메뉴 건너뛰기




Volumn , Issue , 2012, Pages

The seven deadly sins of cloud computing research

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING; COMPUTER SCIENCE;

EID: 85017487069     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (39)

References (45)
  • 1
    • 79957809015 scopus 로고    scopus 로고
    • HadoopDB: An architectural hybrid of MapReduce and DBMS technologies for analytical workloads
    • ABOUZEID, A., ET AL. HadoopDB: An architectural hybrid of MapReduce and DBMS technologies for analytical workloads. Proc. VLDB Endowment 2, 1 (2009), 922-933.
    • (2009) Proc. VLDB Endowment , vol.2 , Issue.1 , pp. 922-933
    • Abouzeid, A.1
  • 2
    • 79960166230 scopus 로고    scopus 로고
    • Reining in the outliers in map-reduce clusters using Mantri
    • ANANTHANARAYANAN, G., ET AL. Reining in the outliers in map-reduce clusters using Mantri. In Proc. OSDI 2010 (2010).
    • (2010) Proc. OSDI 2010
    • Ananthanarayanan, G.1
  • 3
    • 84870497669 scopus 로고    scopus 로고
    • Disk-locality in datacenter computing considered irrelevant
    • ANANTHANARAYANAN, G., ET AL. Disk-Locality in Datacenter Computing Considered Irrelevant. In Proc. HotOS (2011), p. 1.
    • (2011) Proc. HotOS , pp. 1
    • Ananthanarayanan, G.1
  • 4
    • 79955970532 scopus 로고    scopus 로고
    • Scarlett: Coping with skewed content popularity in mapreduce clusters
    • ANANTHANARAYANAN, G., ET AL. Scarlett: coping with skewed content popularity in mapreduce clusters. In Proc. EuroSys (2011), pp. 287-300.
    • (2011) Proc. EuroSys , pp. 287-300
    • Ananthanarayanan, G.1
  • 6
    • 79957872898 scopus 로고    scopus 로고
    • Hyracks: A flexible and extensible foundation for data-intensive computing
    • BORKAR, V., ET AL. Hyracks: A flexible and extensible foundation for data-intensive computing. In Proc. ICDE (2011), pp. 1151-1162.
    • (2011) Proc. ICDE , pp. 1151-1162
    • Borkar, V.1
  • 7
    • 79956351190 scopus 로고    scopus 로고
    • Haloop: Efficient iterative data processing on large clusters
    • BU, Y., ET AL. HaLoop: Efficient iterative data processing on large clusters. Proc. VLDB Endowment 3, 1-2 (2010), 285-296.
    • (2010) Proc. VLDB Endowment , vol.3 , Issue.1-2 , pp. 285-296
    • Bu, Y.1
  • 8
    • 77957570704 scopus 로고    scopus 로고
    • Flumejava: Easy, efficient data-parallel pipelines
    • CHAMBERS, C., ET AL. FlumeJava: Easy, efficient data-parallel pipelines. In Proc. PLDI (2010), pp. 363-375.
    • (2010) Proc. PLDI , pp. 363-375
    • Chambers, C.1
  • 9
    • 80053134218 scopus 로고    scopus 로고
    • Managing data transfers in computer clusters with Orchestra
    • CHOWDHURY, M., ET AL. Managing data transfers in computer clusters with Orchestra. In Proc. SIGCOMM (2011), p. 98.
    • (2011) Proc. SIGCOMM , pp. 98
    • Chowdhury, M.1
  • 10
    • 77954780115 scopus 로고    scopus 로고
    • MapReduce online
    • CONDIE, T., ET AL. MapReduce online. In Proc. NSDI (2010), pp. 21-21.
    • (2010) Proc. NSDI , pp. 21
    • Condie, T.1
  • 11
    • 30344488259 scopus 로고    scopus 로고
    • MapReduce: Simplified data processing on large clusters
    • DEAN, J., ET AL. MapReduce: Simplified Data Processing on Large Clusters. In Proc. OSDI (2004), p. 10.
    • (2004) Proc. OSDI , pp. 10
    • Dean, J.1
  • 12
    • 85051353062 scopus 로고    scopus 로고
    • Hadoop++: Making a yellow elephant run like a cheetah (without it even noticing)
    • DITTRICH, J., ET AL. Hadoop++: Making a yellow elephant run like a cheetah (without it even noticing). In Proc. VLDB (2010).
    • (2010) Proc. VLDB
    • Dittrich, J.1
  • 13
    • 78650003594 scopus 로고    scopus 로고
    • Twister: A runtime for iterative mapreduce
    • EKANAYAKE, J., ET AL. Twister: a runtime for iterative mapreduce. In Proc. HPDC (2010), pp. 810-818.
    • (2010) Proc. HPDC , pp. 810-818
    • Ekanayake, J.1
  • 14
    • 84857174691 scopus 로고    scopus 로고
    • IHadoop: Asynchronous iterations for MapReduce
    • ELNIKETY, E., ET AL. iHadoop: asynchronous iterations for MapReduce. In Proc. CloudCom (2011), pp. 81-90.
    • (2011) Proc. CloudCom , pp. 81-90
    • Elnikety, E.1
  • 15
    • 84871124446 scopus 로고    scopus 로고
    • TidyFs: A simple and small distributed filesystem
    • FETTERLY, D., ET AL. TidyFS: a simple and small distributed filesystem. In Proc. USENIX ATC (2011).
    • (2011) Proc. USENIX ATC
    • Fetterly, D.1
  • 16
    • 67650302322 scopus 로고    scopus 로고
    • Sector and sphere: The design and implementation of a high-performance data cloud
    • GU, Y., ET AL. Sector and Sphere: the design and implementation of a high-performance data cloud. Phil. trans. A, Math., phys., eng. sciences 367, 1897 (2009), 2429-45.
    • (2009) Phil. Trans. A, Math., Phys., Eng. Sciences , vol.367 , Issue.1897 , pp. 2429-2445
    • Gu, Y.1
  • 17
    • 77954920597 scopus 로고    scopus 로고
    • CoMET: Batched stream processing for data intensive distributed computing
    • HE, B., ET AL. Comet: batched stream processing for data intensive distributed computing. In Proc. SoCC (2010), pp. 63-74.
    • (2010) Proc. SoCC , pp. 63-74
    • He, B.1
  • 18
    • 84868343915 scopus 로고    scopus 로고
    • Mesos: A platform for fine-grained resource sharing in the data center
    • HINDMAN, B., ET AL. Mesos: A platform for fine-grained resource sharing in the data center. In Proc. NSDI (2011).
    • (2011) Proc. NSDI
    • Hindman, B.1
  • 19
    • 34548041192 scopus 로고    scopus 로고
    • Dryad: Distributed data-parallel programs-from sequential building blocks
    • ISARD, M., ET AL. Dryad: distributed data-parallel programs-from sequential building blocks. SIGOPS OSR 41, 3 (2007), 59.
    • (2007) SIGOPS OSR , vol.41 , Issue.3 , pp. 59
    • Isard, M.1
  • 20
    • 72249118633 scopus 로고    scopus 로고
    • Quincy: Fair scheduling for distributed computing clusters
    • ISARD, M., ET AL. Quincy: fair scheduling for distributed computing clusters. In Proc. SOSP (2009), pp. 261-276.
    • (2009) Proc. SOSP , pp. 261-276
    • Isard, M.1
  • 21
    • 77954901315 scopus 로고    scopus 로고
    • An analysis of traces from a production MapReduce cluster
    • KAVULYA, S., ET AL. An Analysis of Traces from a Production MapReduce Cluster. In Proc. CCGRID (2010), pp. 94-103.
    • (2010) Proc. CCGRID , pp. 94-103
    • Kavulya, S.1
  • 22
    • 84958083264 scopus 로고    scopus 로고
    • Multicore OS benchmarks: We can do better
    • KUZ, I., ET AL. Multicore OS benchmarks: we can do better. Proc. HotOS (2011).
    • (2011) Proc. HotOS
    • Kuz, I.1
  • 23
    • 78650877494 scopus 로고    scopus 로고
    • CloudCMP: Comparing public cloud providers
    • LI, A., ET AL. CloudCmp: comparing public cloud providers. In Proc. IMC (2010), pp. 1-14.
    • (2010) Proc. IMC , pp. 1-14
    • Li, A.1
  • 24
    • 79961137349 scopus 로고    scopus 로고
    • Cloud MapReduce: A MapReduce implementation on top of a cloud operating system
    • LIU, H., ET AL. Cloud MapReduce: A MapReduce Implementation on Top of a Cloud Operating System. In Proc. CCGRID (2011), pp. 464-474.
    • (2011) Proc. CCGRID , pp. 464-474
    • Liu, H.1
  • 25
    • 84863735533 scopus 로고    scopus 로고
    • Distributed Graphlab: A framework for machine learning and data mining in the cloud
    • LOW, Y., ET AL. Distributed GraphLab: a framework for machine learning and data mining in the cloud. Proc. VLDB Endow. 5, 8 (2012), 716-727.
    • (2012) Proc. VLDB Endow. , vol.5 , Issue.8 , pp. 716-727
    • Low, Y.1
  • 26
    • 77954723629 scopus 로고    scopus 로고
    • Pregel: A system for large-scale graph processing
    • MALEWICZ, G., ET AL. Pregel: a system for large-scale graph processing. In Proc. SIGMOD (2010), pp. 135-146.
    • (2010) Proc. SIGMOD , pp. 135-146
    • Malewicz, G.1
  • 27
    • 85031898917 scopus 로고    scopus 로고
    • Towards characterizing cloud backend workloads: Insights from Google compute clusters
    • MISHRA, A., ET AL. Towards characterizing cloud backend workloads: insights from Google compute clusters. SIGMETRICS Perf. Eval. Review 37, 4 (2010), 34-41.
    • (2010) SIGMETRICS Perf. Eval. Review , vol.37 , Issue.4 , pp. 34-41
    • Mishra, A.1
  • 29
    • 84862779280 scopus 로고    scopus 로고
    • CIEL: A universal execution engine for distributed data-flow computing
    • MURRAY, D. G., ET AL. CIEL: a universal execution engine for distributed data-flow computing. In Proc. NSDI (2011).
    • (2011) Proc. NSDI
    • Murray, D.G.1
  • 30
    • 33745942001 scopus 로고    scopus 로고
    • The Panasas ActiveScale storage cluster: Delivering scalable high bandwidth storage
    • NAGLE, D., ET AL. The Panasas ActiveScale storage cluster: Delivering scalable high bandwidth storage. In Proc. SC (2004).
    • (2004) Proc. SC
    • Nagle, D.1
  • 31
    • 55349148888 scopus 로고    scopus 로고
    • Pig Latin: A not-so-foreign language for data processing
    • OLSTON, C., ET AL. Pig Latin: a not-so-foreign language for data processing. In Proc. SIGMOD (2008), pp. 1099-1110.
    • (2008) Proc. SIGMOD , pp. 1099-1110
    • Olston, C.1
  • 32
    • 85080496495 scopus 로고    scopus 로고
    • Nova: Continuous Pig/Hadoop workflows
    • OLSTON, C., ET AL. Nova: continuous Pig/Hadoop workflows. In Proc. SIGMOD (2011).
    • (2011) Proc. SIGMOD
    • Olston, C.1
  • 33
    • 70350512695 scopus 로고    scopus 로고
    • A comparison of approaches to large-scale data analysis
    • PAVLO, A., ET AL. A comparison of approaches to large-scale data analysis. In Proc. SIGMOD (2009), pp. 165-178.
    • (2009) Proc. SIGMOD , pp. 165-178
    • Pavlo, A.1
  • 34
    • 84862798988 scopus 로고    scopus 로고
    • Piccolo: Building fast, distributed programs with partitioned tables
    • POWER, R., ET AL. Piccolo: building fast, distributed programs with partitioned tables. In Proc. OSDI (2010).
    • (2010) Proc. OSDI
    • Power, R.1
  • 36
    • 85080577621 scopus 로고    scopus 로고
    • Nobody ever got fired for using Hadoop
    • ROWSTRON, A., ET AL. Nobody ever got fired for using Hadoop. In Proc. HotCDP (2012).
    • (2012) Proc. HotCDP
    • Rowstron, A.1
  • 37
    • 80053503082 scopus 로고    scopus 로고
    • Runtime measurements in the cloud: Observing, analyzing, and reducing variance
    • SCHAD, J., ET AL. Runtime measurements in the cloud: observing, analyzing, and reducing variance. Proc. VLDB Endowment 3, 1-2 (2010), 460-471.
    • (2010) Proc. VLDB Endowment , vol.3 , Issue.1-2 , pp. 460-471
    • Schad, J.1
  • 38
    • 85080593269 scopus 로고    scopus 로고
    • Modeling and synthesizing task placement constraints in Google compute clusters
    • SHARMA, B., ET AL. Modeling and synthesizing task placement constraints in Google compute clusters. Proc. SoCC (2011).
    • (2011) Proc. SoCC
    • Sharma, B.1
  • 39
    • 77957838299 scopus 로고    scopus 로고
    • The Hadoop distributed file system
    • SHVACHKO, K., ET AL. The Hadoop distributed file system. In Proc. IEEE MSST (2010), pp. 1-10.
    • (2010) Proc. IEEE MSST , pp. 1-10
    • Shvachko, K.1
  • 40
    • 77952775707 scopus 로고    scopus 로고
    • Hive - A petabyte scale data warehouse using Hadoop
    • THUSOO, A., ET AL. Hive - a petabyte scale data warehouse using Hadoop. In Proc. ICDE (2010), pp. 996-1005.
    • (2010) Proc. ICDE , pp. 996-1005
    • Thusoo, A.1
  • 41
    • 85076883048 scopus 로고    scopus 로고
    • Improving MapReduce performance in heterogeneous environments
    • ZAHARIA, M., ET AL. Improving MapReduce performance in heterogeneous environments. In Proc. OSDI (2008), pp. 29-42.
    • (2008) Proc. OSDI , pp. 29-42
    • Zaharia, M.1
  • 42
    • 77954636142 scopus 로고    scopus 로고
    • Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling
    • ZAHARIA, M., ET AL. Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling. In Proc. EuroSys (2010), pp. 265-278.
    • (2010) Proc. EuroSys , pp. 265-278
    • Zaharia, M.1
  • 43
    • 79960813884 scopus 로고    scopus 로고
    • Spark: Cluster computing with working sets
    • ZAHARIA, M., ET AL. Spark: Cluster computing with working sets. In Proc. HotCloud (2010), p. 10.
    • (2010) Proc. HotCloud , pp. 10
    • Zaharia, M.1
  • 44
    • 85080624873 scopus 로고    scopus 로고
    • Imapreduce: A distributed computing framework for iterative computation
    • ZHANG, Y., ET AL. iMapReduce: A Distributed Computing Framework for Iterative Computation. In Proc. IPDPS (2011).
    • (2011) Proc. IPDPS
    • Zhang, Y.1
  • 45
    • 82155168650 scopus 로고    scopus 로고
    • Priter: A distributed framework for prioritized iterative computations
    • ZHANG, Y., ET AL. PrIter: a distributed framework for prioritized iterative computations. In Proc. SOCC (2011), ACM, p. 13.
    • (2011) Proc. SOCC , pp. 13
    • Zhang, Y.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.