{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:31:59Z","timestamp":1774369919477,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031484209","type":"print"},{"value":"9783031484216","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-48421-6_14","type":"book-chapter","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T19:06:29Z","timestamp":1700593589000},"page":"197-211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["ChainsFormer: A Chain Latency-Aware Resource Provisioning Approach for\u00a0Microservices Cluster"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4570-2722","authenticated-orcid":false,"given":"Chenghao","family":"Song","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0046-5153","authenticated-orcid":false,"given":"Minxian","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8985-2792","authenticated-orcid":false,"given":"Kejiang","family":"Ye","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4761-9973","authenticated-orcid":false,"given":"Huaming","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3913-0369","authenticated-orcid":false,"given":"Sukhpal Singh","family":"Gill","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9754-6496","authenticated-orcid":false,"given":"Rajkumar","family":"Buyya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9480-0356","authenticated-orcid":false,"given":"Chengzhong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,20]]},"reference":[{"key":"14_CR1","unstructured":"Burns, B., Beda, J., Hightower, K.: Kubernetes: up and running: dive into the future of infrastructure. O\u2019Reilly Media (2019)"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Gan, Y., Liang, M., Dev, S., et al.: Sage: practical and scalable ml-driven performance debugging in microservices. In: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2021, pp. 135\u2013151 (2021)","DOI":"10.1145\/3445814.3446700"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Gan, Y., Zhang, Y., Hu, K., et al.: Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices. In: Proceedings of the 24th International Conference on Architectural Support for Programming Languages and Operating Systemsm ASPLOS 2019, pp. 19\u201333 (2019)","DOI":"10.1145\/3297858.3304004"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Hossen, M.R., Islam, M.A., Ahmed, K.: Practical efficient microservice autoscaling with qos assurance. In: Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2022, pp. 240\u201352 (2022)","DOI":"10.1145\/3502181.3531460"},{"key":"14_CR5","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s00453-011-9583-5","volume":"65","author":"K Ioannidou","year":"2013","unstructured":"Ioannidou, K., Nikolopoulos, S.D.: The longest path problem is polynomial on cocomparability graphs. Algorithmica 65, 177\u2013205 (2013)","journal-title":"Algorithmica"},{"issue":"3","key":"14_CR6","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/TPDS.2020.3025914","volume":"32","author":"S Kardani-Moghaddam","year":"2021","unstructured":"Kardani-Moghaddam, S., Buyya, R., Ramamohanarao, K.: Adrl: a hybrid anomaly-aware deep reinforcement learning-based resource scaling in clouds. IEEE Trans. Parallel Distrib. Syst. 32(3), 514\u2013526 (2021)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"12","key":"14_CR7","doi-asserted-by":"publisher","first-page":"3901","DOI":"10.1109\/TPDS.2022.3174631","volume":"33","author":"S Luo","year":"2022","unstructured":"Luo, S., Xu, H., Lu, C., et al.: An in-depth study of microservice call graph and runtime performance. IEEE Trans. Parallel Distrib. Syst. 33(12), 3901\u20133914 (2022)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Mirhosseini, A., Elnikety, S., Wenisch, T.F.: Parslo: a gradient descent-based approach for near-optimal partial slo allotment in microservices. In: Proceedings of the ACM Symposium on Cloud Computing, SoCC 2021, pp. 442\u2013457 (2021)","DOI":"10.1145\/3472883.3486985"},{"key":"14_CR9","unstructured":"Qiu, H., Banerjee, S.S., Jha, S., et al.: $$\\{$$FIRM$$\\}$$: an intelligent fine-grained resource management framework for $$\\{$$SLO-Oriented$$\\}$$ microservices. In: 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20), pp. 805\u2013825 (2020)"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Rzadca, K., Findeisen, P., Swiderski, J., et al.: Autopilot: workload autoscaling at google. In: Proceedings of the Fifteenth European Conference on Computer Systems. EuroSys 2020 (2020)","DOI":"10.1145\/3342195.3387524"},{"issue":"3","key":"14_CR11","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1109\/TMC.2019.2957804","volume":"20","author":"S Wang","year":"2021","unstructured":"Wang, S., Guo, Y., Zhang, N., et al.: Delay-aware microservice coordination in mobile edge computing: a reinforcement learning approach. IEEE Trans. Mob. Comput. 20(3), 939\u2013951 (2021)","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"4","key":"14_CR12","doi-asserted-by":"publisher","first-page":"3995","DOI":"10.1109\/TNSM.2022.3210211","volume":"19","author":"M Xu","year":"2022","unstructured":"Xu, M., Song, C., Ilager, S., et al.: Coscal: multifaceted scaling of microservices with reinforcement learning. IEEE Trans. Netw. Serv. Manage. 19(4), 3995\u20134009 (2022)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Xu, M., Song, C., Wu, H., et al.: Esdnn: deep neural network based multivariate workload prediction in cloud computing environments. ACM Trans. Internet Technol. 22(3) (2022)","DOI":"10.1145\/3524114"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hua, W., Zhou, Z., et al.: Sinan: Ml-based and qos-aware resource management for cloud microservices. In: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2021, pp. 167\u2013181 (2021)","DOI":"10.1145\/3445814.3446693"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Xu, M., Rodriguez, M., et al.: Machine learning-based orchestration of containers: A taxonomy and future directions. ACM Comput. Surv. 54(10s) (2022)","DOI":"10.1145\/3510415"}],"container-title":["Lecture Notes in Computer Science","Service-Oriented Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-48421-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T19:07:53Z","timestamp":1700593673000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-48421-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031484209","9783031484216"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-48421-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"20 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSOC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Service-Oriented Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icsoc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icsoc2023.diag.uniroma1.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ConfTool","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"208","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"other papers accepted: 3 industry full papers, 3 keynote abstracts (in the front matter)","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}