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Serverless computing, in particular the Function-as-a-Service (FaaS)execution model, has recently shown to be effective for running large-scalecomputations. However, little attention has been paid to highly-parallelapplications with unbalanced and irregular workloads. Typically, theseworkloads have been kept out of the cloud due to the impossibility ofanticipating their computing resources ahead of time, frequently leading tosevere resource over- and underprovisioning situations. Our main insight inthis article is, however, that the elasticity and ease of management ofserverless computing technology can be a key enabler for effectively runningthese problematic workloads for the first time in the cloud. More concretely,we demonstrate that with a simple serverless executor pool abstraction one canachieve a better cost-performance trade-off than a Spark cluster of static sizebuilt upon large EC2 virtual machines. To support this conclusion, we evaluatethree irregular algorithms: Unbalanced Tree Search (UTS), Mandelbrot Set usingthe Mariani-Silver algorithm and Betweenness Centrality (BC) on a random graph.For instance, our serverless implementation of UTS is able to outperform Sparkby up to 55% with the same cost. We also show that a serverless environment canoutperform a large EC2 in the BC algorithm by a 10% using the same amount ofvirtual CPUs. This provides the first concrete evidence that highly-parallel,irregular workloads can be efficiently executed using purely statelessfunctions with almost zero burden on users i.e., no need for users tounderstand non-obvious system-level parameters and optimizations. Furthermore,we show that UTS can benefit from the FaaS pay-as-you-go billing model, whichmakes it worth for the first time to enable certain application-leveloptimizations that can lead to significant improvements (e.g. of 41%) withnegligible increase in cost.

Exploiting Inherent Elasticity of Serverless in Irregular Algorithms