본 포스트는 아파치 하둡 얀 (Apache Hadoop YARN)의 최근 논문의 “Introduction” 섹션을 발췌하고, 해당 원문에 소개되는 용어 및 기술을 정리한다.
Apache Hadoop began as one of many open-source implementations of MapReduce , focused on tackling the unprecedented scale required to index web crawls. Its execution architecture was tuned for this use case, focusing on strong fault tolerance for massive, data-intensive computations. In many large web companies and startups, Hadoop clusters are the common place where operational data are stored and processed. More importantly, it became the place within an organization where engineers and researchers have instantaneous and almost unrestricted access to vast amounts of computational resources and troves of company data. This is both a cause of Hadoop’s success and also its biggest curse, as the public of developers extended the MapReduce programming model beyond the capabilities of the cluster management substrate. A common pattern submits “map-only” jobs to spawn arbitrary processes in the cluster. Examples of (ab)uses include forking web servers and gang-scheduled computation of iterative workloads. Developers, in order to leverage the physical resources, often resorted to clever workarounds to sidestep the limits of the MapReduce API. These limitations and misuses motivated an entire class of papers using Hadoop as a baseline for unrelated environments. While many papers exposed substantial issues with the Hadoop architecture or implementation, some simply denounced (more or less ingeniously) some of the side-effects of these misuses. The limitations of the original Hadoop architecture are, by now, well understood by both the academic and open-source communities.
In this paper, we present a community-driven effort to move Hadoop past its original incarnation. We present the next generation of Hadoop compute platform known as YARN, which departs from its familiar, monolithic architecture. By separating resource management functions from the programming model, YARN delegates many scheduling-related functions to per-job components. In this new context, MapReduce is just one of the applications running on top of YARN. This separation provides a great deal of flexibility in the choice of programming framework. Examples of alternative programming models that are becoming available on YARN are: Dryad , Giraph, Hoya, REEF , Spark , Storm  and Tez . Programming frameworks running on YARN coordinate intra-application communication, execution flow, and dynamic optimizations as they see fit, unlocking dramatic performance improvements. We describe YARN’s inception, design, open-source development, and deployment from our perspective as early architects and implementors.
 Apache hadoop. http://hadoop.apache.org.
 Apache tez. http://incubator.apache.org/projects/tez.html.
 Netty project. http://netty.io.
 Storm. http://storm-project.net/.
 H.Ballani, P.Costa, T.Karagiannis, and A.I.Rowstron. Towards predictable datacenter networks. In SIGCOMM, volume 11, pages 242–253, 2011.
 F.P.Brooks,Jr. The mythical man-month (anniversary ed.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1995.
 N. Capit, G. Da Costa, Y. Georgiou, G. Huard, C. Martin, G. Mounie, P. Neyron, and O. Richard. A batch scheduler with high level components. In Cluster Computing and the Grid, 2005. CC-Grid 2005. IEEE International Symposium on, volume 2, pages 776–783 Vol. 2, 2005.
 R. Chaiken, B. Jenkins, P.-A. Larson, B. Ramsey, D. Shakib, S. Weaver, and J. Zhou. Scope: easy and efficient parallel processing of massive data sets. Proc. VLDB Endow., 1(2):1265–1276, Aug. 2008.
 M. Chowdhury, M. Zaharia, J. Ma, M. I. Jordan, and I. Stoica. Managing data transfers in computer clusters with orchestra. SIGCOMM- Computer Communication Review, 41(4):98, 2011.
 B.-G. Chun, T. Condie, C. Curino, R. Ramakrishnan, R. Sears, and M. Weimer. Reef: Retainable evaluator execution framework. In VLDB 2013, Demo, 2013.
 B. F. Cooper, E. Baldeschwieler, R. Fonseca, J. J. Kistler, P. Narayan, C. Neerdaels, T. Negrin, R. Ramakrishnan, A. Silberstein, U. Srivastava, et al. Building a cloud for Yahoo! IEEE Data Eng. Bull., 32(1):36–43, 2009.
 J. Dean and S. Ghemawat. MapReduce: simplified data processing on large clusters. Commun. ACM, 51(1):107–113, Jan. 2008.
 W. Emeneker, D. Jackson, J. Butikofer, and D. Stanzione. Dynamic virtual clustering with xen and moab. In G. Min, B. Martino, L. Yang, M. Guo, and G. Rnger, editors, Frontiers of High Performance Computing and Networking, ISPA 2006 Workshops, volume 4331 of Lecture Notes in Computer Science, pages 440–451. Springer Berlin Heidelberg, 2006.
 Facebook Engineering Team. Under the Hood: Scheduling MapReduce jobs more efficiently with Corona. http://on.fb.me/TxUsYN, 2012.
 D. Gottfrid. Self-service prorated super-computing fun. http://open. blogs.nytimes.com/2007/11/01/self-service-prorated-super-computing-fun, 2007.
 T. Graves. GraySort and MinuteSort at Yahoo on Hadoop 0.23. http://sortbenchmark. org/Yahoo2013Sort.pdf, 2013.
 B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. D. Joseph, R. Katz, S. Shenker, and I. Stoica. Mesos: a platform for fine-grained resource sharing in the data center. In Proceedings of the 8th USENIX conference on Networked systems design and implementation, NSDI’11, pages 22–22, Berkeley, CA, USA, 2011. USENIX Association.
 M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: distributed data-parallel programs from sequential building blocks. In Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, EuroSys ’07, pages 59–72, New York, NY, USA, 2007. ACM.
 M. Islam, A. K. Huang, M. Battisha, M. Chiang, S. Srinivasan, C. Peters, A. Neumann, and A. Abdelnur. Oozie: towards a scalable workflow management system for hadoop. In Proceedings of the 1st ACM SIGMOD Workshop on Scalable Workflow Execution Engines and Technologies, page 4. ACM, 2012.
 D. B. Jackson, Q. Snell, and M. J. Clement. Core algorithms of the maui scheduler. In Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP ’01, pages 87–102, London, UK, UK, 2001. Springer-Verlag.
 S. Loughran, D. Das, and E. Baldeschwieler. Introducing Hoya – HBase on YARN. http://hortonworks.com/blog/introducing-hoya-hbase-on-yarn/, 2013.
 G. Malewicz, M. H. Austern, A. J. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. Pregel: a system for large-scale graph processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, SIGMOD ’10, pages 135–146, New York, NY, USA, 2010. ACM.
 R. O. Nambiar and M. Poess. The making of tpcds. In Proceedings of the 32nd international conference on Very large data bases, VLDB ’06, pages 1049–1058. VLDB Endowment, 2006.
 C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig Latin: a not-so-foreign language for data processing. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, SIGMOD ’08, pages 1099–1110, New York, NY, USA, 2008. ACM.
 O.O’Malley. Hadoop: The Definitive Guide, chapter Hadoop at Yahoo!, pages 11–12. O’Reilly Media, 2012.
 M. Schwarzkopf, A. Konwinski, M. Abd-El-Malek, and J. Wilkes. Omega: flexible, scalable schedulers for large compute clusters. In Proceedings of the 8th ACM European Conference on Computer Systems, EuroSys ’13, pages 351–364, New York, NY, USA, 2013. ACM.
 K.Shvachko, H.Kuang, S.Radia, and R.Chansler. The Hadoop Distributed File System. In Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), MSST ’10, pages 1–10, Washington, DC, USA, 2010. IEEE Computer Society.
 T.-W. N. Sze. The two quadrillionth bit of π is 0! http://developer.yahoo.com/blogs/hadoop/two-quadrillionth-bit-0-467.html.
 D. Thain, T. Tannenbaum, and M. Livny. Distributed computing in practice: the Condor experience. Concurrency and Computation: Practice and Experience, 17(2-4):323–356, 2005.
 A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, N. Z. 0002, S. Anthony, H. Liu, and R. Murthy. Hive - a petabyte scale data warehouse using Hadoop. In F. Li, M. M. Moro, S. Ghandeharizadeh, J. R. Haritsa, G. Weikum, M. J. Carey, F. Casati, E. Y. Chang, I. Manolescu, S. Mehrotra, U. Dayal, and V. J. Tsotras, editors, Proceedings of the 26th International Conference on Data Engineering, ICDE 2010, March 1-6, 2010, Long Beach, California, USA, pages 996–1005. IEEE, 2010.
 Y. Yu, M. Isard, D. Fetterly, M. Budiu, U. Erlingsson, P. K. Gunda, and J. Currey. DryadLINQ: a system for general-purpose distributed data-parallel computing using a high-level language. In Proceedings of the 8th USENIX conference on Operating systems design and implementation, OSDI’08, pages 1–14, Berkeley, CA, USA, 2008. USENIX Association.
 M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica. Spark: cluster computing with working sets. In Proceedings of the 2nd USENIX conference on Hot topics in cloud computing, HotCloud’10, pages 10–10, Berkeley, CA, USA, 2010. USENIX Association.
 Vinod Kumar Vavilapali, et. al, Apache Hadoop YARN – Yet Another Resource Negotiator, SoCC’13, 1-3 Oct. 2013, Santa Clara, California, USA.