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[3] Apache hadoop 2.6.0—Hadoop map reduce next generation-2.6.0—Capacity scheduler. https://hadoop.apache.org/docs/r2.6.0/
hadoop-yarn/hadoop-yarn-site/CapacityScheduler.html
[4] Zaharia M, Borthakur D, Sarma JS, Elmeleegy K, Shenker S, Stoica I. Job scheduling for multi-user mapreduce clusters. Technical
Report, UCB/EECS-2009-55, Berkeley: EECS Department, University of California, 2009.
[5] Zaharia M, Borthakur D, Sen Sarma J, Elmeleegy K, Shenker S, Stoica I. Delay scheduling: A simple technique for achieving
locality and fairness in cluster scheduling. In: Proc. of the 5th European Conf. on Computer Systems. 2010. 265−278.
[6] Zaharia M, Konwinski A, Joseph AD, Katz RH, Stoica I. Improving MapReduce performance in heterogeneous environments. In:
Proc. of the OSDI, Vol.8. 2008. 7.
[7] Welcome to ApacheTM hadoop®! http://hadoop.apache.org/
[8] Reiss C, Tumanov A, Ganger GR, Katz RH, Kozuch MA. Towards understanding heterogeneous clouds at scale: Google trace
analysis. Technical Report, Intel Science and Technology Center for Cloud Computing, 2012. 84.
[9] Abdul-Rahman OA, Aida K. Towards understanding the usage behavior of google cloud users: The mice and elephants phenomenon.
In: Proc. of the 2014 IEEE 6th Int’l Conf. on Cloud Computing Technology and Science (CloudCom). 2014. 272−277.
[10] Di S, Kondo D, Cappello F. Characterizing cloud applications on a google data center. In: Proc. of the 2013 42nd Int’l Conf. on
Parallel Processing (ICPP). 2013. 468−473.
[11] Boutin E, et al. Apollo: Scalable and coordinated scheduling for cloud-scale computing. In: Proc. of the 11th USENIX Symp. on
Operating Systems Design and Implementation (OSDI 2014). 2014. 285−300.
[12] Schwarzkopf M, Konwinski A, Abd-El-Malek M, Wilkes J. Omega: Flexible, scalable schedulers for large compute clusters. In:
Proc. of the 8th ACM European Conf. on Computer Systems. 2013. 351−364.
[13] Grandl R, Ananthanarayanan G, Kandula S, Rao S, Akella A. Multi-Resource packing for cluster schedulers. In: Proc. of the 2014
ACM Conf. on SIGCOMM. 2014. 455−466.
[14] Lu P, Lee YC, Wang C, Zhou BB, Chen J, Zomaya AY. Workload characteristic oriented scheduler for mapreduce. In: Proc. of the
2012 IEEE 18th Int’l Conf. on Parallel and Distributed Systems (ICPADS). 2012. 156−163.
[15] Tian C, Zhou H, He Y, Zha L. A dynamic mapreduce scheduler for heterogeneous workloads. In: Proc. of the 2009 8th Int’l Conf.
on Grid and Cooperative Computing. 2009. 218−224.
[16] Dean J, Barroso LA. The tail at scale. Communications of the ACM, 2013,56(2):74−80.
[17] Garraghan P, Ouyang X, Yang R, McKee D, Xu J. Straggler root-cause and impact analysis for massive-scale virtualized cloud
datacenters. IEEE Trans. on Services Computing, 2016,12(1).
[18] Vavilapalli VK, et al. Apache hadoop yarn: Yet another resource negotiator. In: Proc. of the 4th Annual Symp. on Cloud Computing.
2013. 5.
[19] Zhang Z, Li C, Tao Y, Yang R, Tang H, Xu J. Fuxi: A fault-tolerant resource management and job scheduling system at Internet
scale. Proc. of the VLDB Endowment, 2014,7(13):1393−1404.
[20] Verma A, Pedrosa L, Korupolu M, Oppenheimer D, Tune E, Wilkes J. Large-Scale cluster management at google with Borg. In:
Proc. of the 10th European Conf. on Computer Systems. 2015. 18.
[21] Ghodsi A, Zaharia M, Hindman B, Konwinski A, Shenker S, Stoica I. Dominant resource fairness: Fair allocation of multiple
resource types. In: Proc. of the NSDI, Vol.11. 2011..
[22] Grandl R, Chowdhury M, Akella A, Ananthanarayanan G. Altruistic scheduling in multi-resource clusters. In: Proc. of the 12th
USENIX Symp. on Operating Systems Design and Implementation (OSDI 2016). 2016.
[23] Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 2008,51(1):107−113.
[24] Hindman B, et al. Mesos: A platform for fine-grained resource sharing in the data center. In: Proc. of the NSDI, Vol.11. 2011.
[25] Isard M, Prabhakaran V, Currey J, Wieder U, Talwar K, Goldberg A. Quincy: Fair scheduling for distributed computing clusters. In:
Proc. of the ACM SIGOPS 22nd Symp. on Operating Systems Principles. 2009. 261−276.
[26] Gog I, Schwarzkopf M, Gleave A, Watson RN, Hand S. Firmament: Fast, centralized cluster scheduling at scale. In: Proc. of the
12th USENIX Symp. on Operating Systems Design and Implementation (OSDI 2016). 2016. 99.
[27] Ananthanarayanan G, et al. Reining in the outliers in map-reduce clusters using mantri. In: Proc. of the OSDI, Vol.10. 2010. 24.
[28] Kung HT, Robinson JT. On optimistic methods for concurrency control. ACM Trans. on Database Systems (TODS), 1981,6(2):
213−226.
[29] Ghodsi A, Zaharia M, Shenker S, Stoica I. Choosy: Max-min fair sharing for datacenter jobs with constraints. In: Proc. of the 8th
ACM European Conf. on Computer Systems. 2013. 365−378.