Leader election, a fundamental coordination problem in distributed systems, has been addressed in many different ways. Among these works, resilient leader election algorithms are of particular interest due to the ongoing emergence of open, complex distributed systems such as smart cities and the Internet of Things. However, previous algorithms with O(diameter) stabilization time complexity either assume some prior knowledge of the network or that very large messages can be sent. In this paper, we present a resilient leader election algorithm with O(diameter) stabilization time, small messages, and no prior knowledge of the network. This algorithm is based on aggregate computing, which provides a layered approach to algorithm development based on composition of resilient algorithmic “building blocks.” With our algorithm, a key design parameter K defines important performance attributes: a larger K will delay the recovery from loss of current leader, while a small K may lead to multiple leaders, and the algorithm will stabilize with O(diameter) time complexity when K ≥ 2.

A resilient leader election algorithm using aggregate computing blocks

Audrito G.;
2020-01-01

Abstract

Leader election, a fundamental coordination problem in distributed systems, has been addressed in many different ways. Among these works, resilient leader election algorithms are of particular interest due to the ongoing emergence of open, complex distributed systems such as smart cities and the Internet of Things. However, previous algorithms with O(diameter) stabilization time complexity either assume some prior knowledge of the network or that very large messages can be sent. In this paper, we present a resilient leader election algorithm with O(diameter) stabilization time, small messages, and no prior knowledge of the network. This algorithm is based on aggregate computing, which provides a layered approach to algorithm development based on composition of resilient algorithmic “building blocks.” With our algorithm, a key design parameter K defines important performance attributes: a larger K will delay the recovery from loss of current leader, while a small K may lead to multiple leaders, and the algorithm will stabilize with O(diameter) time complexity when K ≥ 2.
2020
21st IFAC World Congress 2020
deu
2020
IFAC-PapersOnLine
Elsevier B.V.
53
2
3336
3341
Aggregate computing; Leader election; Multiagent system; Resilience
Mo Y.; Audrito G.; Dasgupta S.; Beal J.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1807002
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