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Quantifying and improving I/O predictability in virtualized systems

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5 Author(s)
Cheng Li ; Dept. of Comput. Sci., Rutgers Univ., Piscataway, NJ, USA ; Goiri, I. ; Bhattacharjee, A. ; Bianchini, R.
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Virtualization enables the consolidation of virtual machines (VMs) to increase the utilization of physical servers in Infrastructure-as-a-Service (IaaS) cloud providers. However, our experience shows that storage I/O performance varies wildly in the face of consolidation. Since many users may desire consistent performance, we argue that IaaS providers should offer a class of predictable-performance service in addition to existing (predictability-oblivious) services. Thus, we propose VirtualFence, a storage system that provides predictable VM performance. VirtualFence uses three main techniques: (1) non-work-conserving time-division I/O scheduling, (2) a small solid-state (SSD) cache in front of a much larger hard disk drive (HDD), and (3) space-partitioning of both the SSD cache and the HDD. Our evaluation shows that VirtualFence improves predictability significantly, while allowing cloud providers to reach any desired compromise between predictability and performance.

Published in:

Quality of Service (IWQoS), 2013 IEEE/ACM 21st International Symposium on

Date of Conference:

3-4 June 2013