Details

Autor(en) / Beteiligte

• Yao, Xuyi
• Chen, Ningjiang
• Yuan, Xuemei
• Ou, Pingjie

Titel

Performance Optimization in Serverless Edge Computing Environment using DRL-Based Function Offloading

Ist Teil von

• 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2022, p.1390-1395

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Serverless computing/Function as a Service (FaaS) has emerged as a new paradigm for running short-lived applications in the cloud. Serverless edge computing is recently adopting serverless computing at edge to run event-driven tasks and supporting application running of resource-constrained Internet of Things (IoT) devices by offloading their tasks to the edge. However, traditional task offloading methods are mainly based on heuristic algorithms for one-shot optimization, which leads to performance degradation in long-term operation. Fortunately, deep reinforcement learning techniques combining reinforcement learning and deep neural networks provide a promising alternative. Therefore, a function offloading algorithm DRLFO is proposed with a deep reinforcement learning algorithm based on actor-critic framework in this paper. The function offloading process in serverless edge computing environment is modeled as a Markov Decision Process. Finally, the experimental results show that the proposed algorithm can successfully converge and outperform the compared baseline algorithm in terms of function success rate and reduce the average latency by 4.6%-22.6%.