ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Conference Paper Multi-Agent Learning-based Package Caching in Serverless Edge Computing
Cited 2 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Hongseok Jeon, Seungjae Shin, Chunglae Cho, Seunghyun Yoon
Issue Date
2022-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.400-402
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC55196.2022.9952429
Abstract
In this paper, we investigate a learning-based caching policy in serverless edge computing (SEC), especially where critical packages for container are cached and shared. To this end, we formulate a cooperative multi-agent decision problem and solve it using deep multi-agent reinforcement learning (DMARL) approach where homogenous agents share a common reward, cache hit rate and QoS violations. The simulation results indicate that the DMARL approach performs better than baselines in the QoS violation rate.
KSP Keywords
Cache hit rate, Caching Policy, Cooperative multi-agent, Decision problem, Learning-based, Multi-agent Learning, Reinforcement Learning(RL), Violation rate, edge computing, multi-agent reinforcement learning, simulation results