Title
Resilient gossip algorithms for collecting online management information in exascale clusters
Abstract
Management of forthcoming exascale clusters requires frequent collection of run-time information about the nodes and the running applications. This paper presents a new paradigm for providing online information to the management system of scalable clusters, consisting of a large number of nodes and one or more masters that manage these nodes. We describe the details of resilient gossip algorithms for sharing local information within subsets of nodes and for sending global information to a master, which holds information on all the nodes. The presented algorithms are decentralized, scalable and resilient, working well even when some nodes fail, without needing any recovery protocol. The paper gives formal expressions for approximating the average ages of the local information at each node and the information collected by the master. It then shows that these results closely match the results of simulations and measurements on a real cluster. The paper also investigates the resilience of the algorithms and the impact on the average age when nodes or masters fail. The main outcome of this paper is that partitioning of large clusters can improve the quality of information available to the management system without increasing the number of messages per node. Copyright © 2015 John Wiley & Sons, Ltd.
Year
DOI
Venue
2015
10.1002/cpe.3465
Concurrency and Computation: Practice and Experience
Keywords
Field
DocType
exascale clusters,gossip algorithms,resource management
Psychological resilience,Resource management,Cluster (physics),Management information systems,Expression (mathematics),Computer science,Parallel computing,Management system,Distributed computing,Information quality,Scalability
Journal
Volume
Issue
ISSN
27
17
1532-0626
Citations 
PageRank 
References 
4
0.41
15
Authors
5
Name
Order
Citations
PageRank
Amnon Barak1590119.00
Zvi Drezner21195140.69
E. Levy3222.24
Matthias Lieber423715.12
Amnon Shiloh510524.32