{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:07:46Z","timestamp":1763644066133,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS2000681, CNS2245827"],"award-info":[{"award-number":["CNS2000681, CNS2245827"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,30]]},"DOI":"10.1145\/3620678.3624980","type":"proceedings-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T13:58:07Z","timestamp":1698760687000},"page":"427-442","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["\u03bcConAdapter"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7199-9224","authenticated-orcid":false,"given":"Jianshu","family":"Liu","sequence":"first","affiliation":[{"name":"Louisiana State University, Baton Rouge, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1219-2084","authenticated-orcid":false,"given":"Shungeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Augusta University, Augusta, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5729-2898","authenticated-orcid":false,"given":"Qingyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Louisiana State University, Baton Rouge, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Decomposing twitter: Adventures in service-oriented architecture. https:\/\/www.infoq.com\/presentations\/twitter-soa\/."},{"key":"e_1_3_2_1_2_1","unstructured":"Microservice architecture diagram examples. https:\/\/www.devteam.space\/blog\/microservice-architecture-examples-and-diagram\/."},{"key":"e_1_3_2_1_3_1","unstructured":"Tony mauro. adopting microservices at netflix: Lessons for architectural design. https:\/\/www.nginx.com\/blog\/microservices-at-netflix-architectural-best-practices\/."},{"key":"e_1_3_2_1_4_1","volume-title":"https:\/\/microservices-demo.github.io\/","author":"Sock","year":"2016","unstructured":"Sock shop microservice demo application. https:\/\/microservices-demo.github.io\/, 2016."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3445814.3446739"},{"key":"e_1_3_2_1_6_1","unstructured":"Consortium O. Rubbos: Bulletin board benchmark. http:\/\/jmob.ow2.org\/rubbos.html 2005."},{"key":"e_1_3_2_1_7_1","unstructured":"Einav Y. Amazon found every 100ms of latency cost them 1% in sales. https:\/\/www.gigaspaces.com\/blog\/amazon-found-every-100ms-of-latency-cost-them-1-in-sales\/."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304013"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2382553.2382556"},{"key":"e_1_3_2_1_10_1","volume-title":"Dqn with model-based exploration: efficient learning on environments with sparse rewards. arXiv preprint arXiv:1903.09295","author":"Gou S. Z.","year":"2019","unstructured":"Gou, S. Z., and Liu, Y. Dqn with model-based exploration: efficient learning on environments with sparse rewards. arXiv preprint arXiv:1903.09295 (2019)."},{"key":"e_1_3_2_1_11_1","first-page":"721","volume-title":"18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21)","author":"Hwang C.","year":"2021","unstructured":"Hwang, C., Kim, T., Kim, S., Shin, J., and Park, K. Elastic resource sharing for distributed deep learning. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21) (2021), pp. 721--739."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3297663.3310309"},{"key":"e_1_3_2_1_13_1","first-page":"117","volume-title":"12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","author":"Jyothi S. A.","year":"2016","unstructured":"Jyothi, S. A., Curino, C., Menache, I., Narayanamurthy, S. M., Tumanov, A., Yaniv, J., Mavlyutov, R., Goiri, I., Krishnan, S., Kulkarni, J., et al. Morpheus: Towards automated {SLOs} for enterprise clusters. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) (2016), pp. 117--134."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132749"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3185768.3186296"},{"key":"e_1_3_2_1_16_1","unstructured":"Kubernetes. Kubernetes. https:\/\/kubernetes.io\/."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/2971"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2021.3066142"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-23502-4_20"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 24th ACM\/IFIP\/USENIX Middleware Conference (Middleware)","author":"Liu J.","year":"2023","unstructured":"Liu, J., Wang, Q., Zhang, S., Hu, L., and Da Silva, D. Sora: A latency sensitive approach for microservice soft resource adaptation. In Proceedings of the 24th ACM\/IFIP\/USENIX Middleware Conference (Middleware) (2023)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS47924.2020.00046"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.123"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE5003.2020.00014"},{"key":"e_1_3_2_1_24_1","volume-title":"Human-level control through deep reinforcement learning. nature 518, 7540","author":"Mnih V.","year":"2015","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. Human-level control through deep reinforcement learning. nature 518, 7540 (2015), 529--533."},{"key":"e_1_3_2_1_25_1","first-page":"361","volume-title":"16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 19)","author":"Ousterhout A.","year":"2019","unstructured":"Ousterhout, A., Fried, J., Behrens, J., Belay, A., and Balakrishnan, H. Shenango: Achieving high {CPU} efficiency for latency-sensitive datacenter workloads. In 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 19) (2019), pp. 361--378."},{"key":"e_1_3_2_1_26_1","first-page":"01","article-title":"Microservices in practice, part 1: Reality check and service design","volume":"34","author":"Pautasso C.","year":"2017","unstructured":"Pautasso, C., Zimmermann, O., Amundsen, M., Lewis, J., and Josuttis, N. Microservices in practice, part 1: Reality check and service design. IEEE Annals of the History of Computing 34, 01 (2017), 91--98.","journal-title":"IEEE Annals of the History of Computing"},{"key":"e_1_3_2_1_27_1","unstructured":"PyTorch. Pytorch official website. https:\/\/pytorch.org\/."},{"key":"e_1_3_2_1_28_1","first-page":"805","volume-title":"14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20)","author":"Qiu H.","year":"2020","unstructured":"Qiu, H., Banerjee, S. S., Jha, S., Kalbarczyk, Z. T., and Iyer, R. K. Firm: An intelligent fine-grained resource management framework for slo-oriented microservices. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20) (2020), pp. 805--825."},{"key":"e_1_3_2_1_29_1","first-page":"387","volume-title":"2023 USENIX Annual Technical Conference (USENIX ATC 23)","author":"Qiu H.","year":"2023","unstructured":"Qiu, H., Mao, W., Wang, C., Franke, H., Youssef, A., Kalbarczyk, Z. T., Ba\u015far, T., and Iyer, R. K. {AWARE}: Automate workload autoscaling with reinforcement learning in production cloud systems. In 2023 USENIX Annual Technical Conference (USENIX ATC 23) (2023), pp. 387--402."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3342195.3387524"},{"key":"e_1_3_2_1_31_1","volume-title":"Learned autoscaling for cloud microservices with multi-armed bandits. arXiv preprint arXiv:2112.14845","author":"Sachidananda V.","year":"2021","unstructured":"Sachidananda, V., and Sivaraman, A. Learned autoscaling for cloud microservices with multi-armed bandits. arXiv preprint arXiv:2112.14845 (2021)."},{"key":"e_1_3_2_1_32_1","first-page":"177","volume-title":"13th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 18)","author":"Sriraman A.","year":"2018","unstructured":"Sriraman, A., and Wenisch, T. F. μtune: Auto-tuned threading for {OLDI} microservices. In 13th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 18) (2018), pp. 177--194."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCTA41146.2020.9206275"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2018.2871086"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3296957.3173206"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3542929.3563469"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/NOMS47738.2020.9110353"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00021"},{"key":"e_1_3_2_1_39_1","article-title":"Microscaler: Cost-effective scaling for microservice applications in the cloud with an online learning approach","author":"Yu G.","year":"2020","unstructured":"Yu, G., Chen, P., and Zheng, Z. Microscaler: Cost-effective scaling for microservice applications in the cloud with an online learning approach. IEEE Transactions on Cloud Computing (2020).","journal-title":"IEEE Transactions on Cloud Computing ("},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3236222"},{"key":"e_1_3_2_1_41_1","first-page":"655","volume-title":"2022 USENIX Annual Technical Conference (USENIX ATC 22)","author":"Zhang Z.","year":"2022","unstructured":"Zhang, Z., Ramanathan, M. K., Raj, P., Parwal, A., Sherwood, T., and Chabbi, M. {CRISP}: Critical path analysis of {Large-Scale} microservice architectures. In 2022 USENIX Annual Technical Conference (USENIX ATC 22) (2022), pp. 655--672."}],"event":{"name":"SoCC '23: ACM Symposium on Cloud Computing","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGOPS ACM Special Interest Group on Operating Systems"],"location":"Santa Cruz CA USA","acronym":"SoCC '23"},"container-title":["Proceedings of the 2023 ACM Symposium on Cloud Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3620678.3624980","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3620678.3624980","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T15:56:05Z","timestamp":1755878165000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3620678.3624980"}},"subtitle":["Reinforcement Learning-based Fast Concurrency Adaptation for Microservices in Cloud"],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":41,"alternative-id":["10.1145\/3620678.3624980","10.1145\/3620678"],"URL":"https:\/\/doi.org\/10.1145\/3620678.3624980","relation":{},"subject":[],"published":{"date-parts":[[2023,10,30]]},"assertion":[{"value":"2023-10-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}