Hybrid Serverless Platform for Smart Deployment of Service Function Chains

Cloud Data Centres deal with dynamic changes all the time. Networks in particular, need to adapt their configurations to changing workloads. Given these expectations, Network Function Virtualization (NFV) using Software Defined Networks (SDNs) has realized the aspect of programmability in networks. NFVs allow network services to be programmed as software entities that can be deployed on commodity clusters in the Cloud. Being software, they inherently carry the ability to be customized to specific tenants' requirements and thus support multi-tenant variations with ease. However, the ability to exploit scaling in alignment with changing demands with minimal loss of service, and improving resource usage efficiency still remains a challenge. Several recent works in literature have proposed platforms to realize Virtual Network functions (VNFs) on the Cloud using service offerings such as Infrastructure as a Service (IaaS) and serverless computing. These approaches are limited by deployment difficulties (configuration and sizing), adaptability to performance requirements (elastic scaling), and changing workload dynamics (scaling and customization). In the current work, we propose a Hybrid Serverless Platform (HSP) to address these identified lacunae. The HSP is implemented using a combination of persistent IaaS, and FaaS components. The IaaS components handle the steady state load, whereas the FaaS components activate during the dynamic change associated with scaling to minimize service loss. The HSP controller takes provisioning decisions based on Quality of Service (QoS) rules and flow statistics using an auto recommender, alleviating users of sizing decisions for function deployment. HSP controller design exploits data locality in SFC realization, reducing data-transfer times between VNFs. It also enables the usage of application characteristics to offer higher control over SFC deployment. A proof-of-concept realization of HSP is presented in the paper and is evaluated for a representative Service Function Chain (SFC) for a dynamic workload, which shows minimal loss in flowlet service, up to 35% resource savings as compared to a pure IaaS deployment and up to 55% lower end-to-end times as compared to a baseline FaaS implementation.