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Gossip-Based Aggregation in Large
Dynamic Networks
´
MARK JELASITY, ALBERTO MONTRESOR, and OZALP BABAOGLU
Universit`a di Bologna
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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