{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T14:51:22Z","timestamp":1776351082476,"version":"3.51.2"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T00:00:00Z","timestamp":1598832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61702014"],"award-info":[{"award-number":["61702014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Top Young Innovative Talents of North China University of Technology","award":["XN018022"],"award-info":[{"award-number":["XN018022"]}]},{"name":"National Key R8D Program of China","award":["2018YFB1402500"],"award-info":[{"award-number":["2018YFB1402500"]}]},{"name":"Beijing Municipal Natural Science Foundation","award":["4192020 and 4202021"],"award-info":[{"award-number":["4192020 and 4202021"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM\/IMS Trans. Data Sci."],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>In urban Internet of Things (IoT) environments, data generated in real time could be processed by analytical applications in online or offline mode. In the management perspective of runtime environments, such modes can hardly be supported in a unified framework under multiple restrictions such as latency, utility, and QoS (quality of service). Meanwhile in the optimization perspective of specific applications, it is difficult for current infrastructure to efficiently allocate sufficient resources to tasks of an application, simultaneously considering multiple factors such as data size, velocity, and locality. In this article, two task allocation methods are proposed for batch and stream analytics to improve resource utility with auto-scaling guarantee when an analytical application is submitted or sudden workloads appear. Taking the highway domain as an example, the task allocation methods are implemented in a novel combined framework accordingly. Using both real-world and simulated data, extensive experiments show that our methods can improve utility efficiency with effective offload support.<\/jats:p>","DOI":"10.1145\/3374751","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T08:37:20Z","timestamp":1594111040000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Task Allocation in Hybrid Big Data Analytics for Urban IoT Applications"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9982-5488","authenticated-orcid":false,"given":"Weilong","family":"Ding","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China and Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuofeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China and Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9933-1170","authenticated-orcid":false,"given":"Jianwu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China and Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,9,14]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 39th International Conference on Very Large Data Bases (VLDB\u201913)","author":"Akidau T.","unstructured":"T. Akidau, A. Balikov, K. Bekiroglu, S. Chernyak, J. Haberman, R. Lax, S. Mcveety, D. Mills, P. Nordstrom, and S. Whittle. 2013. MillWheel: Fault-tolerant stream processing at internet scale. In Proceedings of the 39th International Conference on Very Large Data Bases (VLDB\u201913). 734--745."},{"key":"e_1_2_1_2_1","volume-title":"Storm Real-time Processing Cookbook","author":"Anderson Q.","unstructured":"Q. Anderson. 2013. Storm Real-time Processing Cookbook. Packt Publishing Ltd."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the Proceedings of the 7th ACM international Conference on Distributed Event-based Systems. ACM, 207--218","author":"Aniello L.","unstructured":"L. Aniello, R. Baldoni, and L. Querzoni. 2013. Adaptive online scheduling in storm. In Proceedings of the Proceedings of the 7th ACM international Conference on Distributed Event-based Systems. ACM, 207--218."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/2733004.2733016"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Y. Cao and H. Wang. 2015. The key technologies of real-time processing large scale microblog data stream. In Cloud Computing and Big Data W. Qiang X. Zheng and C.-H. Hsu (Eds.). Springer International Publishing Cham 295--306.","DOI":"10.1007\/978-3-319-28430-9_22"},{"key":"e_1_2_1_6_1","first-page":"28","article-title":"Apache Flink: Stream and batch processing in a single engine","volume":"36","author":"Carbone P.","year":"2015","unstructured":"P. Carbone, S. Ewen, S. Haridi, A. Katsifodimos, V. Markl, and K. Tzoumas. 2015. Apache Flink: Stream and batch processing in a single engine. IEEE Bull. IEEE Comput. Soc. Techn. Commit. Data Eng. 36, 4 (2015), 28--38.","journal-title":"IEEE Bull. IEEE Comput. Soc. Techn. Commit. Data Eng."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 2016 International Conference on Management of Data. ACM, 1087--1098","author":"Chen G. J.","unstructured":"G. J. Chen, J. L. Wiener, S. Iyer, A. Jaiswal, R. Lei, N. Simha, W. Wang, K. Wilfong, T. Williamson, and S. Yilmaz. 2016. Realtime data processing at facebook. In Proceedings of the 2016 International Conference on Management of Data. ACM, 1087--1098."},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the 19th International Conference on Network-Based Information Systems (NBiS\u201916)","author":"Cho H.","unstructured":"H. Cho, H. Shiokawa, and H. Kitagawa. 2016. Jsflow: Integration of massive streams and batches via json-based dataflow algebra. In Proceedings of the 19th International Conference on Network-Based Information Systems (NBiS\u201916). IEEE, 188--195."},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jnca.2017.12.001","article-title":"Distributed data stream processing and edge computing: A survey on resource elasticity and future directions","volume":"103","author":"Dias De Assun O. M.","year":"2018","unstructured":"O. M. Dias De Assun, A. Da Silva Veith, and R. Buyya. 2018. Distributed data stream processing and edge computing: A survey on resource elasticity and future directions. J. Netw. Comput. Appl. 103 (2018), 1--17.","journal-title":"J. Netw. Comput. Appl."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2244"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.08.026"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.simpat.2017.01.002","article-title":"A collaborative calculation on real-time stream in smart cities","volume":"73","author":"Ding W.","year":"2017","unstructured":"W. Ding, S. Zhang, and Z. Zhao. 2017. A collaborative calculation on real-time stream in smart cities. Simul. Model. Pract. Theory 73 (2017), 72--82.","journal-title":"Simul. Model. Pract. Theory"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1155\/2018\/9354273","article-title":"DS-Harmonizer: A harmonization service on spatio-temporal data stream in edge computing environment","volume":"2018","author":"Ding W.","year":"2018","unstructured":"W. Ding and Z. Zhao. 2018. DS-Harmonizer: A harmonization service on spatio-temporal data stream in edge computing environment. Wireless Commun. Mobile Comput. 2018 (2018), 12.","journal-title":"Wireless Commun. Mobile Comput."},{"key":"e_1_2_1_14_1","doi-asserted-by":"crossref","unstructured":"W. Ding Z. Zhao and Y. Han. 2016. An adaptive replica mechanism for real-time stream processing. In Proceedings of the 2016 International IEEE Conferences on Ubiquitous Intelligence 8 Computing Advanced and Trusted Computing Scalable Computing and Communications Cloud and Big Data Computing Internet of People and Smart World Congress (UIC\/ATC\/ScalCom\/CBDCom\/IoP\/SmartWorld\u201916). IEEE 449--455.","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0081"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the 23rd IEEE International Conference on Web Services (ICWS\u201916)","author":"Ding W.","unstructured":"W. Ding, Z. Zhao, and Y. Han. 2016. A framework to improve the availability of stream computing. In Proceedings of the 23rd IEEE International Conference on Web Services (ICWS\u201916). IEEE, 594--601."},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1504\/IJIMS.2020.110231","article-title":"A multidimensional service template for data analysis in highway domain","volume":"4","author":"Ding W.","year":"2020","unstructured":"W. Ding, J. Zou, and Z. Zhao. 2020. A multidimensional service template for data analysis in highway domain. International Journal of Internet Manufacturing and Services 4, 4 (2020), 290--306.","journal-title":"International Journal of Internet Manufacturing and Services"},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT\u201915)","author":"Farris I.","unstructured":"I. Farris, L. Militano, M. Nitti, L. Atzori, and A. Iera. 2015. Federated edge-assisted mobile clouds for service provisioning in heterogeneous IoT environments. In Proceedings of the 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT\u201915). 591--596."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243929"},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the IEEE Conference on Open Systems (ICOS\u201915)","author":"Ismail B. I.","unstructured":"B. I. Ismail, E. M. Goortani, M. B. A. Karim, W. M. Tat, S. Setapa, J. Y. Luke, and O. H. Hoe. 2015. Evaluation of Docker as Edge computing platform. In Proceedings of the IEEE Conference on Open Systems (ICOS\u201915). 130--135."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2016.2519462"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0210-7"},{"key":"e_1_2_1_22_1","volume-title":"Computer Science","author":"Lampton J. H.","unstructured":"J. H. Lampton. 2015. Pig squeal: Bridging batch and stream processing using incremental updates. In Computer Science. University of Maryland."},{"key":"e_1_2_1_23_1","volume-title":"Computer Science","author":"Li B.","unstructured":"B. Li. 2015. A platform for scalable low-latency analytics using mapreduce. In Computer Science. University of Massachusetts--Amherst, 378."},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the 45th International Conference on Parallel Processing Workshops (ICPPW\u201916)","author":"Locher T.","unstructured":"T. Locher and A. C. Sima. 2016. Cyclone: Unified stream and batch processing. In Proceedings of the 45th International Conference on Parallel Processing Workshops (ICPPW\u201916). IEEE, 220--229."},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of the International Conference on Distributed Computing Systems (ICDCS\u201915)","author":"Lohrmann B.","unstructured":"B. Lohrmann, P. Janacik, and O. Kao. 2015. Elastic stream processing with latency guarantees. In Proceedings of the International Conference on Distributed Computing Systems (ICDCS\u201915)."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2831347.2831354"},{"key":"e_1_2_1_27_1","first-page":"865","article-title":"Traffic flow prediction with big data: A deep learning approach","volume":"16","author":"Lv Y.","year":"2015","unstructured":"Y. Lv, Y. Duan, W. Kang, Z. Li, and F. Y. Wang. 2015. Traffic flow prediction with big data: A deep learning approach. IEEE Trans. Intell. Transport. Syst. 16 (2015), 865--873.","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2682318"},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. ACM","author":"Machen A.","unstructured":"A. Machen, S. Wang, K. K. Leung, B. J. Ko, and T. Salonidis. 2016. Migrating running applications across mobile edge clouds: poster. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. ACM, New York City, NY, 435--436."},{"key":"e_1_2_1_30_1","volume-title":"Fog Computing: A taxonomy, survey and future directions. In Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives, B. Di Martino, K.-C. Li","author":"Mahmud R.","year":"2018","unstructured":"R. Mahmud, R. Kotagiri, and R. Buyya. 2018. Fog Computing: A taxonomy, survey and future directions. In Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives, B. Di Martino, K.-C. Li, L. T. Yang, and A. Esposito (Eds.). Springer, Singapore, 103--130."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10922-016-9385-9"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.01.018"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2814571"},{"key":"e_1_2_1_34_1","volume-title":"Proceedings of the 2016 IEEE International Smart Cities Conference (ISC2\u201916)","author":"Nazmudeen M. S. H.","unstructured":"M. S. H. Nazmudeen, A. T. Wan, and S. M. Buhari. 2016. Improved throughput for Power Line Communication (PLC) for smart meters using fog computing based data aggregation approach. In Proceedings of the 2016 IEEE International Smart Cities Conference (ISC2\u201916). 1--4."},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW\u201916)","author":"Pahl C.","unstructured":"C. Pahl, S. Helmer, L. Miori, J. Sanin, and B. Lee. 2016. A Container-Based Edge Cloud PaaS Architecture Based on Raspberry Pi Clusters. In Proceedings of the 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW\u201916). 117--124."},{"key":"e_1_2_1_36_1","volume-title":"Proceedings of the 16th Annual Middleware Conference. ACM, 149--161","author":"Peng B.","unstructured":"B. Peng, M. Hosseini, Z. Hong, R. Farivar, and R. Campbell. 2015. R-Storm: Resource-aware scheduling in storm. In Proceedings of the 16th Annual Middleware Conference. ACM, 149--161."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3057266"},{"key":"e_1_2_1_38_1","first-page":"1275","article-title":"Edge computing for the Internet of Things: A case study","volume":"5","author":"Premsankar G.","year":"2018","unstructured":"G. Premsankar, M. D. Francesco, and T. Taleb. 2018. Edge computing for the Internet of Things: A case study. IEEE IoT J. 5 (2018), 1275--1284.","journal-title":"IEEE IoT J."},{"key":"e_1_2_1_39_1","volume-title":"Proceedings of the European Conference on Computer Systems (Eurosys\u201913)","author":"Qian Z.","unstructured":"Z. Qian, Y. He, C. Su, Z. Wu, H. Zhu, T. Zhang, L. Zhou, Y. Yu, and Z. Zhang. 2013. TimeStream: Reliable stream computation in the cloud. In Proceedings of the European Conference on Computer Systems (Eurosys\u201913)."},{"key":"e_1_2_1_40_1","unstructured":"B. T. Rao and L. Reddy. 2012. Survey on improved scheduling in Hadoop MapReduce in cloud environments. arXiv preprint arXiv:1207.0780."},{"key":"e_1_2_1_41_1","volume-title":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming. ACM, 43--48","author":"Salmon L.","unstructured":"L. Salmon, C. Ray, and C. Claramunt. 2015. A hybrid approach combining real-time and archived data for mobility analysis. In Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming. ACM, 43--48."},{"key":"e_1_2_1_42_1","volume-title":"Proceedings of the 1st International Workshop on Big Dynamic Distributed Data (BD3\u201913)","author":"Sattler K.-U.","unstructured":"K.-U. Sattler and F. Beier. 2013. Towards elastic stream processing: Patterns and infrastructure. In Proceedings of the 1st International Workshop on Big Dynamic Distributed Data (BD3\u201913). Citeseer, 49."},{"key":"e_1_2_1_43_1","volume-title":"Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems. ACM, 258--269","author":"Saurez E.","unstructured":"E. Saurez, K. Hong, D. Lillethun, U. Ramachandran, and B. Ottenwalder. 2016. Incremental deployment and migration of geo-distributed situation awareness applications in the fog. In Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems. ACM, 258--269."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2579198"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2015.03.012"},{"key":"e_1_2_1_46_1","volume-title":"Proceedings of the 2016 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN\u201916)","author":"Yangui S.","unstructured":"S. Yangui, P. Ravindran, O. Bibani, R. H. Glitho, N. B. Hadj-Alouane, M. J. Morrow, and P. A. Polakos. 2016. A platform as-a-service for hybrid cloud\/fog environments. In Proceedings of the 2016 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN\u201916), 1--7."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2778504"},{"key":"e_1_2_1_48_1","volume-title":"An architecture for fast and general data processing on large clusters. Doctor of Philosophy in Computer Science, Electrical Engineering and Computer Sciences","author":"Zaharia M.","unstructured":"M. Zaharia. 2014. An architecture for fast and general data processing on large clusters. Doctor of Philosophy in Computer Science, Electrical Engineering and Computer Sciences, University of California at Berkeley."},{"key":"e_1_2_1_49_1","doi-asserted-by":"crossref","unstructured":"W. Zhang H. Lv L. Xu Y. Liu X. Liu Q. Lu Z. Li and J. Zhou. 2017. An Online-Offline Combined Big Data Mining Platform. In Proceedings of the IEEE 14th International Conference on Dependable Autonomic and Secure Computing 14th International Conference on Pervasive Intelligence and Computing 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech\u201917). 1220--1225.","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2017.195"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2015.2500258"},{"key":"e_1_2_1_51_1","doi-asserted-by":"crossref","unstructured":"J. Y. Zhu J. Xu and V. O. K. Li. 2016. A Four-Layer Architecture for Online and Historical Big Data Analytics. In Proceedings of the IEEE 14th International Conference on Dependable Autonomic and Secure Computing 14th International Conference on Pervasive Intelligence and Computing 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech 2016). IEEE 634--639.","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.115"}],"container-title":["ACM\/IMS Transactions on Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3374751","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3374751","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T13:57:51Z","timestamp":1776347871000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3374751"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,31]]},"references-count":51,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,8,31]]}},"alternative-id":["10.1145\/3374751"],"URL":"https:\/\/doi.org\/10.1145\/3374751","relation":{},"ISSN":["2691-1922"],"issn-type":[{"value":"2691-1922","type":"print"}],"subject":[],"published":{"date-parts":[[2020,8,31]]},"assertion":[{"value":"2019-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-11-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-09-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}