A Transportation Problem–Based Heuristic for Energy-Efficient Scheduling of Serverless Workflow Applications

Serverless computing has emerged as a cloud computing paradigm that mitigates the complexities of resource provisioning and management while charging users only for the resources they consume. However, some inherent characteristics of serverless environments, such as stateless functions and scaling to zero, make it challenging to execute workflow applications that require data transmission between functions. As a result, serverless service providers need to offer efficient workflow execution services to encourage the adoption of these platforms. At the same time, the high energy consumption of serverless cloud data centers has not received sufficient attention from the research community. Accordingly, we introduce Transportation-based Energy-efficient Serverless Scheduling (TESS), an approach designed to address deadline-constrained workflow scheduling in serverless environments while reducing energy consumption. To achieve this, we first formulate the problem as a variation of the well-known Transportation Problem and then propose a scheduling algorithm based on solutions to this problem. Evaluation results demonstrate that the proposed algorithm reduces energy consumption by 10%–31% compared with state-of-the-art algorithms.