{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T18:53:22Z","timestamp":1759776802165,"version":"3.41.0"},"publisher-location":"New York, New York, USA","reference-count":19,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1145\/3335484.3335493","type":"proceedings-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T16:54:49Z","timestamp":1562604889000},"page":"6-10","source":"Crossref","is-referenced-by-count":4,"title":["Optimized Speculative Execution Strategy for Different Workload Levels in Heterogeneous Spark Cluster"],"prefix":"10.1145","author":[{"given":"Xiaohan","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youlong","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","reference":[{"key":"key-10.1145\/3335484.3335493-1","unstructured":"Zhiwei Y, Quan Z, Song W, et al. Adaptive Task Scheduling Strategy for Heterogeneous Spark Cluster. Computer Engineering, 2016, 42(1): 31--35,40."},{"key":"key-10.1145\/3335484.3335493-2","doi-asserted-by":"crossref","unstructured":"Dean, Jeffrey, and Sanjay Ghemawat. MapReduce: simplified data processing on large clusters. Communications of the ACM 51.1 (2008): 107--113.","DOI":"10.1145\/1327452.1327492"},{"key":"key-10.1145\/3335484.3335493-3","unstructured":"Ananthanarayanan G, Kandula S, Greenberg A, et al. Reining in the outliers in map-reduce clusters using Mantri. Usenix Conference on Operating Systems Design and Implementation. USENIX Association, 2010: 265--278."},{"key":"key-10.1145\/3335484.3335493-4","doi-asserted-by":"crossref","unstructured":"Zaharia M, Xin R S, Wendell P, et al. Apache Spark: a unified engine for big data processing. Communications of the ACM, 2016, 59(11): 56--65.","DOI":"10.1145\/2934664"},{"key":"key-10.1145\/3335484.3335493-5","doi-asserted-by":"crossref","unstructured":"Tantisiriroj W, Son S W, Patil S, et al. On the duality of data-intensive file system design: reconciling HDFS and PVFS. High PERFORMANCE Computing, Networking, Storage and Analysis. IEEE, 2011: 1--12.","DOI":"10.1145\/2063384.2063474"},{"key":"key-10.1145\/3335484.3335493-6","doi-asserted-by":"crossref","unstructured":"Ling X, Yuan Y, Wang D, et al. Joint scheduling of MapReduce jobs with servers: Performance bounds and experiments. Journal of Parallel & Distributed Computing, 2016, s 90--91(IEEEINFOCOM2014): 52--66.","DOI":"10.1016\/j.jpdc.2016.02.002"},{"key":"key-10.1145\/3335484.3335493-7","doi-asserted-by":"crossref","unstructured":"Xu H, Lau W C, Yang Z, et al. Mitigating Service Variability in MapReduce Clusters via Task Cloning: A Competitive Analysis. IEEE Transactions on Parallel & Distributed Systems, 2017, 28(10): 2866--2880.","DOI":"10.1109\/TPDS.2017.2689767"},{"key":"key-10.1145\/3335484.3335493-8","doi-asserted-by":"crossref","unstructured":"Zhao X, Kang K, Sun Y Z, et al. Insight and reduction of MapReduce stragglers in heterogeneous environment. IEEE International Conference on CLUSTER Computing. IEEE, 2014: 1--8.","DOI":"10.1109\/CLUSTER.2013.6702673"},{"key":"key-10.1145\/3335484.3335493-9","doi-asserted-by":"crossref","unstructured":"Qiu Z, P&#233;rez J F, Harrison P G. Variability-aware request replication for latency curtailment. IEEE INFOCOM 2016 - the IEEE International Conference on Computer Communications. IEEE, 2015: 12--21.","DOI":"10.1109\/INFOCOM.2016.7524365"},{"key":"key-10.1145\/3335484.3335493-10","doi-asserted-by":"crossref","unstructured":"Xu H, Lau W C. Task-Cloning Algorithms in a MapReduce Cluster with Competitive Performance Bounds. International Conference on Distributed Computing Systems. IEEE 2015: 339--348.","DOI":"10.1109\/ICDCS.2015.42"},{"key":"key-10.1145\/3335484.3335493-11","unstructured":"Xu H, Lau W C. Optimization for Speculative Execution in Big Data Processing Clusters. IEEE Transactions on Parallel & Distributed Systems, 2017, 28(2): 530--545."},{"key":"key-10.1145\/3335484.3335493-12","doi-asserted-by":"crossref","unstructured":"Liu Q, Jin D, Liu X, et al. A Survey of Speculative Execution Strategy in MapReduce. International Conference on Cloud Computing and Security. Springer, 2016: 296--307.","DOI":"10.1007\/978-3-319-48671-0_27"},{"key":"key-10.1145\/3335484.3335493-13","doi-asserted-by":"crossref","unstructured":"Wu H, Li K, Tang Z, et al. A Heuristic Speculative Execution Strategy in Heterogeneous Distributed Environments. International Symposium on Parallel Architectures. IEEE, 2014: 268--273.","DOI":"10.1109\/PAAP.2014.29"},{"key":"key-10.1145\/3335484.3335493-14","doi-asserted-by":"crossref","unstructured":"Yang H, Liu X, Chen S, et al. Improving Spark performance with MPTE in heterogeneous environments. International Conference on Audio, Language and Image Processing. IEEE, 2017: 28--33.","DOI":"10.1109\/ICALIP.2016.7846627"},{"key":"key-10.1145\/3335484.3335493-15","unstructured":"Ananthanarayanan G, Ghodsi A, Shenker S, et al. Effective straggler mitigation: attack of the clones. Proc Nsdi, 2013, 21(10): 185--198."},{"key":"key-10.1145\/3335484.3335493-16","unstructured":"PUMA Datasets [Online]. Available: https:\/\/engineering.purdue.edu\/~puma\/datasets.htm.2016."},{"key":"key-10.1145\/3335484.3335493-17","doi-asserted-by":"crossref","unstructured":"Leskovec J, Lang K J, Dasgupta A, et al. Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. Internet Mathematics, 2009, 6(1): 29--123.","DOI":"10.1080\/15427951.2009.10129177"},{"key":"key-10.1145\/3335484.3335493-18","doi-asserted-by":"crossref","unstructured":"Chen Q, Liu C, Xiao Z. Improving MapReduce Performance Using Smart Speculative Execution Strategy. IEEE Computer Society, 2014: 954--967.","DOI":"10.1109\/TC.2013.15"},{"key":"key-10.1145\/3335484.3335493-19","doi-asserted-by":"crossref","unstructured":"Kaur S, Saini P. Deadline-aware MapReduce scheduling with selective speculative execution. International Conference on Computing, Communication and NETWORKING Technologies. IEEE Computer Society, 2017: 1--5.","DOI":"10.1109\/ICCCNT.2017.8204109"}],"event":{"number":"4","sponsor":["Shenzhen University","Sun Yat-Sen University"],"acronym":"ICBDC 2019","name":"the 2019 4th International Conference","start":{"date-parts":[[2019,5,10]]},"location":"Guangzhou, China","end":{"date-parts":[[2019,5,12]]}},"container-title":["Proceedings of the 2019 4th International Conference on Big Data and Computing  - ICBDC 2019"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3335484.3335493","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3335493&ftid=2070094&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:19Z","timestamp":1750206379000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3335484.3335493"}},"subtitle":[],"proceedings-subject":"Big Data and Computing","short-title":[],"issued":{"date-parts":[[2019]]},"references-count":19,"URL":"https:\/\/doi.org\/10.1145\/3335484.3335493","relation":{},"subject":[],"published":{"date-parts":[[2019]]}}}