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Distributed Offline Load Balancing in MapReduce Networks
KTH, School of Electrical Engineering (EES), Automatic Control.
City University London.
Electrical and Computer Engineering Department, University of Cyprus.
KTH, School of Electrical Engineering (EES), Automatic Control.
2013 (English)In: 2013 IEEE 52ND ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), IEEE conference proceedings, 2013, 835-840 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we address the problem of balancing the processing load of MapReduce tasks running on heterogeneous clusters, i.e., clusters with different capacities and update cycles. We present a fully decentralized algorithm, based on ratio consensus, where each mapper decides the amount of workload data to handle for a single user job using only job specific local information, i.e., information that can be collected from directly connected neighboring mappers, regarding their current workload and capacity. In contrast to other algorithms in the literature, the proposed algorithm can be deployed in heterogeneous networks and can operate asynchronously in both directed and undirected communication topologies. The performance of the proposed algorithm is demonstrated via simulation experiments on large-scale strongly connected topologies. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 835-840 p.
Keyword [en]
Emerging control applications, Agents and autonomous systems, Linear systems
National Category
Control Engineering Computer Systems Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-136169ISI: 000352223501010OAI: oai:DiVA.org:kth-136169DiVA: diva2:675501
Conference
52nd IEEE Conference on Decision and Control; Florence, Italy, 10-13 December, 2013
Note

QC 20151208

Available from: 2013-12-04 Created: 2013-12-04 Last updated: 2015-12-08Bibliographically approved

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