QoS aware FaaS for Heterogeneous Edge-Cloud continuum

• Published in: 2022 IEEE 15th International Conference on Cloud Computing (CLOUD) pp. 70 - 80
• Main Authors: Sheshadri, K R, Lakshmi, J
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2022
• Subjects: Cloud computing; Computational modeling; Costs; Edge Cloud; Edge Cloud continuum; Edge Cloud FaaS; FAA; FaaS; Function as a Service; Heterogeneous Edge Cloud platforms; Heterogeneous FaaS platforms; Microservice architectures; QoS; QoS aware FaaS for Heterogeneous Edge Cloud continuum; Quality of service; Serverless computing
• ISSN: 2159-6190
• DOI: 10.1109/CLOUD55607.2022.00023

Function as a Service (FaaS) is one of the widely used serverless computing service offerings to build and deploy applications on the Cloud. The platform is popular for its "pay-as-you-go" billing model, microservice-based design, event-driven executions, and autonomous scaling. Although it has its firm roots in Cloud computing service offerings, it is considerably explored in the Edge computing layer. The efficient resource management of FaaS is attractive to Edge computing because of the limited nature of resources. Existing literature on Edge-Cloud FaaS platforms orchestrates compute workloads based on factors such as data locality, resource availability, network costs, and bandwidth. However, the state-of-the-art platforms lack a comprehensive way to address the challenges of managing heterogeneous resources in the FaaS platform. The resource specification in a heterogeneous setting, lack of Quality of Service (QoS) driven resource provisioning, and function deployment exacerbate the problem of resource selection, and function deployment in FaaS platforms with a heterogeneous resource pool. To address these gaps, the current work presents a novel heterogeneous FaaS platform that deduces function resource specification using Machine Learning (ML) methods, performs smart function placement on Edge/Cloud based on a user-specified QoS requirement, and exploit data locality by caching appropriate data for function executions. Experimental results based on real-world workloads on a video surveillance application show that the proposed platform brings efficient resource utilization and cost savings at the Cloud by reducing the resource usage by up to 30%, while improving the performance of function executions by up to 25% at Edge and Cloud.