Campus Units
Electrical and Computer Engineering, Mathematics
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
2019
Journal or Book Title
arXiv
Abstract
Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on different servers. The servers also need to exchange intermediate values to complete the computation. Experimental evidence suggests that this so-called Shuffle phase can be a significant part of the overall execution time for several classes of jobs. Prior work has demonstrated a natural tradeoff between computation and communication whereby running redundant copies of jobs can reduce the Shuffle traffic load, thereby leading to reduced overall execution times. For a single job, the main drawback of this approach is that it requires the original job to be split into a number of files that grows exponentially in the system parameters. When extended to multiple jobs (with specific function types), these techniques suffer from a limitation of a similar flavor, i.e., they require an exponentially large number of jobs to be executed. In practical scenarios, these requirements can significantly reduce the promised gains of the method. In this work, we show that a class of combinatorial structures called resolvable designs can be used to develop efficient coded distributed computing schemes for both the single and multiple job scenarios considered in prior work. We present both theoretical analysis and exhaustive experimental results (on Amazon EC2 clusters) that demonstrate the performance advantages of our method. For the single and multiple job cases, we obtain speed-ups of 4.69x (and 2.6x over prior work) and 4.31x over the baseline approach, respectively.
Copyright Owner
The Authors
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Konstantinidis, Konstantinos and Ramamoorthy, Aditya, "Resolvable Designs for Speeding up Distributed Computing" (2019). Electrical and Computer Engineering Publications. 226.
https://lib.dr.iastate.edu/ece_pubs/226
Included in
Computer Sciences Commons, Electrical and Computer Engineering Commons, Mathematics Commons
Comments
This is a pre-print of the article Konstantinidis, Konstantinos, and Aditya Ramamoorthy. "Resolvable Designs for Speeding up Distributed Computing." arXiv preprint arXiv:1908.05666 (2019). Posted with permission.