|Author's Email Address
||This thesis had been viewed 5537 times. Download 117 times.|
||Computer Science and Engineering|
|Type of Document
||Reducing Communication Overhead and Computation Costs in a Cloud Network by Early Combination of Partial Results|
|Date of Defense
||This thesis describes a method of reducing communication overheads within the MapReduce infrastructure of a cloud computing environment. MapReduce is an framework for parallelizing the processing on massive data systems stored across a|
distributed computer network. One of the benefits of MapReduce is that the computation is usually performed on a computer (node) that holds the data file. Not
only does this approach achieve parallelism, but it also benefits from a characteristic common to many applications: that the answer derived from a computation is often smaller than the size of the input file.
Our new method benefits also from this feature. We delay the transmission of individual answers out a given node, so as to allow these answers to be combined locally, first. This combination has two advantages. First, it allows for a further reduction in the amount of data to ultimately transmit. And second, it allows for additional computation across files (such as a merge-sort).
There is a limit to the benefit of delaying transmission, however, because the reducer stage of MapReduce cannot begin its work until the nodes transmit their answers. We therefore consider a mechanism to allow the user to adjust the amount of delay before data transmission out of each node.
||Chung-nan Lee - chair|
CHUN-HUNG RICHARD LIN - co-chair
Steve W.Haga - advisor
Indicate in-campus at 3 year and off-campus access at 5 year.|
|Date of Submission