1 / 29

Storage Management in Virtualized Cloud Environments

Storage Management in Virtualized Cloud Environments. Sankaran Sivathanu, Ling Liu, Mei Yiduo and Xing Pu. Student Workshop on Frontiers of Cloud Computing, IBM 2010. Talk Outline. Introduction Measurement results & Observations Data Placement & Provisioning Workload Interference

jgillis
Download Presentation

Storage Management in Virtualized Cloud Environments

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Storage Management in Virtualized Cloud Environments Sankaran Sivathanu, Ling Liu, Mei Yiduo and Xing Pu Student Workshop on Frontiers of Cloud Computing, IBM 2010

  2. Talk Outline • Introduction • Measurement results & Observations • Data Placement & Provisioning • Workload Interference • Impacts of Virtualization • Summary

  3. Cloud & Virtualization • Cloud Environment – Goals • Flexibility in resource configuration • Maximum resource utilization • Pay-per-use Model • Virtualization – Benefits • Resource consolidation • Re-structuring flexibility • Separate protection domains • Virtualization suits as one of the basic foundations of Cloud infrastructures

  4. Fundamental Issues • Could Service Providers (CSPs) vs. Customers • Customers purchase computing resources • CSPs provide virtual resources (VMs) • Customers perceive their resources as physical machines! • Multiple VMs reside in single physical host • Resource Interference • End-user performance depends on other users • End-user unaware of where their data physically exists

  5. Goals of our Measurement • For cloud service providers • How to place data such that end-user performance is maximized ? • How to co-locate workloads for least interference ? • For End-Users • How to purchase resources in tune with requirement ? • How to tune applications for maximum performance ? • General insights on storage I/O in virtualized environments

  6. Benchmarks Used • Postmark • Mail Server Workload • Create/Delete, Read/Append files • Parameters • File Size • # of files • Read/Write ratio • Synthetic Workload • Sequential vs. random accesses • Zipf Distribution

  7. Data Provisioning & Placement

  8. Disk Provisioning Consider a 100GB Disk Case - I Case - II Workload Data footprint ~150MB 40GB Partition 4GB Partition Throughput : 1.4 MB/s Throughput : 2.1 MB/s Performance Difference : 33%

  9. Where to place VM disk ? • Postmark benchmark • Read operation • Cases : • Read from physical partitions in different zones • Based on LBNs • LBNs start from inner zone and proceeds towards outer zones. • Read from disk file (.vmdk)

  10. Where to place multiple VM disks ? • Postmark benchmark • 2 instances (1 for each VM) • Random reads • Compare physical partitions placed in different zones • O -> Outer • I -> Inner • M -> Mid

  11. Observations • Customers should purchase storage based on workload requirement, not price • Thin provisioning may be practiced • Throughput intensive VMs can be placed in outer disk zones • Multiple VMs that may be accessed simultaneously should be co-located on disk • CSPs can monitor access patterns and move virtual disks accordingly

  12. Workload Interference

  13. CPU-Disk Interference Physical Host VM - 1 VM - 2 CPU CPU CPU DISK DISK DISK Throughput : 23.4 MB/s Throughput : 27.6 MB/s Performance Difference : 15.3%

  14. CPU-Disk Interference • CPU allocation ratios has no effect on disk throughput across VMs • Disk intensive job performs better along with a CPU intensive job

  15. Reason ? CPU-Disk Interference Dynamic Frequency Scaling

  16. CPU-Disk Interference • CPU DFS is enabled in Linux by default • Three ‘governors’ to control the DFS policy • On-demand (default) • Performance • Power-save • When 1 core is idle, entire CPU is down-scaled because overall CPU utilization falls

  17. Disk-Disk Interference • 1 instance of Postmark in each VMs • 65.3% more time taken when compared to running Postmark in a single VM • Overhead mainly attributed to disk seeks : No more sequential accesses CPU CPU VM-1 VM-2 V.Disk-1 V.Disk-2 Physical Host Physical Disk

  18. Disk-Disk Interference • VMs using separate physical disks • 17.52% more time taken when compared to running Postmark in a single VM • Overhead attributed to contention in Dom-0’s queue structures CPU CPU VM-1 VM-2 V.Disk-1 V.Disk-2 Physical Host Disk - 1 Disk - 2

  19. Disk-Disk Interference • Postmark Benchmark (Reads) • Cases : • Running in a single VM • 1 instance in each of two VMs • 2 VMs reading from virtual disks in same physical disk • 2 VMs reading from virtual disks in different physical disks

  20. Disk-Disk Interference • IO scheduling policy in Dom-0 has less effect • ‘Ideal’ case is time taken when running Postmark in single VM • Other cases are running 1 instance of Postmark in each of 2 VMs (separate physical disks)

  21. Disk-Disk Interference • Interference with respect to workload type • Synthetic read workload • VMs use separate physical disks • Cases : • Mix of sequential versus random reads • Sequential requests from both VMs flood Dom-0 queue - contention

  22. Observations • CPU-intensive and disk-intensive workloads can be co-located for optimal performance and power • Virtual disks that may be accessed simultaneously must be placed in separate physical disks • I/O scheduling in Dom-0 has less effect on disk workload interference • Two sequential workloads, when co-located suffer in performance due to queue contention • With separate disks, workload contention is generally minimal, other than the case of two sequential workloads

  23. Impacts of Virtualization

  24. Sequentiality • Postmark benchmark (reads) • No much overhead seen for random disk accesses • VM overhead is mitigated by larger disk overhead • More felt for sequential disk accesses

  25. Block Size • Postmark sequential reads • Fixed overhead with every requests • As block sizes increase, # of requests are reduced, hence overhead is reduced • Efficient to read in larger blocks

  26. Block size wrt. Locality

  27. Observations • VM overhead is not felt in random workloads – amortized by disk seeks • Extra layers of indirection is the reason for VM overhead – when block size is large, overhead is amortized • Block size may be increased only if there is sufficient locality in access

  28. Summary • Storage purchased must depend on requirement, not price! • It is better to place sequentially accessed streams in outer disk zone • Co-locate virtual disks that may be accessed simultaneously • Co-locate CPU intensive task with disk intensive task for better power and performance • Avoid co-locating two sequential workloads on single physical machine – even when it goes to separate physical disks! • Read in large blocks only when there is locality in workload

  29. Questions Contact : sankaran@gatech.edu

More Related