Minerva: An automated resource provisioning tool for large-scale storage systems
ACM Transactions on Computer Systems (TOCS)
Hippodrome: Running Circles Around Storage Administration
FAST '02 Proceedings of the Conference on File and Storage Technologies
Easy and Efficient Disk I/O Workload Characterization in VMware ESX Server
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
PARDA: proportional allocation of resources for distributed storage access
FAST '09 Proccedings of the 7th conference on File and storage technologies
Using TCP/IP traffic shaping to achieve iSCSI service predictability
LISA'10 Proceedings of the 24th international conference on Large installation system administration
Workload-aware database monitoring and consolidation
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
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Virtualization has been effective in providing performance isolation and proportional allocation of resources, such as CPU and memory between VMs by using automated distributed resource schedulers and VM migration. Storage VMotion allows users to migrate virtual hard disks from one data store to another without stopping the virtual machine. There is a dire need for an automated tool to manage storage resources more effectively by doing virtual disk placement and load balancing of workloads across multiple data stores. Applicable beyond virtualization, this problem is challenging because it requires modeling both workloads and characterizing underlying devices. Furthermore, device characteristics such as number of disks backing a LUN, disk types etc. are hidden from the hosts by the virtualization layer at the array. In this paper, we propose a storage resource scheduler (SRS) to manage virtual disk placement and automatic load balancing using Storage VMotion. Our initial results lead us to believe that we can effectively model workloads and devices to improve overall storage resource utilization in practice.