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.File | Dimensione | Formato | |
---|---|---|---|
ifacfinal.pdf
Accesso aperto
Tipo di file:
POSTPRINT (VERSIONE FINALE DELL’AUTORE)
Dimensione
419.48 kB
Formato
Adobe PDF
|
419.48 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.