
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>APPLICATION-AWARE OPTIMIZATIONS FOR BIG DATA ACCESS</dc:title>
  <dc:creator>Yin, Yanlong</dc:creator>
  <dc:description>Many High-Performance Computing (HPC) applications spend a significant portion of their execution time in accessing data from les and they are becoming increasingly data-intensive. For them, I/O performance is a significant bottleneck leading to wastage of CPU cycles and the corresponding wasted energy consumption. Various optimization techniques exist to improve data access performance. However, the existing general-purpose optimization techniques are not able to satisfy diverse applications&apos; demands. On the other hand, the application-specific optimization pro- cess is usually a difficult task due to the complexity involved in understanding the parallel I/O system and the applications&apos; I/O behaviors. To address these challenges, this thesis proposes an application-aware data access optimization framework and claims that it is feasible and useful to utilize applications&apos; characteristics to improve the performance and efficiency of the parallel I/O system. Under this framework, an optimization may consist of several basic but challenging steps, including capturing the application&apos;s characteristics, identifying the causality of I/O performance degra- dation, and delivering optimization solutions. To make these steps easier, we design and implement the IOSIG toolkit as an essential system support for the default par- allel I/O system. The toolkit is able to pro le the applications&apos; I/O behaviors and then generate comprehensive characteristics through trace analysis. With the help of IOSIG, we design several optimization techniques on data layout optimization, data reorganization, and I/O scheduling. The proposed framework has significant poten- tial to boost application-aware I/O optimization. The results prove that the proposed optimization techniques can significantly improve the data access performance.</dc:description>
  <dc:description>Ph.D. in Computer Science, July 2014</dc:description>
  <dc:contributor>Sun, Xian-He</dc:contributor>
  <dc:date>2014</dc:date>
  <dc:date>2014-07</dc:date>
  <dc:type>Dissertation</dc:type>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>islandora:9202</dc:identifier>
  <dc:identifier>http://hdl.handle.net/10560/3410</dc:identifier>
  <dc:source>CS / Computer Science</dc:source>
  <dc:source>Illinois Institute of Technology</dc:source>
  <dc:language>en</dc:language>
  <dc:rights>In Copyright</dc:rights>
  <dc:rights>http://rightsstatements.org/page/InC/1.0/</dc:rights>
  <dc:rights>Restricted Access</dc:rights>
</oai_dc:dc>