On 18 June 2012 18:17, Suresh S <suresh...@gmail.com> wrote: > Dear Sir/Madam, > > > I joined as a Research scholar(PhD) recently. > I am interested to do research in cloud computing. Last month i was attend > one workshop. > From that, i know about Hadoop. I am very much intrested to do research in > hadoop. > Please give some topics and problems to work. Thanks in advance. > *Regards* >
Given you are doing PhD, presumably you are expected to start with the reading of state of the art before diving into the depths of your own work For that reason, I'm attaching a .bib file containing papers you may want to read, This list is incomplete and biased towards work I was doing last year on data integrity within Hadoop -it omits all of Lamport's work on Distribute Computing, and all the classic RDBMs papers, the latter list including: [Chamberlin81] D Chamberlin et al., A History and Evaluation of System R, 1981. [Codd71] E. F Codd, *A Database Sublanguage Founded on the Relational Calculus*, 1971 [Date84]: C.J. Date, *A Critique of the SQL Database Language* 1984 Also: -everything from Google, Yahoo! Amazon and Microsoft Research groups, Facebook, etc. -the work done in the 1980s and early 1990s on "massively parallel" computers. They tried out a lot of designs there, some of which could have relevance again. Regarding working inside Hadoop itself, be aware that - The code is big, complicated and needs testing on large clusters. - It's in use in production, which makes people reluctant to accept large changes to the core There are some tactics to address that, especially if you are looking at the classic CS-hard problems of scheduling, data placement, etc - Work in your own scheduler - Use the block placement plugin - Find other plugin points, or help design one for the specific area you want to play in. - YARN lets you run completely different applications in a Hadoop cluster. Another thing to be aware of is that because of the R&D money being invested in the platform, sometimes it does change dramatically -and it is hard to compete with the efforts of a team of full time developers. For example, I've long complained that Hadoop wasn't that good in a virtual world. and last week VMWare published a patch that contains many tens of thousands of lines of code to address it. Anyone doing a PhD on the same problem would now be in trouble. This is why working on a related-but-higher-level stack such as Asterix or Stratosphere may be a good approach; another is to pick a specific application problem and look at implementing it within the Hadoop platform. Steve .bib file in no particular order: @Article{ Chen94:raid, author = "Peter M. Chen and Edward K. Lee and Garth A. Gibson and Randy H. Katz and David A. 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