Computer Applications and Software, Volume. 42, Issue 4, 303(2025)
DYNAMIC LOAD BALANCING ALGORITHM OF MICROSERVICE CHAIN BASED ON DEEP REINFORCEMENT LEARNING
[1] [1] Dragoni N, Giallorenzo S, Lafuente A, et al. Microservices: Yesterday, today, and tomorrow[M]//Present and Ulterior Software Engineering. Springer, 2017: 195-216.
[2] [2] Ding Z, Wang S, Pan M. QoS-constrained service selection for networked microservices[J]. IEEE Access, 2020, 8: 39285-39299.
[4] [4] Arapakis I, Bai X, Cambazoglu B. Impact of response latency on user behavior in web search[C]//37th International ACM SIGIR Conference on Research & Development in Information Retrieval, 2014: 103-112.
[5] [5] Niu Y, Liu F, Li Z. Load balancing across microservices[C]//IEEE Conference on Computer Communications, 2018: 198-206.
[6] [6] Mukherjee J, Wang M, Krishnamurthy D. Performance testing web applications on the cloud[C]//2014 IEEE 7th International Conference on Software Testing, Verification and Validation Workshops, 2014: 363-369.
[7] [7] Zhang Y, Xu K, Wang H, et al. Going fast and fair: Latency optimization for cloud-based service chains[J]. IEEE Network, 2018, 32(2): 138-143.
[8] [8] Wan F, Wu X, Zhang Q. Chain-oriented load balancing in microservice system[C]//2020 World Conference on Computing and Communication Technologies, 2020: 10-14.
[9] [9] Yu Y, Yang J, Guo C, et al. Joint optimization of service request routing and instance placement in the microservice system[J]. Journal of Network and Computer Applications, 2019, 147: 102441.
[10] [10] Wu Z, Wei J, Zhang F, et al. MDLB: A metadata dynamic load balancing mechanism based on reinforcement learning[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21: 1034-1046.
[11] [11] Yang Z, Nguyen P, Jin H, et al. MIRAS: Model-based re-inforcement learning for microservice resource allocation over scientific workflows[C]//2019 IEEE 39th International Conference on Distributed Computing Systems, 2019: 122-132.
[12] [12] Bhargavi K, Babu B S. Load balancing scheme for the public cloud using reinforcement learning with Raven Roosting Optimization Policy (RROP)[C]//2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution, 2019: 1-6.
[13] [13] Krzemien W, Stagni F, Haen C, et al. Addressing scalability with message queues: architecture and use cases for DIRAC interware[EB]. arXiv: 1902.09645, 2019.
[14] [14] Li W, Lemieux Y, Gao J, et al. Service mesh: Challenges, state of the art, and future research opportunities[C]//2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), 2019: 122-127.
[15] [15] Larsson L, Trneberg W, Klein C, et al. Impact of etcd deployment on Kubernetes, Istio, and application performance[J]. Software: Practice and Experience, 2020, 50(10): 1986-2007.
[16] [16] Grondman I, Busoniu L, Lopes G, et al. A survey of actorcritic reinforcement learning: Standard and natural policy gradients[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(6): 1291-1307.
[17] [17] Arapakis I, Park S, Pielot M. Deep reinforcement learning with double Q-Learning[EB]. arXiv: 2101.09086, 2021.
[18] [18] Fujimoto S, Hoof H, Meger D. Addressing function approximation error in actor-criticmethods[EB]. arXiv: 1802.09477.
[19] [19] Kistowski J, Eismann S, Schmitt N, et al. TeaStore: A micro-service reference application for benchmarking, modeling and resource management research[C]//2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 2018: 223-236.
[20] [20] Locustio. Locust[EB/OL]. (2015-02-03) [2021-11-28]. https://github.com/locustio/locust.
[21] [21] Boutaba R, Salahuddin M, Limam N, et al. A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities[J]. Journal of Internet Services and Applications, 2018, 9: 16.
[22] [22] Sambangi S, Gondi L. Multi linear regression model to detect distributed denial of service attacks in cloud environments[C]//Innovations in Cyber Physical Systems, 2021: 535-545.
[23] [23] Moore D, Notz W, Flinger M. The basic practice of statistics[M]. Palgrave Macmillan, 2010.
[24] [24] Akamai. Akamai's 2014 online holiday shopping trends and trafficreport[EB/OL]. (2015-03-06) [2021-11-28]. http://content.akamai.com/PG2112-Holiday-Recap-Report.html.
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Zhang Suyao. DYNAMIC LOAD BALANCING ALGORITHM OF MICROSERVICE CHAIN BASED ON DEEP REINFORCEMENT LEARNING[J]. Computer Applications and Software, 2025, 42(4): 303
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Received: Nov. 28, 2021
Accepted: Aug. 25, 2025
Published Online: Aug. 25, 2025
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