{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T11:27:47Z","timestamp":1742988467470,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030875701"},{"type":"electronic","value":"9783030875718"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87571-8_14","type":"book-chapter","created":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T09:02:47Z","timestamp":1631782967000},"page":"156-168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Online Runtime Prediction Method for\u00a0Distributed Iterative Jobs"],"prefix":"10.1007","author":[{"given":"Xiaofei","family":"Yue","sequence":"first","affiliation":[]},{"given":"Lan","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Yuhai","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Hangxu","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Guoren","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,17]]},"reference":[{"key":"14_CR1","unstructured":"Dean, J., et al.: Large scale distributed deep networks. In: Advances in Neural Information Processing Systems, pp. 1232\u20131240 (2012)"},{"key":"14_CR2","unstructured":"Carbone, P., et\u00a0al.: Apache flink\u2122: stream and batch processing in a single engine. IEEE Data Eng. Bull. 38(4), 28\u201338 (2015)"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Tumanov, A., et al.: TetriSched: global rescheduling with adaptive plan-ahead in dynamic heterogeneous clusters. In: Proceedings of the Eleventh European Conference on Computer Systems, pp. 35:1\u201335:16 (2016)","DOI":"10.1145\/2901318.2901355"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Wolf, J.L., et al.: FLEX: a slot allocation scheduling optimizer for mapreduce workloads. In: 11th International Middleware Conference, vol. 6452, pp. 1\u201320 (2010)","DOI":"10.1007\/978-3-642-16955-7_1"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Thamsen, L., et al.: Selecting resources for distributed dataflow systems according to runtime targets. In: 35th IEEE International Performance Computing and Communications Conference, pp. 1\u20138 (2016)","DOI":"10.1109\/PCCC.2016.7820629"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Lama, P., Zhou, X.: AROMA: automated resource allocation and configuration of mapreduce environment in the cloud. In: 9th International Conference on Autonomic Computing, pp. 63\u201372 (2012)","DOI":"10.1145\/2371536.2371547"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Renner, T., et al.: Adaptive resource management for distributed data analytics based on container-level cluster monitoring. In: Proceedings of the 6th International Conference on Data Science, Technology and Applications, pp. 38\u201347 (2017)","DOI":"10.5220\/0006420100380047"},{"key":"14_CR8","doi-asserted-by":"publisher","unstructured":"Thamsen, L., et al.: Ellis: dynamically scaling distributed dataflows to meet runtime targets. In: IEEE International Conference on Cloud Computing Technology and Science, pp. 146\u2013153 (2017). https:\/\/doi.org\/10.1109\/CloudCom.2017.37","DOI":"10.1109\/CloudCom.2017.37"},{"issue":"14","key":"14_CR9","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.14778\/2556549.2556553","volume":"6","author":"AD Popescu","year":"2013","unstructured":"Popescu, A.D., et al.: Predict: towards predicting the runtime of large scale iterative analytics. Proc. VLDB Endow. 6(14), 1678\u20131689 (2013)","journal-title":"Proc. VLDB Endow."},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Koch, J., et al.: SMiPE: estimating the progress of recurring iterative distributed dataflows. In: 18th International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 156\u2013163 (2017)","DOI":"10.1109\/PDCAT.2017.00034"},{"key":"14_CR11","unstructured":"Kumar, V., et al.: Apache Hadoop YARN: yet another resource negotiator. In: ACM Symposium on Cloud Computing, pp. 5:1\u20135:16 (2013)"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Hilman, M.H., et al.: Task runtime prediction in scientific workflows using an online incremental learning approach. In: 11th IEEE\/ACM International Conference on Utility and Cloud Computing, pp. 93\u2013102 (2018)","DOI":"10.1109\/UCC.2018.00018"},{"key":"14_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-30952-7_1","volume-title":"Web Information Systems and Applications","author":"M Gao","year":"2019","unstructured":"Gao, M., et al.: Online anomaly detection via incremental tensor decomposition. In: Ni, W., Wang, X., Song, W., Li, Y. (eds.) WISA 2019. LNCS, vol. 11817, pp. 3\u201314. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30952-7_1"},{"issue":"1","key":"14_CR14","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/TCC.2017.2732344","volume":"8","author":"T Pham","year":"2020","unstructured":"Pham, T., et al.: Predicting workflow task execution time in the cloud using A two-stage machine learning approach. IEEE Trans. Cloud Comput. 8(1), 256\u2013268 (2020). https:\/\/doi.org\/10.1109\/TCC.2017.2732344","journal-title":"IEEE Trans. Cloud Comput."},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"da Silva, R.F., et al.: Online task resource consumption prediction for scientific workflows. Parallel Process. Lett. 25(3), 1541003:1\u20131541003:25 (2015)","DOI":"10.1142\/S0129626415410030"},{"key":"14_CR16","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.future.2017.02.040","volume":"72","author":"S Pumma","year":"2017","unstructured":"Pumma, S., et al.: A runtime estimation framework for ALICE. Future Gener. Comput. Syst. 72, 65\u201377 (2017). https:\/\/doi.org\/10.1016\/j.future.2017.02.040","journal-title":"Future Gener. Comput. Syst."}],"container-title":["Lecture Notes in Computer Science","Web Information Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87571-8_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:51:17Z","timestamp":1709833877000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87571-8_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030875701","9783030875718"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87571-8_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"17 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaifeng","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wisa22021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.wisa.org.cn\/wisa2021\/index.html","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"206","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":"49","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":"18","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":"24% - 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":"6,5","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":"3","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)"}}]}}