A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Understanding Vertical Scalability of I/O Virtualization for MapReduce Workloads: Challenges and Opportunities
[chapter]
2014
Lecture Notes in Computer Science
As the explosion of data sizes continues to push the limits of our abilities to efficiently store and process big data, next generation big data systems face multiple challenges. One such important challenge relates to the limited scalability of I/O, a determining factor in the overall performance of big data applications. Although paradigms like MapReduce have long been used to take advantage of local disks and avoid data movements over the network as much as possible, with increasing core
doi:10.1007/978-3-642-54420-0_1
fatcat:t7isdcjlsvbnlkdn7qio6ta5ma