , Alberto Montresor , Tore Urnes, Design patterns from biology for distributed computing, ACM Transactions on Autonomous and Adaptive Systems (TAAS), v.1 n.1, p.26-66, September 2006
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E. A. Eiben , M. Schoenauer , J. L. J. Laredo , P. A. Castillo , A. M. Mora , J. J. Merelo, Exploring selection mechanisms for an agent-based distributed evolutionary algorithm, Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, July 07-11, 2007, London, United Kingdom
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Paolo Costa , Vincent Gramoli , Márk Jelasity , Gian Paolo Jesi , Erwan Le Merrer , Alberto Montresor , Leonardo Querzoni, Exploring the interdisciplinary connections of gossip-based systems, ACM SIGOPS Operating Systems Review, v.41 n.5, October 2007
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Ping Luo , Hui Xiong , Kevin Lü , Zhongzhi Shi, Distributed classification in peer-to-peer networks, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA
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Marco Biazzini , Balazs Banhelyi , Alberto Montresor , Mark Jelasity, Distributed hyper-heuristics for real parameter optimization, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, July 08-12, 2009, Montreal, Québec, Canada
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REVIEW
"Petcu Dana : Reviewer"
A new robust and adaptive protocol for computing aggregate values over network components is presented and studied. It is suitable for large and dynamic systems, including peer-to-peer or grid computing systems. The core of the protocol is a decen
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