- Locality Awareness
- High Cluster Utilization
- Secure and Auditable Operation
- Support for Programming Model Diversity
- Flexible Resource Model
- Backward compatibility
Apache Hadoop YARN – Related Work
Others have recognized the same limitations in the classic Hadoop architecture, and have concurrently developed alternative solutions, which can be closely compared to YARN. Among the many efforts the most closely resembling YARN are: Mesos , Omega , Corona , and Cosmos , maintained and used respectively by Twitter, Google, Facebook and Microsoft.
These systems share a common inspiration, and the high-level goal of improving scalability, latency and programming model flexibility. The many architectural differences are a reflection of diverse design priorities, and sometimes simply the effect of different historical contexts. While a true quantitative comparison is impossible to provide, we will try to highlight some of the architectural differences and our understanding of their rationale.
Omega’s design leans more heavily towards distributed, multi-level scheduling. This reflects a greater focus on scalability, but makes it harder to enforce global properties such as capacity/fairness/deadlines. To this goal the authors seem to rely on coordinated development of the various frameworks that will be respectful of each other at runtime. This is sensible for a closed-world like Google, but not amenable to an open platform like Hadoop where arbitrary frameworks from diverse independent sources are share the same cluster.
Corona uses push based communication as opposed to the heartbeat based control-plane framework approach in YARN and other frameworks. The latency/scalability tradeoffs are non-trivial and would deserve a detailed comparison.
While Mesos and YARN both have schedulers at two levels, there are two very significant differences. First, Mesos is an offer-based resource manager, whereas YARN has a request-based approach. YARN allows the AM to ask for resources based on various criteria including locations, allows the requester to modify future requests based on what was given and on current usage. Our approach was necessary to support the location based allocation. Second, instead of a per-job intraframework scheduler, Mesos leverages a pool of central schedulers (e.g., classic Hadoop or MPI). YARN enables late binding of containers to tasks, where each individual job can perform local optimizations, and seems more amenable to rolling upgrades (since each job can run on a different version of the framework). On the other side, per-job ApplicationMaster might result in greater overhead than the Mesos approach.
Cosmos closely resembles Hadoop 2.0 architecturally with respect to storage and compute layers with the key difference of not having a central resource manager. However, it seems to be used for a single application type: Scope . By virtue of a more narrow target Cosmos can leverage many optimizations such as native compression, indexed files, co-location of partitions of datasets to speed up Scope. The behavior with multiple application frameworks is not clear.
Prior to these recent efforts, there is a long history of work on resource management and scheduling. Condor , Torque , Moab  and Maui . Our early Hadoop clusters used some of these systems, but we found that they could not support the MapReduce model in a first-class way. Specifically, neither the data locality nor the elastic scheduling needs of map and reduce phases were expressible, so one was forced to allocate “virtual” Hadoop with the attendant utilization costs discussed in section 2.1. Perhaps some of these issues were due to the fact that many of these distributed schedulers were originally created to support MPI style and HPC application models and running coarse-grained non-elastic workloads. These cluster schedulers do allow clients to specify the types of processing environments, but unfortunately not locality constraints which is a key concern for Hadoop.
Another class of related technologies comes from the world of cloud infrastructures such as EC2, Azure, Eucalyptus and VMWare offerings. These mostly target VM-based sharing of a cluster, and are generally designed for long running processes (as the VM boot-times overheads are prohibitive).
 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. SIGCOMMComputer 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.