FaaSFlow: Enable Efficient Workflow Execution for Function-as-a-Service

Serverless computing (Function-as-a-Service) provides fine-grain resource sharing by running functions (or Lambdas) in containers. Data-dependent functions are required to be invoked following a pre-defined logic, which is known as serverless workflows. However, our investigation shows that the traditional master-worker based workflow execution architecture performs poorly in serverless context. One significant overhead results from the master-side workflow schedule pattern, with which the functions are triggered in the master node and assigned to worker nodes for execution. Besides, the data movement between workers also reduces the throughput. To this end, we present a worker-side workflow schedule pattern for serverless workflow execution. Following the design, we implement FaaSFlow to enable efficient workflow execution in the serverless context. Besides, we propose an adaptive storage library FaaStore that enables fast data transfer between functions on the same node without through the database. Experiment results show that FaaSFlow effectively mitigates the workflow scheduling overhead by 74.6% on average and data transmission overhead by 95% at most. When the network bandwidth fluctuates, FaaSFlow-FaaStore reduces the throughput degradation by 23.0%, and is able tomultiply the utilization of network bandwidth by 1.5x-4x.

Automated summary

Serverless computing provides fine-grain resource sharing, but the traditional workflow execution architecture performs poorly in this context. We propose a worker-side schedule pattern for serverless workflows to enable efficient execution. Experimental results show significant reductions in workflow scheduling and data transmission overheads.