{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:44:34Z","timestamp":1767325474728,"version":"3.48.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032105066","type":"print"},{"value":"9783032105073","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-10507-3_12","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:40:39Z","timestamp":1767325239000},"page":"224-242","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fedsort: An Optimized Federated Scheduling Strategy for\u00a0Cloud Workloads with\u00a0Inter-task Dependencies"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9358-4905","authenticated-orcid":false,"given":"Suhas Gowda","family":"Harish","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2279-8909","authenticated-orcid":false,"given":"Shresht","family":"Veeraswamy Gunashekar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4944-5287","authenticated-orcid":false,"given":"Sparsh","family":"Balnad kattemane","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1635-7576","authenticated-orcid":false,"given":"Meghana","family":"Thiyyakat","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1549-2971","authenticated-orcid":false,"given":"Prafullata K.","family":"Auradkar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Vavilapalli, V.K., et al.: Apache hadoop yarn: yet another resource negotiator. In: Proceedings of the 4th Annual Symposium on Cloud Computing, pp. 1\u201316 (2013)","DOI":"10.1145\/2523616.2523633"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Ousterhout, K., Wendell, P., Zaharia, M., Stoica, I.: Sparrow: distributed, low latency scheduling. In: Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, pp. 69\u201384 (2013)","DOI":"10.1145\/2517349.2522716"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Delgado, P., Didona, D., Dinu, F., Zwaenepoel, W.: Job-aware scheduling in eagle: divide and stick to your probes. In: Proceedings of the Seventh ACM Symposium on Cloud Computing, pp. 497\u2013509 (2016)","DOI":"10.1145\/2987550.2987563"},{"key":"12_CR4","unstructured":"Delgado, P., Dinu, F., Kermarrec, A.M., Zwaenepoel, W.: Hawk: hybrid datacenter scheduling. In: 2015 USENIX Annual Technical Conference (USENIX ATC 2015), pp. 499\u2013510 (2015)"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Pigeon: an effective distributed, hierarchical datacenter job scheduler. In: Proceedings of the ACM Symposium on Cloud Computing, pp. 246\u2013258 (2019)","DOI":"10.1145\/3357223.3362728"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Thiyyakat, M., Kalambur, S., Sitaram, D.: Megha: decentralized federated scheduling for data center workloads. In: 2023 15th International Conference on COMmunication Systems & NETworkS (COMSNETS), pp. 278\u2013286. IEEE (2023)","DOI":"10.1109\/COMSNETS56262.2023.10041383"},{"key":"12_CR7","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11241-014-9213-9","volume":"51","author":"J Li","year":"2015","unstructured":"Li, J., Luo, Z., Ferry, D., Agrawal, K., Gill, C., Chenyang, L.: Global EDF scheduling for parallel real-time tasks. Real-Time Syst. 51, 395\u2013439 (2015)","journal-title":"Real-Time Syst."},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Akbar, M.F., Munir, E.U., Rafique, M.M., Malik, Z., Khan, S.U., Yang, L.T.: List-based task scheduling for cloud computing. In: 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 652\u2013659. IEEE (2016)","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData.2016.143"},{"key":"12_CR9","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1007\/978-3-030-03577-8_53","volume-title":"Information Systems and Technologies to Support Learning","author":"SA Makhlouf","year":"2019","unstructured":"Makhlouf, S.A., Yagoubi, B.: Clustering strategy for scientific workflow applications in IAAS cloud environment. In: Rocha, \u00c1., Serrhini, M. (eds.) EMENA-ISTL 2018. SIST, vol. 111, pp. 482\u2013491. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-03577-8_53"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Sakellariou, R., Zhao, H.: A hybrid heuristic for DAG scheduling on heterogeneous systems. In: Proceedings of 18th International Parallel and Distributed Processing Symposium, p. 111. IEEE (2004)","DOI":"10.1109\/IPDPS.2004.1303065"},{"key":"12_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2024.103403","volume":"156","author":"M Thiyyakat","year":"2024","unstructured":"Thiyyakat, M., Chaudhary, R., Nayak, S.G., Shetty, A., Kalambur, S., Sitaram, D.: Eventually-consistent federated scheduling for data center workloads. Ad Hoc Netw. 156, 103403 (2024)","journal-title":"Ad Hoc Netw."},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Chai, Z., Anwar, A.: Characterizing co-located datacenter workloads: an alibaba case study. In: Proceedings of the 9th Asia-Pacific Workshop on Systems, pp. 1\u20133 (2018)","DOI":"10.1145\/3265723.3265742"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Jassas, M., Mahmoud, Q.H.: Failure analysis and characterization of scheduling jobs in google cluster trace. In: IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, pp. 3102\u20133107. IEEE (2018)","DOI":"10.1109\/IECON.2018.8592822"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Reiss, C., Tumanov, A., Ganger, G.R., Katz, R.H., Kozuch, M.A.: Heterogeneity and dynamicity of clouds at scale: google trace analysis. In: Proceedings of the Third ACM Symposium on Cloud Computing, pp. 1\u201313 (2012)","DOI":"10.1145\/2391229.2391236"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Tirmazi, M., et al.: Borg: the next generation. In: Proceedings of the Fifteenth European Conference on Computer Systems, pp. 1\u201314 (2020)","DOI":"10.1145\/3342195.3387517"},{"key":"12_CR16","unstructured":"Reiss, C., Wilkes, J., Hellerstein, J.L.: Google cluster-usage traces: format+ schema. Google Inc., White Paper, vol. 1, pp. 1\u201314 (2011)"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Lu, C., Chen, W., Ye, K., Xu, C.-Z.: Understanding the workload characteristics in alibaba: a view from directed acyclic graph analysis. In: 2020 International Conference on High Performance Big Data and Intelligent Systems (HPBD &IS), pp. 1\u20138. IEEE (2020)","DOI":"10.1109\/HPBDIS49115.2020.9130578"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Li, C., Tao, Y., Yang, R., Tang, H., Xu, J.: Fuxi: a fault-tolerant resource management and job scheduling system at internet scale. In: Proceedings of the VLDB Endowment, vol. 7, pp. 1393\u20131404. VLDB Endowment Inc. (2014)","DOI":"10.14778\/2733004.2733012"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Learning-Based Synthetic\u00a0Job Trace. An empirical study of machine learning-based synthetic job trace. In: Job Scheduling Strategies for Parallel Processing, p. 20. Springer (2024)","DOI":"10.1007\/978-3-031-74430-3_2"},{"key":"12_CR20","unstructured":"Amvrosiadis, G., Park, J.W., Ganger, G.R., Gibson, G.A., Baseman, E., DeBardeleben, N.: On the diversity of cluster workloads and its impact on research results. In: 2018 USENIX Annual Technical Conference (USENIX ATC 2018), pp. 533\u2013546 (2018)"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Tian, H., Zheng, Y., Wang, W.: Characterizing and synthesizing task dependencies of data-parallel jobs in alibaba cloud. In: Proceedings of the ACM Symposium on Cloud Computing, pp. 139\u2013151 (2019)","DOI":"10.1145\/3357223.3362710"}],"container-title":["Lecture Notes in Computer Science","Job Scheduling Strategies for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-10507-3_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:40:41Z","timestamp":1767325241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-10507-3_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032105066","9783032105073"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-10507-3_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"JSSPP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Job Scheduling Strategies for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"jsspp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/jsspp.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}