MQFQ-Sticky applies multi-queue fair queuing and anticipatory scheduling to GPU serverless functions, reporting 2x-20x latency reductions and fairer GPU service than FCFS, batching, or SJF policies.
Serverless computing provides on-demand high performance computing for biomedical research
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abstract
Cloud computing offers on-demand, scalable computing and storage, and has become an essential resource for the analyses of big biomedical data. The usual approach to cloud computing requires users to reserve and provision virtual servers. An emerging alternative is to have the provider allocate machine resources dynamically. This type of serverless computing has tremendous potential for biomedical research in terms of ease-of-use, instantaneous scalability and cost effectiveness. In our proof of concept example, we demonstrate how serverless computing provides low cost access to hundreds of CPUs, on demand, with little or no setup. In particular, we illustrate that the all-against-all pairwise comparison among all unique human proteins can be accomplished in approximately 2 minutes, at a cost of less than $1, using Amazon Web Services Lambda. This is a 250x speedup compared to running the same task on a typical laptop computer.
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MQFQ-Sticky: Fair Queueing For Serverless GPU Functions
MQFQ-Sticky applies multi-queue fair queuing and anticipatory scheduling to GPU serverless functions, reporting 2x-20x latency reductions and fairer GPU service than FCFS, batching, or SJF policies.