Microservices have gained popularity as an architectural approach for developing scalable and modular applications. Traditionally, microservice deployment relies on virtual machines (VMs) from Infrastructure-as-a-Service (IaaS) computing. However, the emerging serverless computing offers new possibilities for more scalable microservice deployment. In this paper, we provide insights into the optimal scenarios for IaaS VMs and serverless, and investigate the challenges in the programming model and invocation pattern. We propose Microless, a framework that achieves the hybrid deployment of microservices on serverless and IaaS VMs and overcomes the challenges. In Microless, the steady workload is processed on IaaS VMs, ensuring optimal resource utilization and run-time performance. For the fluctuating workload, serverless can rapidly scale out resources to handle burst requests, minimizing response latency and enhancing cost-effectiveness. Experimental results validate the effectiveness of Microless in runtime performance and deployment cost.

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.