Abstract
In this paper we address the problem of balancing the processing load of MapReduce tasks running on heterogeneous clusters, i. e., clusters composed of nodes 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 usage and capacity. In contrast to other algorithms in the literature, the proposed algorithm can be deployed in heterogeneous clusters 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.
Original language | English |
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Title of host publication | 2013 IEEE 52ND ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC) |
Publisher | IEEE |
Pages | 835-840 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-4673-5717-3 |
ISBN (Print) | 978-1-4673-5714-2 |
DOIs | |
Publication status | Published - 2013 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE Conference on Decision and Control - Florence, Italy Duration: 10 Dec 2013 → 13 Dec 2013 Conference number: 52 |
Publication series
Name | IEEE Conference on Decision and Control |
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Publisher | IEEE |
ISSN (Print) | 0743-1546 |
Conference
Conference | IEEE Conference on Decision and Control |
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Abbreviated title | CDC |
Country | Italy |
City | Florence |
Period | 10/12/2013 → 13/12/2013 |