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Coded Elastic Computing on Machines With Heterogeneous Storage and Computation Speed.

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Title: Coded Elastic Computing on Machines With Heterogeneous Storage and Computation Speed.
Authors: Woolsey, Nicholas1 nicholas.woolsey@utah.edu, Chen, Rong-Rong1 rchen@ece.utah.edu, Ji, Mingyue1 mingyuej@usc.edu
Source: IEEE Transactions on Communications. May2021, Vol. 69 Issue 5, p2894-2908. 15p.
Subjects: Heterogeneous computing, Combinatorial optimization, Speed, Storage
Abstract: We study the optimal design of heterogeneous Coded Elastic Computing (CEC) where machines have varying computation speeds and storage. CEC introduced by Yang et al. in 2018 is a framework that mitigates the impact of elastic events, where machines can join and leave at arbitrary times. In CEC, data is distributed among machines using a Maximum Distance Separable (MDS) code such that subsets of machines can perform the desired computations. However, state-of-the-art CEC designs only operate on homogeneous networks where machines have the same speeds and storage. This may not be practical. In this work, based on an MDS storage assignment, we develop a novel computation assignment approach for heterogeneous CEC networks to minimize the overall computation time. We first consider the scenario where machines have heterogeneous computing speeds but same storage and then the scenario where both heterogeneities are present. We propose a novel combinatorial optimization formulation and solve it exactly by decomposing it into a convex optimization problem to find the optimal computation load and a filling problem to find the exact computation assignment. A low-complexity filling algorithm is adapted and can be completed within a number of iterations equal to at most the number of available machines. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Communications is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Communications%22">IEEE Transactions on Communications</searchLink>. May2021, Vol. 69 Issue 5, p2894-2908. 15p.
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  Data: We study the optimal design of heterogeneous Coded Elastic Computing (CEC) where machines have varying computation speeds and storage. CEC introduced by Yang et al. in 2018 is a framework that mitigates the impact of elastic events, where machines can join and leave at arbitrary times. In CEC, data is distributed among machines using a Maximum Distance Separable (MDS) code such that subsets of machines can perform the desired computations. However, state-of-the-art CEC designs only operate on homogeneous networks where machines have the same speeds and storage. This may not be practical. In this work, based on an MDS storage assignment, we develop a novel computation assignment approach for heterogeneous CEC networks to minimize the overall computation time. We first consider the scenario where machines have heterogeneous computing speeds but same storage and then the scenario where both heterogeneities are present. We propose a novel combinatorial optimization formulation and solve it exactly by decomposing it into a convex optimization problem to find the optimal computation load and a filling problem to find the exact computation assignment. A low-complexity filling algorithm is adapted and can be completed within a number of iterations equal to at most the number of available machines. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Communications is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TCOMM.2021.3056089
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        Text: English
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        PageCount: 15
        StartPage: 2894
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      – SubjectFull: Heterogeneous computing
        Type: general
      – SubjectFull: Combinatorial optimization
        Type: general
      – SubjectFull: Speed
        Type: general
      – SubjectFull: Storage
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              Text: May2021
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