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ETAS: predictive scheduling of functions on worker nodes of Apache OpenWhisk platform

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Abstract

Fast execution of functions is an inevitable challenge in the serverless computing landscape. Inefficient dispatching, fluctuations in invocation rates, burstiness of workloads, and wide range of execution times of serverless functions result in load imbalances and overloading of worker nodes of serverless platforms that impose high latency on invocations. This paper is concentrated on function scheduling within worker nodes of Apache OpenWhisk serverless platform and presents ETAS, a predictive scheduling scheme to reduce response times of invocations besides increasing workers throughputs and resource utilizations. ETAS schedules functions using their execution times, estimated by their previous execution’s history, arrival times, and containers’ status. We have implemented ETAS in Apache OpenWhisk and show that, compared to the OpenWhisk worker scheduler and several queue scheduling schemes, ETAS outperforms others by reducing the average waiting time by 30% and increasing the throughput by 40%.

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Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Notes

  1. https://openwhisk.apache.org/.

  2. https://openwhisk.apache.org/documentation.html

  3. https://www.nginx.com/.

  4. https://kafka.apache.org/.

  5. https://www.docker.com/.

  6. https://couchdb.apache.org/.

  7. https://aws.amazon.com/lambda/

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  1. School of Computer engineering, Iran University of Science and Technology, Tehran, Iran

    Ali Banaei & Mohsen Sharifi

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  1. Ali Banaei
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Banaei, A., Sharifi, M. ETAS: predictive scheduling of functions on worker nodes of Apache OpenWhisk platform. J Supercomput 78, 5358–5393 (2022). https://doi.org/10.1007/s11227-021-04057-z

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