Hybrid Serverless Platform for Service Function Chains

• Published in: 2023 IEEE 16th International Conference on Cloud Computing (CLOUD) pp. 493 - 504
• Main Authors: R, Sheshadri K, Lakshmi, J
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2023
• Subjects: Cloud computing; Data centers; Dynamic scheduling; FaaS; Hybrid Serverless Platform; IaaS; Quality of service; SDN; Serverless; Serverless computing; Service function chaining; SFC; Software
• ISSN: 2159-6190
• DOI: 10.1109/CLOUD60044.2023.00066

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) have realized the aspect of programmability in networks, bringing in the necessary fidelity to managing network resources. NFVs allows 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 different services such as Infrastructure as a Service (IaaS) and serverless computing. These approaches suffer from deployment difficulties (configuration and sizing) or adaptability to performance requirements or changing dynamics. 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's controller takes provisioning decisions on Quality of Service (QoS) attributes derived from flow statistics, thereby alleviating sizing decisions for deployment. A proof-of-concept realization of HSP is presented in the paper and is evaluated for an example Service Function Chain (SFC) scenario 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 SFC execution times as compared to a baseline FaaS implementation.