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Title: Heterogeneous Computation Assignments in Coded Elastic Computing
We study the optimal design of a heterogeneous coded elastic computing (CEC) network where machines have varying relative computation speeds. CEC introduced by Yang et al. is a framework which mitigates the impact of elastic events, where machines join and leave the network. A set of data is distributed among storage constrained machines using a Maximum Distance Separable (MDS) code such that any subset of machines of a specific size can perform the desired computations. This design eliminates the need to re-distribute the data after each elastic event. In this work, we develop a process for an arbitrary heterogeneous computing network to minimize the overall computation time by defining an optimal computation load, or number of computations assigned to each machine. We then present an algorithm to define a specific computation assignment among the machines that makes use of the MDS code and meets the optimal computation load.  more » « less
Award ID(s):
1824558 1817154
NSF-PAR ID:
10188068
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2020 IEEE International Symposium on Information Theory (ISIT)
Page Range / eLocation ID:
168 to 173
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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