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......@@ -427,38 +427,39 @@ We thank the first Spark users, including Tim Hunter, Lester Mackey, Dilip Josep
## 引用资料
[1] ApacheHive.http://hadoop.apache.org/hive.
[2] Scala.http://www.scala-lang.org.
[3] G.Ananthanarayanan,A.Ghodsi,S.Shenker,andI.Stoica. Disk-locality in datacenter computing considered irrelevant. In HotOS ’11, 2011.
[4] P.Bhatotia,A.Wieder,R.Rodrigues,U.A.Acar,and R. Pasquin. Incoop: MapReduce for incremental computations. In ACM SOCC ’11, 2011.
[5] R.BoseandJ.Frew.Lineageretrievalforscientificdata processing: a survey. ACM Computing Surveys, 37:1–28, 2005.
[6] S.BrinandL.Page.Theanatomyofalarge-scalehypertextual web search engine. In WWW, 1998.
[7] Y.Bu,B.Howe,M.Balazinska,andM.D.Ernst.HaLoop: efficient iterative data processing on large clusters. Proc. VLDB Endow., 3:285–296, September 2010.
[8] C.Chambers,A.Raniwala,F.Perry,S.Adams,R.R.Henry, R. Bradshaw, and N. Weizenbaum. FlumeJava: easy, efficient data-parallel pipelines. In PLDI ’10\. ACM, 2010.
[9] J.Cheney,L.Chiticariu,andW.-C.Tan.Provenancein databases: Why, how, and where. Foundations and Trends in Databases, 1（4）:379–474, 2009.
[10] J.DeanandS.Ghemawat.MapReduce:Simplifieddata processing on large clusters. In OSDI, 2004.
[11] J. Ekanayake, H. Li, B. Zhang, T. Gunarathne, S.-H. Bae, J. Qiu, and G. Fox. Twister: a runtime for iterative mapreduce. In HPDC ’10, 2010.
[12] P.K.Gunda,L.Ravindranath,C.A.Thekkath,Y.Yu,and L. Zhuang. Nectar: automatic management of data and computation in datacenters. In OSDI ’10, 2010.
[13] Z.Guo,X.Wang,J.Tang,X.Liu,Z.Xu,M.Wu,M.F. Kaashoek, and Z. Zhang. R2: an application-level kernel for record and replay. OSDI’08, 2008.
[14] T.Hastie,R.Tibshirani,andJ.Friedman.TheElementsof Statistical Learning: Data Mining, Inference, and Prediction. Springer Publishing Company, New York, NY, 2009.
[15] B.He,M.Yang,Z.Guo,R.Chen,B.Su,W.Lin,andL.Zhou. Comet: batched stream processing for data intensive distributed computing. In SoCC ’10.
[16] A.Heydon,R.Levin,andY.Yu.Cachingfunctioncallsusing precise dependencies. In ACM SIGPLAN Notices, pages 311–320, 2000.
[17] B.Hindman,A.Konwinski,M.Zaharia,A.Ghodsi,A.D. Joseph, R. H. Katz, S. Shenker, and I. Stoica. Mesos: A platform for fine-grained resource sharing in the data center. In NSDI ’11.
[18] T.Hunter,T.Moldovan,M.Zaharia,S.Merzgui,J.Ma,M.J. Franklin, P. Abbeel, and A. M. Bayen. Scaling the Mobile Millennium system in the cloud. In SOCC ’11, 2011.
[19] M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: distributed data-parallel programs from sequential building blocks. In EuroSys ’07, 2007\. [20] S.Y.Ko,I.Hoque,B.Cho,andI.Gupta.Onavailabilityof intermediate data in cloud computations. In HotOS ’09, 2009.
[21] D. Logothetis, C. Olston, B. Reed, K. C. Webb, and K. Yocum. Stateful bulk processing for incremental analytics. SoCC ’10.
[22] 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 SIGMOD, 2010.
[23] D.G.Murray,M.Schwarzkopf,C.Smowton,S.Smith, A. Madhavapeddy, and S. Hand. Ciel: a universal execution engine for distributed data-flow computing. In NSDI, 2011.
[24] B.NitzbergandV.Lo.Distributedsharedmemory:asurveyof issues and algorithms. Computer, 24（8）:52 –60, Aug 1991.
[25] J.Ousterhout,P.Agrawal,D.Erickson,C.Kozyrakis, J. Leverich, D. Mazie\`res, S. Mitra, A. Narayanan, G. Parulkar, M. Rosenblum, S. M. Rumble, E. Stratmann, and R. Stutsman. The case for RAMClouds: scalable high-performance storage entirely in DRAM. SIGOPS Op. Sys. Rev., 43:92–105, Jan 2010\.
[26] D.PengandF.Dabek.Large-scaleincrementalprocessingusing distributed transactions and notifications. In OSDI 2010.
[27] R.PowerandJ.Li.Piccolo:Buildingfast,distributedprograms with partitioned tables. In Proc. OSDI 2010, 2010.
[28] R.RamakrishnanandJ.Gehrke.DatabaseManagement Systems. McGraw-Hill, Inc., 3 edition, 2003.
[29] K.Thomas,C.Grier,J.Ma,V.Paxson,andD.Song.Designand evaluation of a real-time URL spam filtering service. In IEEE Symposium on Security and Privacy, 2011\.
[30] J.W.Young.Afirstorderapproximationtotheoptimum checkpoint interval. Commun. ACM, 17:530–531, Sept 1974.
[31] 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 OSDI ’08, 2008.
[32] M.Zaharia,D.Borthakur,J.SenSarma,K.Elmeleegy,S. Shenker, and I. Stoica. Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling. In EuroSys ’10, 2010.
[33] M.Zaharia,M.Chowdhury,T.Das,A.Dave,J.Ma,M. McCauley, M. Franklin, S. Shenker, and I. Stoica. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. Technical Report UCB/EECS-2011-82, EECS Department, UC Berkeley, 2011.
[1] ApacheHive.http://hadoop.apache.org/hive.
[2] Scala.http://www.scala-lang.org.
[3] G.Ananthanarayanan,A.Ghodsi,S.Shenker,andI.Stoica. Disk-locality in datacenter computing considered irrelevant. In HotOS ’11, 2011.
[4] P.Bhatotia,A.Wieder,R.Rodrigues,U.A.Acar,and R. Pasquin. Incoop: MapReduce for incremental computations. In ACM SOCC ’11, 2011.
[5] R.BoseandJ.Frew.Lineageretrievalforscientificdata processing: a survey. ACM Computing Surveys, 37:1–28, 2005.
[6] S.BrinandL.Page.Theanatomyofalarge-scalehypertextual web search engine. In WWW, 1998.
[7] Y.Bu,B.Howe,M.Balazinska,andM.D.Ernst.HaLoop: efficient iterative data processing on large clusters. Proc. VLDB Endow., 3:285–296, September 2010.
[8] C.Chambers,A.Raniwala,F.Perry,S.Adams,R.R.Henry, R. Bradshaw, and N. Weizenbaum. FlumeJava: easy, efficient data-parallel pipelines. In PLDI ’10\. ACM, 2010.
[9] J.Cheney,L.Chiticariu,andW.-C.Tan.Provenancein databases: Why, how, and where. Foundations and Trends in Databases, 1（4）:379–474, 2009.
[10] J.DeanandS.Ghemawat.MapReduce:Simplifieddata processing on large clusters. In OSDI, 2004.
[11] J. Ekanayake, H. Li, B. Zhang, T. Gunarathne, S.-H. Bae, J. Qiu, and G. Fox. Twister: a runtime for iterative mapreduce. In HPDC ’10, 2010.
[12] P.K.Gunda,L.Ravindranath,C.A.Thekkath,Y.Yu,and L. Zhuang. Nectar: automatic management of data and computation in datacenters. In OSDI ’10, 2010.
[13] Z.Guo,X.Wang,J.Tang,X.Liu,Z.Xu,M.Wu,M.F. Kaashoek, and Z. Zhang. R2: an application-level kernel for record and replay. OSDI’08, 2008.
[14] T.Hastie,R.Tibshirani,andJ.Friedman.TheElementsof Statistical Learning: Data Mining, Inference, and Prediction. Springer Publishing Company, New York, NY, 2009.
[15] B.He,M.Yang,Z.Guo,R.Chen,B.Su,W.Lin,andL.Zhou. Comet: batched stream processing for data intensive distributed computing. In SoCC ’10.
[16] A.Heydon,R.Levin,andY.Yu.Cachingfunctioncallsusing precise dependencies. In ACM SIGPLAN Notices, pages 311–320, 2000.
[17] B.Hindman,A.Konwinski,M.Zaharia,A.Ghodsi,A.D. Joseph, R. H. Katz, S. Shenker, and I. Stoica. Mesos: A platform for fine-grained resource sharing in the data center. In NSDI ’11.
[18] T.Hunter,T.Moldovan,M.Zaharia,S.Merzgui,J.Ma,M.J. Franklin, P. Abbeel, and A. M. Bayen. Scaling the Mobile Millennium system in the cloud. In SOCC ’11, 2011.
[19] M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: distributed data-parallel programs from sequential building blocks. In EuroSys ’07, 2007\.
[20] S.Y.Ko,I.Hoque,B.Cho,andI.Gupta.Onavailabilityof intermediate data in cloud computations. In HotOS ’09, 2009.
[21] D. Logothetis, C. Olston, B. Reed, K. C. Webb, and K. Yocum. Stateful bulk processing for incremental analytics. SoCC ’10.
[22] 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 SIGMOD, 2010.
[23] D.G.Murray,M.Schwarzkopf,C.Smowton,S.Smith, A. Madhavapeddy, and S. Hand. Ciel: a universal execution engine for distributed data-flow computing. In NSDI, 2011.
[24] B.NitzbergandV.Lo.Distributedsharedmemory:asurveyof issues and algorithms. Computer, 24（8）:52 –60, Aug 1991.
[25] J.Ousterhout,P.Agrawal,D.Erickson,C.Kozyrakis, J. Leverich, D. Mazie\`res, S. Mitra, A. Narayanan, G. Parulkar, M. Rosenblum, S. M. Rumble, E. Stratmann, and R. Stutsman. The case for RAMClouds: scalable high-performance storage entirely in DRAM. SIGOPS Op. Sys. Rev., 43:92–105, Jan 2010\.
[26] D.PengandF.Dabek.Large-scaleincrementalprocessingusing distributed transactions and notifications. In OSDI 2010.
[27] R.PowerandJ.Li.Piccolo:Buildingfast,distributedprograms with partitioned tables. In Proc. OSDI 2010, 2010.
[28] R.RamakrishnanandJ.Gehrke.DatabaseManagement Systems. McGraw-Hill, Inc., 3 edition, 2003.
[29] K.Thomas,C.Grier,J.Ma,V.Paxson,andD.Song.Designand evaluation of a real-time URL spam filtering service. In IEEE Symposium on Security and Privacy, 2011\.
[30] J.W.Young.Afirstorderapproximationtotheoptimum checkpoint interval. Commun. ACM, 17:530–531, Sept 1974.
[31] 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 OSDI ’08, 2008.
[32] M.Zaharia,D.Borthakur,J.SenSarma,K.Elmeleegy,S. Shenker, and I. Stoica. Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling. In EuroSys ’10, 2010.
[33] M.Zaharia,M.Chowdhury,T.Das,A.Dave,J.Ma,M. McCauley, M. Franklin, S. Shenker, and I. Stoica. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. Technical Report UCB/EECS-2011-82, EECS Department, UC Berkeley, 2011.
## 原文链接
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