{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:33:10Z","timestamp":1774121590839,"version":"3.50.1"},"reference-count":95,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Hadoop\u00a0is\u00a0a\u00a0framework for\u00a0storing\u00a0and\u00a0processing\u00a0huge\u00a0volumes\u00a0of\u00a0data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.<\/jats:p>","DOI":"10.1186\/s13677-023-00520-9","type":"journal-article","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T17:02:00Z","timestamp":1696957320000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["MapReduce scheduling algorithms in Hadoop: a systematic study"],"prefix":"10.1186","volume":"12","author":[{"given":"Soudabeh","family":"Hedayati","sequence":"first","affiliation":[]},{"given":"Neda","family":"Maleki","sequence":"additional","affiliation":[]},{"given":"Tobias","family":"Olsson","sequence":"additional","affiliation":[]},{"given":"Fredrik","family":"Ahlgren","sequence":"additional","affiliation":[]},{"given":"Mahdi","family":"Seyednezhad","sequence":"additional","affiliation":[]},{"given":"Kamal","family":"Berahmand","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,10]]},"reference":[{"key":"520_CR1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jpdc.2014.08.003","volume":"79","author":"MD Assun\u00e7\u00e3o","year":"2015","unstructured":"Assun\u00e7\u00e3o MD et al (2015) Big Data computing and clouds: Trends and future directions. J Parallel Distributed Comput 79:3\u201315","journal-title":"J Parallel Distributed Comput"},{"key":"520_CR2","doi-asserted-by":"crossref","unstructured":"Thusoo A et al (2010) \"Hive-a petabyte scale data warehouse using hadoop.\" 2010 IEEE 26th international conference on data engineering (ICDE 2010). IEEE","DOI":"10.1109\/ICDE.2010.5447738"},{"key":"520_CR3","doi-asserted-by":"crossref","unstructured":"Deshai N et al (2019) \"Big data Hadoop MapReduce job scheduling: A short survey.\" Information Systems Design and Intelligent Applications: Proceedings of Fifth International Conference INDIA 2018 Volume 1. Springer, Singapore","DOI":"10.1007\/978-981-13-3329-3_33"},{"key":"520_CR4","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/ACCESS.2014.2332453","volume":"2","author":"H Hu","year":"2014","unstructured":"Hu H et al (2014) Toward scalable systems for big data analytics: A technology tutorial. IEEE Access 2:652\u2013687","journal-title":"IEEE Access"},{"key":"520_CR5","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.ins.2014.01.015","volume":"275","author":"CP Chen","year":"2014","unstructured":"Chen CP, Zhang C-Y (2014) Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Inf Sci 275:314\u2013347","journal-title":"Inf Sci"},{"key":"520_CR6","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11036-013-0489-0","volume":"19","author":"M Chen","year":"2014","unstructured":"Chen M, Mao S, Liu Y (2014) Big data: A survey. Mobile Netw Appl 19:171\u2013209","journal-title":"Mobile Netw Appl"},{"issue":"1","key":"520_CR7","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/1327452.1327492","volume":"51","author":"J Dean","year":"2008","unstructured":"Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107\u2013113","journal-title":"Commun ACM"},{"key":"520_CR8","doi-asserted-by":"crossref","unstructured":"Bakni N-E and I Assayad (2021) Survey on improving the performance of MapReduce in Hadoop. In: Proceedings of the 4th International Conference on Networking, Information Systems & Security","DOI":"10.1145\/3454127.3456617"},{"key":"520_CR9","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.future.2017.09.063","volume":"87","author":"B Zhang","year":"2018","unstructured":"Zhang B, Wang X, Zheng Z (2018) The optimization for recurring queries in big data analysis system with MapReduce. Futur Gener Comput Syst 87:549\u2013556","journal-title":"Futur Gener Comput Syst"},{"key":"520_CR10","unstructured":"Kashgarani H, Kotthoff L (2021) \"Is algorithm selection worth it? Comparing selecting single algorithms and parallel execution.\" AAAI Workshop on\u00a0Meta-Learning and MetaDL Challenge. PMLR"},{"issue":"9","key":"520_CR11","first-page":"308","volume":"2","author":"SR Pakize","year":"2014","unstructured":"Pakize SR (2014) A comprehensive view of Hadoop MapReduce scheduling algorithms. Int J Comput Netw Commun Secur 2(9):308\u2013317","journal-title":"Int J Comput Netw Commun Secur"},{"key":"520_CR12","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.future.2022.04.035","volume":"135","author":"Y Kang","year":"2022","unstructured":"Kang Y, Pan L, Liu S (2022) Job scheduling for big data analytical applications in clouds: A taxonomy study. Futur Gener Comput Syst 135:129\u2013145","journal-title":"Futur Gener Comput Syst"},{"issue":"10","key":"520_CR13","first-page":"272","volume":"3","author":"HS Bhosale","year":"2014","unstructured":"Bhosale HS, Gadekar DP (2014) Big data processing using hadoop: survey on scheduling. Int J Sci Res 3(10):272\u2013277","journal-title":"Int J Sci Res"},{"key":"520_CR14","doi-asserted-by":"crossref","unstructured":"Shvachko K et al (2010) \"The hadoop distributed file system.\" 2010 IEEE 26th symposium on mass storage systems and technologies (MSST). Ieee","DOI":"10.1109\/MSST.2010.5496972"},{"key":"520_CR15","doi-asserted-by":"crossref","unstructured":"Khushboo K, Gupta N (2021) \"Analysis of hadoop MapReduce scheduling in heterogeneous environment.\" Ain Shams Engineering Journal 12(1):1101\u20131110","DOI":"10.1016\/j.asej.2020.06.009"},{"key":"520_CR16","unstructured":"White T\u00a0(2012) Hadoop: The definitive guide. \" O'Reilly Media, Inc.\""},{"key":"520_CR17","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.jpdc.2017.11.001","volume":"118","author":"Z Lu","year":"2018","unstructured":"Lu Z et al (2018) IoTDeM: An IoT big data-oriented MapReduce performance prediction extended model in multiple edge clouds. J Parallel Distributed Comput 118:316\u2013327","journal-title":"J Parallel Distributed Comput"},{"issue":"1","key":"520_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-016-0051-6","volume":"3","author":"R Singh","year":"2016","unstructured":"Singh R, Kaur PJ (2016) Analyzing performance of Apache Tez and MapReduce with hadoop multinode cluster on Amazon cloud. J Big Data 3(1):1\u201310","journal-title":"J Big Data"},{"issue":"1","key":"520_CR19","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/TSC.2015.2453973","volume":"9","author":"H Wang","year":"2015","unstructured":"Wang H et al (2015) BeTL: MapReduce checkpoint tactics beneath the task level. IEEE Trans Serv Comput 9(1):84\u201395","journal-title":"IEEE Trans Serv Comput"},{"key":"520_CR20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.infsof.2015.03.007","volume":"64","author":"K Petersen","year":"2015","unstructured":"Petersen K, Vakkalanka S, Kuzniarz L (2015) Guidelines for conducting systematic mapping studies in software engineering: An update. Inf Softw Technol 64:1\u201318","journal-title":"Inf Softw Technol"},{"key":"520_CR21","unstructured":"Cruz-Benito J (2016) Systematic literature review & mapping"},{"key":"520_CR22","doi-asserted-by":"crossref","unstructured":"Lu Q et al (2015) \"MapReduce job optimization: a mapping study.\" 2015 International Conference on Cloud Computing and Big Data (CCBD). IEEE","DOI":"10.1109\/CCBD.2015.33"},{"issue":"4","key":"520_CR23","doi-asserted-by":"crossref","first-page":"3381","DOI":"10.1007\/s10586-021-03339-8","volume":"24","author":"R Ghazali","year":"2021","unstructured":"Ghazali R et al (2021) A classification of Hadoop job schedulers based on performance optimization approaches. Clust Comput 24(4):3381\u20133403","journal-title":"Clust Comput"},{"issue":"7","key":"520_CR24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5539\/mas.v13n7p38","volume":"13","author":"AA Abdallat","year":"2019","unstructured":"Abdallat AA, Alahmad AI, AlWidian JA (2019) Hadoop mapreduce job scheduling algorithms survey and use cases. Mod Appl Sci 13(7):1\u201338","journal-title":"Mod Appl Sci"},{"key":"520_CR25","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.1007\/s11227-018-2719-5","volume":"76","author":"IAT Hashem","year":"2020","unstructured":"Hashem IAT et al (2020) MapReduce scheduling algorithms: a review. J Supercomput 76:4915\u20134945","journal-title":"J Supercomput"},{"key":"520_CR26","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.jss.2017.09.001","volume":"134","author":"M Soualhia","year":"2017","unstructured":"Soualhia M, Khomh F, Tahar S (2017) Task scheduling in big data platforms: a systematic literature review. J Syst Softw 134:170\u2013189","journal-title":"J Syst Softw"},{"key":"520_CR27","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s10723-017-9408-0","volume":"15","author":"SN Khezr","year":"2017","unstructured":"Khezr SN, Navimipour NJ (2017) MapReduce and its applications, challenges, and architecture: a comprehensive review and directions for future research. J Grid Comput 15:295\u2013321","journal-title":"J Grid Comput"},{"issue":"3","key":"520_CR28","first-page":"35","volume":"16","author":"M Senthilkumar","year":"2016","unstructured":"Senthilkumar M, Ilango P (2016) A survey on job scheduling in big data. Cybern Inf Technol 16(3):35\u201351","journal-title":"Cybern Inf Technol"},{"key":"520_CR29","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s11192-016-1945-y","volume":"109","author":"IAT Hashem","year":"2016","unstructured":"Hashem IAT et al (2016) MapReduce: Review and open challenges. Scientometrics 109:389\u2013422","journal-title":"Scientometrics"},{"key":"520_CR30","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1007\/s10766-015-0395-0","volume":"44","author":"R Li","year":"2016","unstructured":"Li R et al (2016) MapReduce parallel programming model: a state-of-the-art survey. Int J Parallel Prog 44:832\u2013866","journal-title":"Int J Parallel Prog"},{"issue":"3","key":"520_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2693315","volume":"47","author":"N Tiwari","year":"2015","unstructured":"Tiwari N et al (2015) Classification framework of MapReduce scheduling algorithms. ACM Comput Surveys (CSUR) 47(3):1\u201338","journal-title":"ACM Comput Surveys (CSUR)"},{"key":"520_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jnca.2014.07.022","volume":"46","author":"I Polato","year":"2014","unstructured":"Polato I et al (2014) A comprehensive view of Hadoop research\u2014A systematic literature review. J Netw Comput Appl 46:1\u201325","journal-title":"J Netw Comput Appl"},{"key":"520_CR33","doi-asserted-by":"crossref","unstructured":"Gao Y, Zhang K (2022) \"Deadline-aware preemptive job scheduling in hadoop yarn clusters.\" 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE","DOI":"10.1109\/CSCWD54268.2022.9776126"},{"issue":"4","key":"520_CR34","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1109\/TPDS.2018.2873373","volume":"30","author":"D Cheng","year":"2018","unstructured":"Cheng D et al (2018) Deadline-aware MapReduce job scheduling with dynamic resource availability. IEEE Trans Parallel Distrib Syst 30(4):814\u2013826","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"520_CR35","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.jss.2015.11.001","volume":"112","author":"Y-C Kao","year":"2016","unstructured":"Kao Y-C, Chen Y-S (2016) Data-locality-aware mapreduce real-time scheduling framework. J Syst Softw 112:65\u201377","journal-title":"J Syst Softw"},{"key":"520_CR36","doi-asserted-by":"crossref","unstructured":"Verma A et al (2012) \"Deadline-based workload management for MapReduce environments: Pieces of the performance puzzle.\" 2012 IEEE Network Operations and Management Symposium. IEEE","DOI":"10.1109\/NOMS.2012.6212006"},{"key":"520_CR37","doi-asserted-by":"crossref","unstructured":"Phan LT et al (2011) \"An empirical analysis of scheduling techniques for real-time cloud-based data processing.\" 2011 IEEE International Conference on Service-Oriented Computing and Applications (SOCA). IEEE","DOI":"10.1109\/SOCA.2011.6166240"},{"key":"520_CR38","doi-asserted-by":"crossref","unstructured":"Kc K, Anyanwu K (2010) \"Scheduling hadoop jobs to meet deadlines.\" 2010 IEEE Second International Conference on Cloud Computing Technology and Science. IEEE","DOI":"10.1109\/CloudCom.2010.97"},{"issue":"2","key":"520_CR39","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s11227-014-1115-z","volume":"69","author":"F Teng","year":"2014","unstructured":"Teng F et al (2014) A novel real-time scheduling algorithm and performance analysis of a MapReduce-based cloud. J Supercomput 69(2):739\u2013765","journal-title":"J Supercomput"},{"key":"520_CR40","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1007\/s11704-014-4138-y","volume":"9","author":"X Wang","year":"2015","unstructured":"Wang X et al (2015) SAMES: deadline-constraint scheduling in MapReduce. Front Comp Sci 9:128\u2013141","journal-title":"Front Comp Sci"},{"key":"520_CR41","doi-asserted-by":"crossref","unstructured":"Dong X, Wang Y, Liao H (2011) \"Scheduling mixed real-time and non-real-time applications in mapreduce environment.\" 2011 IEEE 17th International Conference on Parallel and Distributed Systems. IEEE","DOI":"10.1109\/ICPADS.2011.115"},{"key":"520_CR42","doi-asserted-by":"crossref","unstructured":"Verma A, Cherkasova L, Campbell\u00a0RH (2011) \"Resource provisioning framework for mapreduce jobs with performance goals.\" Middleware 2011: ACM\/IFIP\/USENIX 12th International Middleware Conference, Lisbon, Portugal, December 12-16, 2011. Proceedings 12. Springer Berlin Heidelberg","DOI":"10.1007\/978-3-642-25821-3_9"},{"key":"520_CR43","doi-asserted-by":"crossref","unstructured":"Jabbari A et al (2021) \"A Cost-Efficient Resource Provisioning and Scheduling Approach for Deadline-Sensitive MapReduce Computations in Cloud Environment.\" 2021 IEEE 14th International Conference on Cloud Computing (CLOUD). IEEE","DOI":"10.1109\/CLOUD53861.2021.00078"},{"key":"520_CR44","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.cie.2018.02.006","volume":"117","author":"Y Shao","year":"2018","unstructured":"Shao Y et al (2018) Efficient jobs scheduling approach for big data applications. Comput Ind Eng 117:249\u2013261","journal-title":"Comput Ind Eng"},{"key":"520_CR45","doi-asserted-by":"crossref","first-page":"6963","DOI":"10.1007\/s10586-018-1981-x","volume":"22","author":"J-W Lin","year":"2019","unstructured":"Lin J-W, Arul JM, Lin C-Y (2019) Joint deadline-constrained and influence-aware design for allocating MapReduce jobs in cloud computing systems. Clust Comput 22:6963\u20136976","journal-title":"Clust Comput"},{"issue":"1","key":"520_CR46","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/TCC.2015.2474403","volume":"6","author":"C-H Chen","year":"2015","unstructured":"Chen C-H, Lin J-W, Kuo S-Y (2015) MapReduce scheduling for deadline-constrained jobs in heterogeneous cloud computing systems. IEEE Trans Cloud Comput 6(1):127\u2013140","journal-title":"IEEE Trans Cloud Comput"},{"key":"520_CR47","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s10586-012-0236-5","volume":"16","author":"Z Tang","year":"2013","unstructured":"Tang Z et al (2013) A MapReduce task scheduling algorithm for deadline constraints. Clust Comput 16:651\u2013662","journal-title":"Clust Comput"},{"key":"520_CR48","doi-asserted-by":"crossref","unstructured":"Verma AL, Cherkasova, and RH Campbell (2011) Aria: automatic resource inference and allocation for mapreduce environments. In: Proceedings of the 8th ACM international conference on Autonomic computing","DOI":"10.1145\/1998582.1998637"},{"issue":"2","key":"520_CR49","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1109\/TNSM.2012.122112.110163","volume":"10","author":"J Polo","year":"2013","unstructured":"Polo J et al (2013) Deadline-based MapReduce workload management. IEEE Trans Netw Serv Manage 10(2):231\u2013244","journal-title":"IEEE Trans Netw Serv Manage"},{"key":"520_CR50","doi-asserted-by":"crossref","DOI":"10.1016\/j.robot.2022.104228","volume":"157","author":"K Kalia","year":"2022","unstructured":"Kalia K et al (2022) Improving MapReduce heterogeneous performance using KNN fair share scheduling. Robot Auton Syst 157:104228","journal-title":"Robot Auton Syst"},{"key":"520_CR51","doi-asserted-by":"crossref","unstructured":"Li Y, Hei X\u00a0 (2022) \"Performance optimization of computing task scheduling based on the Hadoop big data platform.\" Neural Computing and Applications pp. 1-12","DOI":"10.1007\/s00521-022-08114-3"},{"issue":"10","key":"520_CR52","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1109\/TPDS.2020.2992073","volume":"31","author":"Z Fu","year":"2020","unstructured":"Fu Z et al (2020) An optimal locality-aware task scheduling algorithm based on bipartite graph modelling for spark applications. IEEE Trans Parallel Distrib Syst 31(10):2406\u20132420","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"520_CR53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0253-9","volume":"6","author":"A Gandomi","year":"2019","unstructured":"Gandomi A et al (2019) HybSMRP: a hybrid scheduling algorithm in Hadoop MapReduce framework. J Big Data 6:1\u201316","journal-title":"J Big Data"},{"key":"520_CR54","doi-asserted-by":"crossref","unstructured":"He C, Lu Y, Swanson D (2011) \"Matchmaking: A new mapreduce scheduling technique.\" 2011 IEEE Third International Conference on Cloud Computing Technology and Science. IEEE","DOI":"10.1109\/CloudCom.2011.16"},{"key":"520_CR55","doi-asserted-by":"crossref","unstructured":"Ibrahim S et al (2012) \"Maestro: Replica-aware map scheduling for mapreduce.\" 2012 12th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012). IEEE","DOI":"10.1109\/CCGrid.2012.122"},{"key":"520_CR56","doi-asserted-by":"crossref","unstructured":"Zhang X et al (2011) \"An effective data locality aware task scheduling method for MapReduce framework in heterogeneous environments.\" 2011 International Conference on Cloud and Service Computing. IEEE","DOI":"10.1109\/CSC.2011.6138527"},{"key":"520_CR57","doi-asserted-by":"crossref","unstructured":"Zhang X et al (2011) \"Improving data locality of mapreduce by scheduling in homogeneous computing environments.\" 2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications. IEEE","DOI":"10.1109\/ISPA.2011.14"},{"key":"520_CR58","doi-asserted-by":"crossref","unstructured":"Zaharia M et al (2010) Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling. In: Proceedings of the 5th European conference on Computer systems","DOI":"10.1145\/1755913.1755940"},{"issue":"9","key":"520_CR59","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1109\/TPDS.2021.3134247","volume":"33","author":"X Tang","year":"2021","unstructured":"Tang X et al (2021) Cost-efficient workflow scheduling algorithm for applications with deadline constraint on heterogeneous clouds. IEEE Trans Parallel Distrib Syst 33(9):2079\u20132092","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"6","key":"520_CR60","first-page":"3178","volume":"34","author":"V Seethalakshmi","year":"2022","unstructured":"Seethalakshmi V, Govindasamy V, Akila V (2022) Real-coded multi-objective genetic algorithm with effective queuing model for efficient job scheduling in heterogeneous Hadoop environment. J King Saud Univ-Computer Inf Sci 34(6):3178\u20133190","journal-title":"J King Saud Univ-Computer Inf Sci"},{"issue":"4","key":"520_CR61","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1007\/s42979-021-00638-0","volume":"2","author":"D Vinutha","year":"2021","unstructured":"Vinutha D, Raju G (2021) Budget constraint scheduler for big data using Hadoop MapReduce. SN Comput Sci 2(4):250","journal-title":"SN Comput Sci"},{"key":"520_CR62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11227-020-03256-4","volume":"77","author":"AK Javanmardi","year":"2021","unstructured":"Javanmardi AK et al (2021) A unit-based, cost-efficient scheduler for heterogeneous Hadoop systems. J Supercomput 77:1\u201322","journal-title":"J Supercomput"},{"key":"520_CR63","doi-asserted-by":"crossref","unstructured":"Rashmi S, Basu A (2016) \"Deadline constrained Cost Effective Workflow scheduler for Hadoop clusters in cloud datacenter.\" 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE","DOI":"10.1109\/CSITSS.2016.7779395"},{"key":"520_CR64","doi-asserted-by":"crossref","unstructured":"Zacheilas N, Kalogeraki V (2016) \"Chess: Cost-effective scheduling across multiple heterogeneous mapreduce clusters.\" 2016 IEEE international conference on autonomic computing (ICAC). IEEE, Berahmand, [10\/4\/2023 8:36 PM]","DOI":"10.1109\/ICAC.2016.58"},{"issue":"5","key":"520_CR65","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1109\/TPDS.2014.2320498","volume":"26","author":"B Palanisamy","year":"2014","unstructured":"Palanisamy B, Singh A, Liu L (2014) Cost-effective resource provisioning for mapreduce in a cloud. IEEE Trans Parallel Distrib Syst 26(5):1265\u20131279","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"6","key":"520_CR66","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1109\/TPDS.2013.297","volume":"25","author":"K Chen","year":"2013","unstructured":"Chen K et al (2013) CRESP: Towards optimal resource provisioning for MapReduce computing in public clouds. IEEE Trans Parallel Distrib Syst 25(6):1403\u20131412","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"2","key":"520_CR67","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1007\/s13369-022-06963-7","volume":"48","author":"S Aarthee","year":"2023","unstructured":"Aarthee S, Prabakaran R (2023) Energy-aware heuristic scheduling using bin packing mapreduce scheduler for heterogeneous workloads performance in big data. Arab J Sci Eng 48(2):1891\u20131905","journal-title":"Arab J Sci Eng"},{"key":"520_CR68","doi-asserted-by":"crossref","first-page":"55842","DOI":"10.1109\/ACCESS.2022.3176729","volume":"10","author":"R Jeyaraj","year":"2022","unstructured":"Jeyaraj R, Paul A (2022) Optimizing MapReduce task scheduling on virtualized heterogeneous environments using ant colony optimization. IEEE Access 10:55842\u201355855","journal-title":"IEEE Access"},{"issue":"2","key":"520_CR69","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/TCC.2014.2379096","volume":"3","author":"Q Zhang","year":"2015","unstructured":"Zhang Q et al (2015) PRISM: Fine-grained resource-aware scheduling for MapReduce. IEEE Trans Cloud Comput 3(2):182\u2013194","journal-title":"IEEE Trans Cloud Comput"},{"key":"520_CR70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.future.2014.01.002","volume":"36","author":"A Rasooli","year":"2014","unstructured":"Rasooli A, Down DG (2014) COSHH: A classification and optimization based scheduler for heterogeneous Hadoop systems. Futur Gener Comput Syst 36:1\u201315","journal-title":"Futur Gener Comput Syst"},{"key":"520_CR71","unstructured":"Polo J et al (2011) \"Resource-aware adaptive scheduling for mapreduce clusters.\" Middleware 2011: ACM\/IFIP\/USENIX 12th International Middleware Conference, Lisbon, Portugal, December 12-16, 2011. Proceedings 12. Springer, Berlin Heidelberg"},{"key":"520_CR72","doi-asserted-by":"crossref","unstructured":"Sharma B et al (2012) \"Mrorchestrator: A fine-grained resource orchestration framework for mapreduce clusters.\" 2012 IEEE Fifth International Conference on Cloud Computing. IEEE","DOI":"10.1109\/CLOUD.2012.37"},{"issue":"1","key":"520_CR73","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TCC.2015.2396056","volume":"5","author":"M Pastorelli","year":"2015","unstructured":"Pastorelli M et al (2015) HFSP: bringing size-based scheduling to hadoop. IEEE Trans Cloud Comput 5(1):43\u201356","journal-title":"IEEE Trans Cloud Comput"},{"key":"520_CR74","doi-asserted-by":"crossref","unstructured":"Tian F, Chen K (2011) \"Towards optimal resource provisioning for running mapreduce programs in public clouds.\" 2011 IEEE 4th International Conference on Cloud Computing. IEEE","DOI":"10.1109\/CLOUD.2011.14"},{"key":"520_CR75","unstructured":"Ghoneem M, Kulkarni L (2017) \"An adaptive MapReduce scheduler for scalable heterogeneous systems.\" Proceedings of the International Conference on Data Engineering and Communication Technology: ICDECT 2016, Volume 2. Springer Singapore, Berahmand, [10\/4\/2023 8:40 PM]"},{"issue":"27","key":"520_CR76","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.7316","volume":"34","author":"P Varalakshmi","year":"2022","unstructured":"Varalakshmi P, Subbiah S (2022) Optimized scheduling of multi-user Map-Reduce jobs in heterogeneous environment. Concurr Comput: Pract Exp 34(27):e7316","journal-title":"Concurr Comput: Pract Exp"},{"key":"520_CR77","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2020.102944","volume":"176","author":"N Maleki","year":"2021","unstructured":"Maleki N, Rahmani AM, Conti M (2021) SPO: a secure and performance-aware optimization for MapReduce scheduling. J Netw Comput Appl 176:102944","journal-title":"J Netw Comput Appl"},{"key":"520_CR78","first-page":"1","volume":"10","author":"N Maleki","year":"2020","unstructured":"Maleki N et al (2020) TMaR: a two-stage MapReduce scheduler for heterogeneous environments. HCIS 10:1\u201326","journal-title":"HCIS"},{"key":"520_CR79","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.future.2016.07.012","volume":"67","author":"Y Jiang","year":"2017","unstructured":"Jiang Y et al (2017) Makespan minimization for MapReduce systems with different servers. Futur Gener Comput Syst 67:13\u201321","journal-title":"Futur Gener Comput Syst"},{"issue":"5","key":"520_CR80","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/TDSC.2013.14","volume":"10","author":"A Verma","year":"2013","unstructured":"Verma A, Cherkasova L, Campbell RH (2013) Orchestrating an ensemble of MapReduce jobs for minimizing their makespan. IEEE Trans Dependable Secure Comput 10(5):314\u2013327","journal-title":"IEEE Trans Dependable Secure Comput"},{"issue":"2","key":"520_CR81","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1109\/TCC.2015.2415802","volume":"5","author":"Y Yao","year":"2015","unstructured":"Yao Y et al (2015) Self-adjusting slot configurations for homogeneous and heterogeneous hadoop clusters. IEEE Trans Cloud Comput 5(2):344\u2013357","journal-title":"IEEE Trans Cloud Comput"},{"key":"520_CR82","doi-asserted-by":"crossref","unstructured":"Zheng H, Wan Z, Wu J (2016) \"Optimizing MapReduce framework through joint scheduling of overlapping phases.\" 2016 25th International Conference on Computer Communication and Networks (ICCCN). IEEE","DOI":"10.1109\/ICCCN.2016.7568555"},{"key":"520_CR83","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.1007\/s11227-014-1335-2","volume":"72","author":"Z Tang","year":"2016","unstructured":"Tang Z et al (2016) An optimized MapReduce workflow scheduling algorithm for heterogeneous computing. J Supercomput 72:2059\u20132079","journal-title":"J Supercomput"},{"issue":"1","key":"520_CR84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13677-022-00322-5","volume":"11","author":"R Ghazali","year":"2022","unstructured":"Ghazali R et al (2022) CLQLMRS: improving cache locality in MapReduce job scheduling using Q-learning. J Cloud Comput 11(1):1\u201317","journal-title":"J Cloud Comput"},{"key":"520_CR85","doi-asserted-by":"crossref","unstructured":"Naik NS, Negi A (2017) \"A learning-based mapreduce scheduler in heterogeneous environments.\" 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE","DOI":"10.1109\/ICACCI.2017.8126142"},{"key":"520_CR86","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.procs.2015.04.080","volume":"50","author":"NS Naik","year":"2015","unstructured":"Naik NS, Negi A, Sastry V (2015) Performance improvement of MapReduce framework in heterogeneous context using reinforcement learning. Proc Comput Sci 50:169\u2013175","journal-title":"Proc Comput Sci"},{"key":"520_CR87","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.cose.2017.12.014","volume":"76","author":"M Varga","year":"2018","unstructured":"Varga M, Petrescu-Nita A, Pop F (2018) Deadline scheduling algorithm for sustainable computing in Hadoop environment. Comput Secur 76:354\u2013366","journal-title":"Comput Secur"},{"key":"520_CR88","doi-asserted-by":"crossref","unstructured":"He C, Lu Y, Swanson D (2013) Real-time scheduling in mapreduce clusters. In: 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing. IEEE","DOI":"10.1109\/HPCC.and.EUC.2013.216"},{"key":"520_CR89","doi-asserted-by":"crossref","unstructured":"Gautam JV et al (2015) \"A survey on job scheduling algorithms in big data processing.\" 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE","DOI":"10.1109\/ICECCT.2015.7226035"},{"key":"520_CR90","doi-asserted-by":"crossref","unstructured":"Chen CH, Lin JW, Kuo\u00a0SY (2014) \"Deadline-constrained MapReduce scheduling based on graph modelling.\" 2014 IEEE 7th International Conference on Cloud Computing. IEEE","DOI":"10.1109\/CLOUD.2014.63"},{"issue":"4","key":"520_CR91","first-page":"1226","volume":"4","author":"PP Nimbalkar","year":"2015","unstructured":"Nimbalkar PP, Gadekar DP (2015) Survey on scheduling algorithm in mapreduce framework. IJSETR 4(4):1226\u20131230","journal-title":"IJSETR"},{"key":"520_CR92","doi-asserted-by":"crossref","unstructured":"Singh N, Agrawal S (2015) A review of research on MapReduce scheduling algorithms in Hadoop.\" International Conference on Computing, Communication & Automation. IEEE","DOI":"10.1109\/CCAA.2015.7148451"},{"issue":"2","key":"520_CR93","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/TPDS.2015.2405552","volume":"27","author":"M Khan","year":"2015","unstructured":"Khan M et al (2015) Hadoop performance modeling for job estimation and resource provisioning. IEEE Trans Parallel Distrib Syst 27(2):441\u2013454","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"520_CR94","doi-asserted-by":"crossref","unstructured":"Mohamed E, Hong Z (2016) \"Hadoop-MapReduce job scheduling algorithms survey.\" 2016 7th International Conference on Cloud Computing and Big Data (CCBD). IEEE","DOI":"10.1109\/CCBD.2016.054"},{"key":"520_CR95","unstructured":"Mittal R and H Kaur A Survey on Data Placement and Workload Scheduling Algorithms in Heterogeneous Network for Hadoop. Int J Comput Appl 975:8887"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-023-00520-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-023-00520-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-023-00520-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T14:48:00Z","timestamp":1700318880000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-023-00520-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,10]]},"references-count":95,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["520"],"URL":"https:\/\/doi.org\/10.1186\/s13677-023-00520-9","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,10]]},"assertion":[{"value":"31 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"143"}}