Abstract
The emergence of the Internet of Things (IoT) ecosystem has exponentially increased the need for real-time and data-intensive applications. It has shifted the computing load from the centralized cloud to peripheral nodes, hence introducing the adoption of edge computing. In edge computing, services are deployed closer to the users and IoT devices. Edge computing provides computation and storage on geographically distributed nodes, some with limited resources. For this reason, resource efficiency and flexibility is fundamental in edge services: To tackle this challenge, serverless computing can be leveraged. It allows to efficiently deploy containerized applications on resource constrained nodes. It aims at providing the required Quality Of Service (QoS) while limiting resource consumption and allows scaling with traffic volume.
In this context, our work aims at analyzing and developing serverless-based
technologies for edge computing applications. It evaluates the use of Artificial Intelligence, namely Reinforcement Learning (RL) techniques, to optimize the scalability and resource efficiency of serverless frameworks on edge computing clusters.
The study will be divided into two main focus areas: Firstly, an experimental analysis of serverless computing for IoT and 5G services is done, considering infrastructures with various features and various open-source software. Secondly, the development and analysis of reinforcement-learning tools to enhance the performance of serverless computing on edge clusters is presented. These tools are evaluated on various IoT-based applications, from simple lightweight webservers to complex stream processing pipelines.
Given the growing traction of serverless computing in both academia and industry, the analysis and tools included in this study will provide important insights on its benefits and drawbacks, while enhancing serverless computing performance for edge services deployment and management.
Keywords: serverless computing, FaaS, reinforcement learning, Edge, IoT, open-source.
In this context, our work aims at analyzing and developing serverless-based
technologies for edge computing applications. It evaluates the use of Artificial Intelligence, namely Reinforcement Learning (RL) techniques, to optimize the scalability and resource efficiency of serverless frameworks on edge computing clusters.
The study will be divided into two main focus areas: Firstly, an experimental analysis of serverless computing for IoT and 5G services is done, considering infrastructures with various features and various open-source software. Secondly, the development and analysis of reinforcement-learning tools to enhance the performance of serverless computing on edge clusters is presented. These tools are evaluated on various IoT-based applications, from simple lightweight webservers to complex stream processing pipelines.
Given the growing traction of serverless computing in both academia and industry, the analysis and tools included in this study will provide important insights on its benefits and drawbacks, while enhancing serverless computing performance for edge services deployment and management.
Keywords: serverless computing, FaaS, reinforcement learning, Edge, IoT, open-source.
| Original language | English |
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| Award date | 14 Jun 2023 |
| Publication status | Published - 2023 |