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QoS aware FaaS for Heterogeneous Edge-Cloud continuum

Sheshadri, KR and Lakshmi, J (2022) QoS aware FaaS for Heterogeneous Edge-Cloud continuum. In: 15th IEEE International Conference on Cloud Computing, CLOUD 2022, 10-16 July 2021, Barcelona, pp. 70-80.

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Official URL: https://doi.org/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. © 2022 IEEE.

Item Type: Conference Paper
Publication: IEEE International Conference on Cloud Computing, CLOUD
Publisher: IEEE Computer Society
Additional Information: The copyright for this article belongs to IEEE Computer Society.
Keywords: Edge computing; Security systems; Specifications, Cloud platforms; Edge cloud continuum; Edge cloud function as a service; Edge clouds; Function as a service; Heterogeneous edge cloud platform; Heterogeneous function as a service platform; Quality of service aware function as a service for heterogeneous edge cloud continuum; Quality-of-service; Serverless computing; Service platforms; Service-aware, Quality of service
Department/Centre: Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems
Date Deposited: 06 Oct 2022 08:43
Last Modified: 06 Oct 2022 08:43
URI: https://eprints.iisc.ac.in/id/eprint/77147

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