[1] 孙大为, 张广艳, 郑纬民.大数据流式计算:关键技术及系统实例[J].软件学报, 2014, 25(4):839-862. SUN D W, ZHANG G Y, ZHENG W M.Big data flow computing:key technologies and system examples[J].Journal of Software, 2014, 25(4):839-862.(in Chinese) [2] SHAHVERDI E, AWAD A, SAKR S.Big stream processing systems:an experimental evaluation[C]//Proceedings of the 35th International Conference on Data Engineering Workshops.Washington D.C., USA:IEEE Press, 2019:53-60. [3] QIN X, JIANG H.A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters[J].Journal of Parallel and Distributed Computing, 2005, 65(8):885-900. [4] ZHU X M, HE C, LI K L, et al.Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters[J].Journal of Parallel and Distributed Computing, 2012, 72(6):751-763. [5] WU Y, WANG Z R, RUAN Q, et al.Node scheduling:a blockchain-based node selection approach on sapiens chain[C]//Proceedings of 2019 IEEE International Conference on Big Data and Smart Computing.Washington D.C., USA:IEEE Press, 2019:1-7. [6] ALI-ELDIN A, TORDSSON J, ELMROTH E.An adaptive hybrid elasticity controller for cloud infrastructures[C]//Proceedings of 2012 IEEE Network Operations and Management Symposium.Washington D.C., USA:IEEE Press, 2012:204-212. [7] TRAN G P C, CHEN Y, KANG D I, et al.Automated demand-based vertical elasticity for heterogeneous real-time workloads[C]//Proceedings of the 9th International Conference on Cloud Computing.Washington D.C., USA:IEEE Press, 2016:831-834. [8] JAYAKUMAR V K, LEE J, KIM I K, et al.A self-optimized generic workload prediction framework for cloud computing[C]//Proceedings of 2020 IEEE International Parallel and Distributed Processing Symposium.Washington D.C., USA:IEEE Press, 2020:779-788. [9] MICHELLE Y, GAREGRAT N, MOHAN S.Towards a resource aware scheduler in Hadoop[C]//Proceedings of the 7th IEEE International Conference on Web Services.Washington D.C., USA:IEEE Press, 2009:102-109. [10] CARUANA G, LI M Z, QI M, et al.gSched:a resource aware Hadoop scheduler for heterogeneous cloud computing environments[J].Concurrency and Computation:Practice & Experience, 2017, 29(20):1-7. [11] NAIK N S, NEGI A, BAPU T B R, et al.A data locality based scheduler to enhance MapReduce performance in heterogeneous environments[J].Future Generation Computer Systems, 2019, 90:423-434. [12] XU X L, CAO L L, WANG X H.Adaptive task scheduling strategy based on dynamic workload adjustment for heterogeneous Hadoop clusters[J].IEEE Systems Journal, 2017, 10(2):471-482. [13] 廉华, 刘瑜.基于YARN资源调度器的MapReduce作业数调节方法[J].计算机系统应用, 2020, 29(3):218-222. LIAN H, LIU Y.Number adjustment method of MapReduce jobs based on YARN resource scheduler[J].Computer Systems & Applications, 2020, 29(3):218-222.(in Chinese) [14] 杨志伟, 郑烇, 王嵩, 等.异构Spark集群下自适应任务调度策略[J].计算机工程, 2016, 42(1):31-35, 40. YANG Z W, ZHENG Q, WANG S, et al.Adaptive task scheduling strategy for heterogeneous spark cluster[J].Computer Engineering, 2016, 42(1):31-35, 40.(in Chinese) [15] 胡亚红, 盛夏, 毛家发.资源不均衡Spark环境任务调度优化算法研究[J].计算机工程与科学, 2020, 42(2):203-209. HU Y H, SHENG X, MAO J F.Task scheduling optimization in Spark environment with unbalanced resources[J].Computer Engineering & Science, 2020, 42(2):203-209.(in Chinese) [16] 郝志峰, 黄泽林, 蔡瑞初, 等.基于YARN的分布式资源动态调度和协同分配系统[J].计算机工程, 2021, 47(2):226-232. HAO Z F, HUANG Z L, CAI R C, et al.Dynamic resource scheduling and collaborative allocation system based on YARN[J].Computer Engineering, 2021, 47(2):226-232.(in Chinese) [17] 何贞贞, 于炯, 李梓杨, 等.基于Flink的任务调度策略[J].计算机工程与设计, 2020, 41(5):1280-1287. HE Z Z, YU J, LI Z Y, et al.Task scheduling strategy based on Flink environment[J].Computer Engineering and Design, 2020, 41(5):1280-1287.(in Chinese) [18] 庆骁.面向FLINK流处理框架的容错策略优化研究[D].哈尔滨:哈尔滨工业大学, 2019. QING X.Research on fault-tolerant strategy optimization for FLINK stream processing framework[D].Harbin:Harbin Institute of Technology, 2019.(in Chinese) [19] 赵娟, 程国钟.基于Hadoop、Storm、Samza、Spark及Flink大数据处理框架的比较研究[J].信息系统工程, 2017(6):117, 119. ZHAO J, CHEN G Z.Comparison of big data processing frameworks based on Hadoop, Storm, Samza, Spark and Flink[J].China CIO News, 2017(6):117, 119.(in Chinese) [20] TOLIOPOULOS T, GOUNARIS A.Adaptive distributed partitioning in Apache Flink[C]//Proceedings of the 36th International Conference on Data Engineering Workshops.Washington D.C., USA:IEEE Press, 2020:127-132. [21] Ganglia[EB/OL].[2020-10-19].http://ganglia.info/. [22] AJILA T, MAJUMDAR S.Data driven priority scheduling on a spark streaming system[C]//Proceedings of the 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.Washington D.C., USA:IEEE Press, 2019:561-568. [23] KAMSKY A.Adapting TPC-C benchmark to measure performance of multi-document transactions in MongoDB[J].Proceedings of the VLDB Endowment, 2019, 12(12):2254-2262. |