Sciweavers

Share
EUROPAR
2014
Springer

Understanding Vertical Scalability of I/O Virtualization for MapReduce Workloads: Challenges and Opportunities

5 years 5 months ago
Understanding Vertical Scalability of I/O Virtualization for MapReduce Workloads: Challenges and Opportunities
Abstract. 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 count per node, local storage comes under increasing I/O pressure itself and prompts the need to equip nodes with multiple disks. However, given the rising need to virtualize large datacenters in order to provide a more flexible allocation and consolidation of physical resources (transforming them into public or private/hybrid clouds), the following questions arise: is it possible to take advantage of multiple local disks at virtual machine (VM) level in order to speed up big data analyt...
Bogdan Nicolae
Added 27 Apr 2014
Updated 27 Apr 2014
Type Journal
Year 2014
Where EUROPAR
Authors Bogdan Nicolae
Comments (0)
books