Map-Balance-Reduce: An improved parallel programming model for load balancing of MapReduce
With the advent of the era of big data, the demand of massive data processing applications is also growing. Currently, MapReduce is the most commonly used data processing programming model. However, in some data processing cases, it has some defects. MapReduce programming based on key/value pairs, m...
Saved in:
| Published in: | Future generation computer systems Vol. 105; pp. 993 - 1001 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.04.2020
|
| Subjects: | |
| ISSN: | 0167-739X |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | With the advent of the era of big data, the demand of massive data processing applications is also growing. Currently, MapReduce is the most commonly used data processing programming model. However, in some data processing cases, it has some defects. MapReduce programming based on key/value pairs, matches the output of the Map tasks that will be transported to Reduce task nodes. The data with same key can only be processed by a Reduce node. If the data corresponding to a particular key or several keys accounts for most of all data, the Reduce node task will generate unbalanced load. In view of this defect, this paper proposes a new parallel programming model—Map-Balance-Reduce (MBR) programming model. It runs on our improved Hadoop framework and can effectively process the special data with unbalanced keys. This paper is based on two different scheduling, the processing and self-adaption scheduling. These two scheduling are designed to achieve MBR programming model. The actual testing results show that compared with MapReduce programming model, the MBR programming model under Hadoop can achieve the improvement of 9.7% to 17.6% in efficiency when testing data distributes unevenly. Furthermore, when testing conventional even-distributed data, it will only bring 1.02% time cost.
•A new programming of “Map-Balance-Reduce” is proposed.•New scheduling algorithms of preprocessing scheduling and self-adaption scheduling are designed.•The proposed work can efficiently process unevenly distributed data for MapReduce. |
|---|---|
| AbstractList | With the advent of the era of big data, the demand of massive data processing applications is also growing. Currently, MapReduce is the most commonly used data processing programming model. However, in some data processing cases, it has some defects. MapReduce programming based on key/value pairs, matches the output of the Map tasks that will be transported to Reduce task nodes. The data with same key can only be processed by a Reduce node. If the data corresponding to a particular key or several keys accounts for most of all data, the Reduce node task will generate unbalanced load. In view of this defect, this paper proposes a new parallel programming model—Map-Balance-Reduce (MBR) programming model. It runs on our improved Hadoop framework and can effectively process the special data with unbalanced keys. This paper is based on two different scheduling, the processing and self-adaption scheduling. These two scheduling are designed to achieve MBR programming model. The actual testing results show that compared with MapReduce programming model, the MBR programming model under Hadoop can achieve the improvement of 9.7% to 17.6% in efficiency when testing data distributes unevenly. Furthermore, when testing conventional even-distributed data, it will only bring 1.02% time cost.
•A new programming of “Map-Balance-Reduce” is proposed.•New scheduling algorithms of preprocessing scheduling and self-adaption scheduling are designed.•The proposed work can efficiently process unevenly distributed data for MapReduce. |
| Author | Pan, Jian Chen, Wei Wang, Lizhe Zhang, Peng Li, Jianjiang Liu, Yajun |
| Author_xml | – sequence: 1 givenname: Jianjiang surname: Li fullname: Li, Jianjiang organization: Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, 100083, PR China – sequence: 2 givenname: Yajun surname: Liu fullname: Liu, Yajun organization: Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, 100083, PR China – sequence: 3 givenname: Jian surname: Pan fullname: Pan, Jian organization: Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, 100083, PR China – sequence: 4 givenname: Peng surname: Zhang fullname: Zhang, Peng organization: Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, 100083, PR China – sequence: 5 givenname: Wei surname: Chen fullname: Chen, Wei organization: Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, 100083, PR China – sequence: 6 givenname: Lizhe orcidid: 0000-0003-2766-0845 surname: Wang fullname: Wang, Lizhe email: lizhe.wang@gmail.com organization: School of Computer Science, China University of Geosciences, Wuhan, 430074, PR China |
| BookMark | eNqFkM1KAzEURrOoYKu-gYu8wIxJM03aLoRa_IOKIAriJtxJbkrKzGTITAu-vanjyoWuLrlfzgf3TMioCQ0ScslZzhmXV7vc7ft9xHzKuMqZyBkXIzJOkcqUWLyfkknX7RhLqeBj8vEEbXYDFTQGsxe0e4NLumqor9sYDmhpCxGqCiua3tsIde2bLa2DTRsXIq0CWFp-88cgOJoKh55zcuKg6vDiZ56Rt7vb1_VDtnm-f1yvNpkRTPaZFGhMKWUhjZvNrZQgUXLGBFMKZwiFVEbJeSHLKTDjrAQHRfqzmPKSWafEGSmGXhND10V0uo2-hvipOdNHJ3qnByf66EQzoZOThC1_Ycb30PvQ9BF89R98PcCYDjt4jLozHpND6yOaXtvg_y74AvudhWI |
| CitedBy_id | crossref_primary_10_1016_j_ins_2018_11_007 crossref_primary_10_1186_s13677_019_0139_6 crossref_primary_10_3233_IDT_190363 crossref_primary_10_1007_s11227_023_05434_6 crossref_primary_10_1016_j_procs_2023_11_030 crossref_primary_10_1007_s11227_019_02855_0 crossref_primary_10_1007_s11227_023_05310_3 crossref_primary_10_1109_ACCESS_2023_3298049 crossref_primary_10_1007_s11036_021_01793_7 crossref_primary_10_1142_S0219649221500519 crossref_primary_10_1016_j_procs_2023_01_041 crossref_primary_10_1007_s11227_020_03279_x crossref_primary_10_1007_s10489_022_03164_5 crossref_primary_10_1007_s11227_018_2391_9 crossref_primary_10_1109_ACCESS_2021_3133666 crossref_primary_10_1007_s11227_018_2578_0 crossref_primary_10_59717_j_xinn_life_2024_100079 crossref_primary_10_1002_cpe_7528 crossref_primary_10_1007_s10766_019_00627_0 crossref_primary_10_3390_math12243930 |
| Cites_doi | 10.1109/TIP.2016.2568752 10.1002/cpe.3642 10.1109/TIP.2016.2627801 10.1109/TIP.2015.2507942 10.1145/1327452.1327492 10.1145/2830544.2830546 10.1016/j.adhoc.2015.07.011 10.1007/s11432-011-4259-y 10.1109/TIP.2016.2617462 10.1109/TSP.2012.2222392 10.1016/j.future.2013.12.039 |
| ContentType | Journal Article |
| Copyright | 2017 Elsevier B.V. |
| Copyright_xml | – notice: 2017 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.future.2017.03.013 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EndPage | 1001 |
| ExternalDocumentID | 10_1016_j_future_2017_03_013 S0167739X17303710 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1~. 1~5 29H 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABDPE ABFNM ABJNI ABMAC ABWVN ABXDB ACDAQ ACGFS ACNNM ACRLP ACRPL ACZNC ADBBV ADEZE ADJOM ADMUD ADNMO AEBSH AEIPS AEKER AFJKZ AFTJW AGCQF AGHFR AGQPQ AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIIUN AIKHN AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD APXCP ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC CS3 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W KOM LG9 M41 MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 PC. Q38 R2- ROL RPZ SBC SDF SDG SES SEW SPC SPCBC SSV SSZ T5K UHS WUQ XPP ZMT ~G- ~HD 9DU AAYXX ACLOT CITATION |
| ID | FETCH-LOGICAL-c306t-63eccb6646cf58d66a6e61003077e5ea467c76846b2a0cfd6afa4a6e921b0df73 |
| ISICitedReferencesCount | 17 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000515213000074&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0167-739X |
| IngestDate | Sat Nov 29 06:59:15 EST 2025 Tue Nov 18 21:43:44 EST 2025 Sat Sep 13 17:02:33 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Scheduling Load balancing Distributed computing MapReduce |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c306t-63eccb6646cf58d66a6e61003077e5ea467c76846b2a0cfd6afa4a6e921b0df73 |
| ORCID | 0000-0003-2766-0845 |
| PageCount | 9 |
| ParticipantIDs | crossref_primary_10_1016_j_future_2017_03_013 crossref_citationtrail_10_1016_j_future_2017_03_013 elsevier_sciencedirect_doi_10_1016_j_future_2017_03_013 |
| PublicationCentury | 2000 |
| PublicationDate | April 2020 2020-04-00 |
| PublicationDateYYYYMMDD | 2020-04-01 |
| PublicationDate_xml | – month: 04 year: 2020 text: April 2020 |
| PublicationDecade | 2020 |
| PublicationTitle | Future generation computer systems |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Tian, Zhou, He, Zha (br000045) 2009 Lu, Zheng, Li (br000030) 2017; 26 Gaizhen (br000075) 2011 Elnikety, Elsayed, Ramadan (br000095) 2011 Mao, Morris, Kaashoek (br000050) 2010 Xue, Shu, Liu, Xue (br000170) 2011; 54 Yuan, Zheng, Lu (br000020) 2017; 26 Liu, Fang, Hu, Zhang (br000120) 2001 Xu, Cai, Aydt, Lees (br000165) 2014 Hsueh, Lin, Chiu (br000140) 2014 B. Catanzaro, N. Sundaram, K. Keutzer, A map reduce framework for programming gpus, in: Proc. Third Workshop Software Tools for MultiCore Systems, STMCS, 2008. Vu, Alaghband (br000160) 2015 Karapiperis, Verykios (br000150) 2015; 17 Kolb, Thor, Rahm (br000135) 2011 Rajasekaran, Reif (br000125) 2007 Elteir, Lin, Feng (br000115) 2010 Lin, Wang, Hsueh (br000105) 2015 Li, Guo, Lu (br000025) 2016; 25 Zou, Xue, Liu (br000070) 2014; 37 Zaki, Li, Parthasarathy (br000100) 1996 D. Gillick, A. Faria, J. DeNero, Mapreduce: Distributed computing for machine learning, Berkley, Dec 18. Apache. hadoop, 2016. Yao, Bai, Zeng, Liang, Fan (br000010) 2015; 27 . Li, Wang, Lu (br000035) 2016; 25 Ren (br000015) 2015; 27 Kathiravelu, Veiga (br000130) 2014 Mestre, Pires, Nascimento (br000145) 2015 Hwang, Sung (br000085) 2013; 61 Green, Munguía, Bader (br000155) 2014 Dean, Ghemawat (br000040) 2008; 51 Hu, Zhao, Yan, Zeng, Guo (br000065) 2015; 35 J. Zawodny, Yahoo! launches worlds largest hadoop production application, Yahoo! Developer Network sBlog. Ren, Zeng, Liu, Cheng (br000005) 2016; 2016 Gunarathne, Wu, Qiu, Fox (br000090) 2010 Lu (10.1016/j.future.2017.03.013_br000030) 2017; 26 Gunarathne (10.1016/j.future.2017.03.013_br000090) 2010 Tian (10.1016/j.future.2017.03.013_br000045) 2009 Dean (10.1016/j.future.2017.03.013_br000040) 2008; 51 Liu (10.1016/j.future.2017.03.013_br000120) 2001 Rajasekaran (10.1016/j.future.2017.03.013_br000125) 2007 Gaizhen (10.1016/j.future.2017.03.013_br000075) 2011 Mestre (10.1016/j.future.2017.03.013_br000145) 2015 Hsueh (10.1016/j.future.2017.03.013_br000140) 2014 Zou (10.1016/j.future.2017.03.013_br000070) 2014; 37 Kolb (10.1016/j.future.2017.03.013_br000135) 2011 Ren (10.1016/j.future.2017.03.013_br000005) 2016; 2016 10.1016/j.future.2017.03.013_br000055 10.1016/j.future.2017.03.013_br000110 Yao (10.1016/j.future.2017.03.013_br000010) 2015; 27 Elteir (10.1016/j.future.2017.03.013_br000115) 2010 Yuan (10.1016/j.future.2017.03.013_br000020) 2017; 26 Kathiravelu (10.1016/j.future.2017.03.013_br000130) 2014 Ren (10.1016/j.future.2017.03.013_br000015) 2015; 27 Lin (10.1016/j.future.2017.03.013_br000105) 2015 Li (10.1016/j.future.2017.03.013_br000025) 2016; 25 Hwang (10.1016/j.future.2017.03.013_br000085) 2013; 61 Vu (10.1016/j.future.2017.03.013_br000160) 2015 Mao (10.1016/j.future.2017.03.013_br000050) 2010 Hu (10.1016/j.future.2017.03.013_br000065) 2015; 35 Xu (10.1016/j.future.2017.03.013_br000165) 2014 10.1016/j.future.2017.03.013_br000060 Green (10.1016/j.future.2017.03.013_br000155) 2014 10.1016/j.future.2017.03.013_br000080 Karapiperis (10.1016/j.future.2017.03.013_br000150) 2015; 17 Xue (10.1016/j.future.2017.03.013_br000170) 2011; 54 Li (10.1016/j.future.2017.03.013_br000035) 2016; 25 Zaki (10.1016/j.future.2017.03.013_br000100) 1996 Elnikety (10.1016/j.future.2017.03.013_br000095) 2011 |
| References_xml | – start-page: 79 year: 2014 end-page: 88 ident: br000130 article-title: An adaptive distributed simulator for cloud and mapreduce algorithms and architectures publication-title: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing – start-page: 981 year: 2015 end-page: 987 ident: br000145 article-title: Adaptive sorted neighborhood blocking for entity matching with mapreduce publication-title: Proceedings of the 30th Annual ACM Symposium on Applied Computing – volume: 54 start-page: 1119 year: 2011 end-page: 1128 ident: br000170 article-title: Corslet: A shared storage system keeping your data private publication-title: Sci. China Inf. Sci. – volume: 26 start-page: 51 year: 2017 end-page: 64 ident: br000020 article-title: Discovering diverse subset for unsupervised hyperspectral band selection publication-title: IEEE Trans. Image Process. – reference: Apache. hadoop, 2016. – reference: D. Gillick, A. Faria, J. DeNero, Mapreduce: Distributed computing for machine learning, Berkley, Dec 18. – start-page: 154 year: 2011 end-page: 156 ident: br000075 article-title: The application of mapreduce in the cloud computing publication-title: Intelligence Information Processing and Trusted Computing (IPTC) – start-page: 218 year: 2009 end-page: 224 ident: br000045 article-title: A dynamic mapreduce scheduler for heterogeneous workloads publication-title: Eighth International Conference on Grid and Cooperative Computing, 2009 – start-page: 3 year: 2014 end-page: 9 ident: br000140 article-title: A load-balanced mapreduce algorithm for blocking-based entity-resolution with multiple keys publication-title: Proceedings of the Twelfth Australasian Symposium on Parallel and Distributed Computing-Volume 152 – start-page: 81 year: 2011 end-page: 90 ident: br000095 article-title: ihadoop: asynchronous iterations for mapreduce publication-title: 2011 IEEE Third International Conference on Cloud Computing Technology and Science – year: 2001 ident: br000120 article-title: An effective dynamic load balancing method publication-title: J. Softw. – start-page: 3 year: 2014 end-page: 10 ident: br000155 article-title: Load balanced clustering coefficients publication-title: Proceedings of the First Workshop on Parallel Programming for Analytics Applications – start-page: 565 year: 2010 end-page: 572 ident: br000090 article-title: Mapreduce in the clouds for science publication-title: 2010 IEEE Second International Conference on Cloud Computing Technology and Science – year: 2010 ident: br000050 article-title: Optimizing mapreduce for multicore architectures publication-title: Computer Science and Artificial Intelligence Laboratory, Tech. Rep. – volume: 61 start-page: 411 year: 2013 end-page: 419 ident: br000085 article-title: Load balanced resampling for real-time particle filtering on graphics processing units publication-title: IEEE Trans. Signal Process. – volume: 25 start-page: 3329 year: 2016 end-page: 3342 ident: br000025 article-title: Spatiotemporal statistics for video quality assessment publication-title: IEEE Trans. Image Process. – volume: 26 start-page: 355 year: 2017 end-page: 368 ident: br000030 article-title: Latent semantic minimal hashing for image retrieval publication-title: IEEE Trans. Image Process. – year: 2007 ident: br000125 article-title: Handbook of Parallel Computing: Models, Algorithms and Applications – volume: 27 start-page: 5780 year: 2015 end-page: 5792 ident: br000010 article-title: Migrate or not? exploring virtual machine migration in roadside cloudlet-based vehicular cloud publication-title: Concurr. Comput.: Pract. Exper. – volume: 35 start-page: 116 year: 2015 end-page: 126 ident: br000065 article-title: A mapreduce based parallel niche genetic algorithm for contaminant source identification in water distribution network publication-title: Ad Hoc Networks – volume: 37 start-page: 378 year: 2014 end-page: 389 ident: br000070 article-title: A case study of large-scale parallel i/o analysis and optimization for numerical weather prediction system publication-title: Future Gener. Comput. Syst. – start-page: 2397 year: 2011 end-page: 2400 ident: br000135 article-title: Block-based load balancing for entity resolution with mapreduce publication-title: Proceedings of the 20th ACM International Conference on Information and Knowledge Management – volume: 17 start-page: 1 year: 2015 end-page: 7 ident: br000150 article-title: Load-balancing the distance computations in record linkage publication-title: ACM SIGKDD Explor. Newslett. – volume: 25 start-page: 740 year: 2016 end-page: 755 ident: br000035 article-title: Surveillance video synopsis via scaling down objects publication-title: IEEE Trans. Image Process. – start-page: 282 year: 1996 end-page: 291 ident: br000100 article-title: Customized dynamic load balancing for a network of workstations publication-title: Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing, 1996 – volume: 51 start-page: 107 year: 2008 end-page: 113 ident: br000040 article-title: Mapreduce: simplified data processing on large clusters publication-title: Commun. ACM – reference: . – start-page: 3483 year: 2014 end-page: 3494 ident: br000165 article-title: Efficient graph-based dynamic load-balancing for parallel large-scale agent-based traffic simulation publication-title: Simulation Conference (WSC), 2014 Winter – reference: J. Zawodny, Yahoo! launches worlds largest hadoop production application, Yahoo! Developer Network sBlog. – start-page: 397 year: 2010 end-page: 405 ident: br000115 article-title: Enhancing mapreduce via asynchronous data processing publication-title: 2010 IEEE 16th International Conference on Parallel and Distributed Systems – volume: 27 start-page: 173 year: 2015 end-page: 195 ident: br000015 article-title: uleepp: An ultra-lightweight energy-efficient and privacy-protected scheme for pervasive and mobile wbsn-cloud communications publication-title: Ad Hoc Sens. Wirel. Netw. – start-page: 49 year: 2015 end-page: 58 ident: br000160 article-title: A load balancing parallel method for frequent pattern mining on multi-core cluster publication-title: Proceedings of the Symposium on High Performance Computing – reference: B. Catanzaro, N. Sundaram, K. Keutzer, A map reduce framework for programming gpus, in: Proc. Third Workshop Software Tools for MultiCore Systems, STMCS, 2008. – start-page: 50 year: 2015 ident: br000105 article-title: Improving mapreduce-based entity-resolution by data-load balancing publication-title: Proceedings of the ASE BigData & SocialInformatics 2015 – volume: 2016 start-page: 5232846:1 year: 2016 end-page: 5232846:9 ident: br000005 article-title: F2AC: A lightweight, fine-grained, and flexible access control scheme for file storage in mobile cloud computing publication-title: Mobile Inf. Syst. – volume: 25 start-page: 3329 issue: 7 year: 2016 ident: 10.1016/j.future.2017.03.013_br000025 article-title: Spatiotemporal statistics for video quality assessment publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2016.2568752 – ident: 10.1016/j.future.2017.03.013_br000055 – ident: 10.1016/j.future.2017.03.013_br000080 – start-page: 565 year: 2010 ident: 10.1016/j.future.2017.03.013_br000090 article-title: Mapreduce in the clouds for science – volume: 27 start-page: 5780 issue: 18 year: 2015 ident: 10.1016/j.future.2017.03.013_br000010 article-title: Migrate or not? exploring virtual machine migration in roadside cloudlet-based vehicular cloud publication-title: Concurr. Comput.: Pract. Exper. doi: 10.1002/cpe.3642 – start-page: 3 year: 2014 ident: 10.1016/j.future.2017.03.013_br000140 article-title: A load-balanced mapreduce algorithm for blocking-based entity-resolution with multiple keys – volume: 26 start-page: 355 issue: 1 year: 2017 ident: 10.1016/j.future.2017.03.013_br000030 article-title: Latent semantic minimal hashing for image retrieval publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2016.2627801 – volume: 25 start-page: 740 issue: 2 year: 2016 ident: 10.1016/j.future.2017.03.013_br000035 article-title: Surveillance video synopsis via scaling down objects publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2507942 – ident: 10.1016/j.future.2017.03.013_br000110 – start-page: 3483 year: 2014 ident: 10.1016/j.future.2017.03.013_br000165 article-title: Efficient graph-based dynamic load-balancing for parallel large-scale agent-based traffic simulation – volume: 51 start-page: 107 issue: 1 year: 2008 ident: 10.1016/j.future.2017.03.013_br000040 article-title: Mapreduce: simplified data processing on large clusters publication-title: Commun. ACM doi: 10.1145/1327452.1327492 – volume: 17 start-page: 1 issue: 1 year: 2015 ident: 10.1016/j.future.2017.03.013_br000150 article-title: Load-balancing the distance computations in record linkage publication-title: ACM SIGKDD Explor. Newslett. doi: 10.1145/2830544.2830546 – volume: 35 start-page: 116 year: 2015 ident: 10.1016/j.future.2017.03.013_br000065 article-title: A mapreduce based parallel niche genetic algorithm for contaminant source identification in water distribution network publication-title: Ad Hoc Networks doi: 10.1016/j.adhoc.2015.07.011 – start-page: 282 year: 1996 ident: 10.1016/j.future.2017.03.013_br000100 article-title: Customized dynamic load balancing for a network of workstations – volume: 54 start-page: 1119 issue: 6 year: 2011 ident: 10.1016/j.future.2017.03.013_br000170 article-title: Corslet: A shared storage system keeping your data private publication-title: Sci. China Inf. Sci. doi: 10.1007/s11432-011-4259-y – volume: 26 start-page: 51 issue: 1 year: 2017 ident: 10.1016/j.future.2017.03.013_br000020 article-title: Discovering diverse subset for unsupervised hyperspectral band selection publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2016.2617462 – volume: 61 start-page: 411 issue: 2 year: 2013 ident: 10.1016/j.future.2017.03.013_br000085 article-title: Load balanced resampling for real-time particle filtering on graphics processing units publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2012.2222392 – year: 2001 ident: 10.1016/j.future.2017.03.013_br000120 article-title: An effective dynamic load balancing method publication-title: J. Softw. – start-page: 981 year: 2015 ident: 10.1016/j.future.2017.03.013_br000145 article-title: Adaptive sorted neighborhood blocking for entity matching with mapreduce – start-page: 2397 year: 2011 ident: 10.1016/j.future.2017.03.013_br000135 article-title: Block-based load balancing for entity resolution with mapreduce – volume: 27 start-page: 173 issue: 3–4 year: 2015 ident: 10.1016/j.future.2017.03.013_br000015 article-title: uleepp: An ultra-lightweight energy-efficient and privacy-protected scheme for pervasive and mobile wbsn-cloud communications publication-title: Ad Hoc Sens. Wirel. Netw. – start-page: 79 year: 2014 ident: 10.1016/j.future.2017.03.013_br000130 article-title: An adaptive distributed simulator for cloud and mapreduce algorithms and architectures – volume: 2016 start-page: 5232846:1 year: 2016 ident: 10.1016/j.future.2017.03.013_br000005 article-title: F2AC: A lightweight, fine-grained, and flexible access control scheme for file storage in mobile cloud computing publication-title: Mobile Inf. Syst. – start-page: 50 year: 2015 ident: 10.1016/j.future.2017.03.013_br000105 article-title: Improving mapreduce-based entity-resolution by data-load balancing – start-page: 397 year: 2010 ident: 10.1016/j.future.2017.03.013_br000115 article-title: Enhancing mapreduce via asynchronous data processing – year: 2007 ident: 10.1016/j.future.2017.03.013_br000125 – start-page: 218 year: 2009 ident: 10.1016/j.future.2017.03.013_br000045 article-title: A dynamic mapreduce scheduler for heterogeneous workloads – year: 2010 ident: 10.1016/j.future.2017.03.013_br000050 article-title: Optimizing mapreduce for multicore architectures – ident: 10.1016/j.future.2017.03.013_br000060 – start-page: 3 year: 2014 ident: 10.1016/j.future.2017.03.013_br000155 article-title: Load balanced clustering coefficients – start-page: 154 year: 2011 ident: 10.1016/j.future.2017.03.013_br000075 article-title: The application of mapreduce in the cloud computing – start-page: 81 year: 2011 ident: 10.1016/j.future.2017.03.013_br000095 article-title: ihadoop: asynchronous iterations for mapreduce – start-page: 49 year: 2015 ident: 10.1016/j.future.2017.03.013_br000160 article-title: A load balancing parallel method for frequent pattern mining on multi-core cluster – volume: 37 start-page: 378 year: 2014 ident: 10.1016/j.future.2017.03.013_br000070 article-title: A case study of large-scale parallel i/o analysis and optimization for numerical weather prediction system publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2013.12.039 |
| SSID | ssj0001731 |
| Score | 2.4112644 |
| Snippet | With the advent of the era of big data, the demand of massive data processing applications is also growing. Currently, MapReduce is the most commonly used data... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 993 |
| SubjectTerms | Distributed computing Load balancing MapReduce Scheduling |
| Title | Map-Balance-Reduce: An improved parallel programming model for load balancing of MapReduce |
| URI | https://dx.doi.org/10.1016/j.future.2017.03.013 |
| Volume | 105 |
| WOSCitedRecordID | wos000515213000074&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0167-739X databaseCode: AIEXJ dateStart: 19950201 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0001731 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELeqjgdeYHyJMYb8wGuQ09R2zFtBm7YKpgoNqeIlcmwHrcrSarTT_oX915w_G7FpsAceGlVWfGlzv9ydL-f7IfRel0RqQwhYv5rBAoWyTFKuM6PAv9KyLnJVO7IJfnpazudiNhjcxL0wVy3vuvL6Wqz-q6phDJRtt84-QN1JKAzAd1A6HEHtcPwnxX-Vq-yTLVhUJvtmG7OakPw7d_kDCDBtu--2NW0szrqw6QJHieOKDtulhKjUSQgl0SDSS-qHskeuG4mlYDYBRSowRIT20Cla_-IqBqYAxAV8fm6HN84ByMUmIXTm87HTHmhTSntmwtyQpBiRXm1LyFuCPeaFY83dGl5Ce6ZTeKbE4IVta6g7LbxPNiw--JYrtjaPuy61ebH1aPEt_h-OLpUfxsq2ReWlVFZKRYqKWP7jnRGnohyincnJ4Xya3HrOA7ll-CdxH6YrFrz9a-6Oc3qxy9kuehIWHXjiwfIMDUz3HD2NhB442PcX6Mdt7HzEkw5H5OCIHNxDDnbIwYAcbJGDE3LwssEJOS_R96PDs8_HWSDfyBSsItcZK-DhrhkbM9XQUjMmmYFQ2_oEbqiR4GCVfYnL6pEkqtFMNnIM54hRXhPd8OIVGnbLzrxGmMOaVhgqx1TXYy10LYjiQuYN3EpupNhDRbxVlQqd6S1BSlvdp6g9lKVZK9-Z5S_n86iFKkSXPmqsAFr3znzzwCvto8fbZ-AtGq4vN-YAPVJX6_Nfl-8Crn4Doj-fUw |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Map-Balance-Reduce%3A+An+improved+parallel+programming+model+for+load+balancing+of+MapReduce&rft.jtitle=Future+generation+computer+systems&rft.au=Li%2C+Jianjiang&rft.au=Liu%2C+Yajun&rft.au=Pan%2C+Jian&rft.au=Zhang%2C+Peng&rft.date=2020-04-01&rft.issn=0167-739X&rft.volume=105&rft.spage=993&rft.epage=1001&rft_id=info:doi/10.1016%2Fj.future.2017.03.013&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_future_2017_03_013 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0167-739X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0167-739X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0167-739X&client=summon |