Improving Existing WMS for Reduced Makespan of Workflows with Lambda

Abstract

AbstractScientific workflows are increasingly important for complex scientific applications. Recently, Function as a Service (FaaS) has emerged as a platform for processing non-interactive tasks. FaaS (such as AWS Lambda and Google Cloud Functions) can play an important role in processing scientific workflows. A number of works have demonstrated their ability to process these workflows. However, some issues were identified when workflows executed on cloud functions due to their limits (e.g., stateless behaviour). A major issue is the additional data transfer during the execution between object storage and the FaaS invocation environment. This leads to increased communication costs. DEWE v3 is one of the Workflow Management Systems (WMSs) that already had foundations for processing workflows with cloud functions. In this paper, we have modified the job dispatch algorithm of DEWE v3 on a function environment to reduce data dependency transfers. Our modified algorithm schedules jobs with precedence constraints to be executed in a single function invocation. Therefore, later jobs can utilise output files generated from their predecessor job in the same invocation. This reduces the makespan of workflow execution. We have evaluated the improved scheduling algorithm and the original with small- and large-scale Montage workflows. The experimental results show that our algorithm can reduce the overall makespan in contrast to the original DEWE v3 by about 10%.KeywordsScientific workflowsCloud functionsServerless architecturesMakespan