首页|XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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Content delivery networks(CDNs)play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions.As an essential component of CDNs,the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers.With the emergence of 5G technology,traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments.Consequently,deep reinforcement learning(DRL)offers a promising solution for intelligent zero-touch network governance.However,the black-box nature of DRL models poses challenges in understanding and making trusting decisions.In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache,which combines DRL with an explainable artificial intelligence(XAI)technique for cache management in CDNs.Instead of focusing solely on achieving performance gains,this study introduces a novel paradigm for providing interpretable caching strategies,thereby establishing a foundation for future transparent and trustworthy edge caching solutions.Specifically,a multi-level cache scheduling framework for CDNs was formulated theoretically,with the D3QN-based caching scheme serving as the targeted interpretable model.Subsequently,by integrating Deep-SHAP into our framework,the contribution of each state input feature to the agent's Q-value output was calculated,thereby providing valuable insights into the decision-making process.The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental inputs.The results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio,quality of service(QoS),and space utilization.

deep reinforcement learning(DRL)explainable artificial intelligence(XAI)multi-level cachecontent delivery network(CDN)D3QN algorithmDeep-SHAP

Xiaolong XU、Fan WU、Muhammad BILAL、Xiaoyu XIA、Wanchun DOU、Lina YAO、Weiyi ZHONG

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School of Software,Nanjing University of Information Science and Technology,Nanjing 210044,China

School of Computing and Communications,Lancaster University,Lancaster LA1 4WA,UK

School of Computing Technologies,Royal Melbourne Institute of Technology,Melbourne VIC 3001,Australia

State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China

School of Computer Science and Engineering,University of New South Wales,Sydney NSW 2052,Australia

Data 61,Commonwealth Scientific and Industrial Research Organization,Sydney VIC 3169,Australia

School of Computer Science,Qufu Normal University,Qufu 273165,China

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National Natural Science Foundation of ChinaNatural Science Foundation of Jiangsu Province of China

92267104BK20211284

2024

10.1007/s11432-023-3987-y

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(7)