Workload-Aware Live Storage Migration for Clouds


The emerging open cloud computing model will provide users with great freedom to dynamically migrate virtualized computing services to, from, and between clouds over the wide-area. While this freedom leads to many potential benefits, the running services must be minimally disrupted by the migration. Unfortunately, current solutions for wide-area migration incur too much disruption as they will significantly slow down storage I/O operations during migration. The resulting increase in service latency could be very costly to a business.  
This research presents a novel storage migration scheduling algorithm that can greatly improve storage I/O performance during wide-area migration. Our algorithm is unique in that it considers individual virtual machine's storage I/O workload such as temporal locality, spatial locality and popularity characteristics to compute an efficient data transfer schedule. Using a fully implemented system on KVM and a trace-driven framework, we show that our algorithm provides large performance benefits across a wide range of popular virtual machine workloads.


Source code

Workload I/O Trace



This research was sponsored by NSF CAREER Award CNS-0448546, NeTS FIND CNS-0721990, NeTS CNS-1018807, by an IBM Faculty Award, an Alfred P. Sloan Research Fellowship, and by Microsoft Corp.  Jie Zheng is additionally supported by an IBM Scholarship. Views and conclusions contained in this research are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of NSF, IBM Corp., Microsoft Corp., the Alfred P. Sloan Foundation, or the U.S. government.


Jie Zheng
Department of Computer Science
Rice University