MicroBlend: An Automated Service-Blending Framework for Microservice-Based Cloud Applications

With the increased usage of public clouds for hosting applications, it becomes essential to choose the appropriate services from the public cloud offerings in order to achieve satisfactory performance while minimizing deployment expenses. Prior research has demonstrated that combining different services can be more cost-effective than solutions based on a single service type. However, automating the combination of resources for applications composed of large graphs of loosely-connected microservices has not yet been thoroughly explored, especially in the context of microservice-based cloud applications. Motivated by this, targeting microservice-based applications, we propose MicroBlend, an automated framework that mixes Infrastructure-as-a-Service (IaaS) and Function-as-a-Service (FaaS) cloud services in a way that is both cost-effective and performance-efficient. MicroBlend focuses on: (i) providing an automated approach for blending resources that takes microservice dependencies into account, (ii) generating FaaS-ready code using a compiler-based approach, and (iii) suggesting an optimization plan for combining microservices with user annotation. We implement MicroBlend on Amazon Web Services (AWS) and evaluate its performance using real-world traces from three different applications. Our findings demonstrate that by employing automated microservice-to-cloud service assignment, MicroBlend can significantly reduce Service Level Objective (SLO) violations by 9%, compared to traditional VM-based resource procurement schemes. Additionally, MicroBlend can decrease costs by 11%.