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Function-as-a-Service is a novel type of cloud service used for creatingdistributed applications and utilizing computing resources. Applicationdeveloper supplies source code of cloud functions, which are small applicationsor application components, while the service provider is responsible forprovisioning the infrastructure, scaling and exposing a REST style API. Thisenvironment seems to be adequate for running scientific workflows, which inrecent years, have become an established paradigm for implementing andpreserving complex scientific processes. In this paper, we present work done onevaluating three major FaaS providers (Amazon, Google, IBM) as a platform forrunning scientific workflows. The experiments were performed with a dedicatedbenchmarking framework, which consisted of instrumented workflow executionengine. The testing load was implemented as a large scale bag-of-tasks styleworkflow, where task count reached 5120 running in parallel. The studiedparameters include raw performance, efficiency of infrastructure provisioning,overhead introduced by the API and network layers, as well as aspects relatedto run time accounting. Conclusions include insights into availableperformance, expressed as raw GFlops values and charts depicting relation ofperformance to function size. The infrastructure provisioning proved to begoverned by parallelism and rate limits, which can be deducted from includedcharts. The overhead imposed by using a REST API proved to be a significantcontribution to overall run time of individual tasks, and possibly the wholeworkflow. The paper ends with pointing out possible future work, which includesbuilding performance models and designing a dedicated scheduling algorithms forrunning scientific workflows on FaaS.

Performance considerations on execution of large scale workflow applications on cloud functions