1 / 19

Quantifying and Improving I/O Predictability in Virtualized Systems

Quantifying and Improving I/O Predictability in Virtualized Systems. Cheng Li, Inigo Goiri , Abhishek Bhattacharjee , Ricardo Bianchini, Thu D. Nguyen. Problem. IaaS cloud providers (e.g., Amazon EC2) Virtualization to consolidate virtual machines Performance may vary with consolidation

dard
Télécharger la présentation

Quantifying and Improving I/O Predictability in Virtualized Systems

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. Quantifying and Improving I/O Predictability in Virtualized Systems Cheng Li, InigoGoiri, AbhishekBhattacharjee, Ricardo Bianchini, Thu D. Nguyen

  2. Problem • IaaS cloud providers (e.g., Amazon EC2) • Virtualization to consolidate virtual machines • Performance may vary with consolidation • Interference and variable resource allocation • Inconsistent and unpredictable performance VM VM VM VM VM VM Physical Machine Physical Machine Physical Machine

  3. Our solution • Virtualized systems with predictable performance • Consolidation should not affect throughput, response time • Predictability is different than isolation • Assignment of resources to VMs must be fixed at all times • New class of predictable-performance service • This paper: storage I/O predictability in VirtualFence

  4. Why? • Many users desire predictable performance • Streaming and gaming apps • Performance tuning, debugging, diagnosis • Proper app design (e.g., workflows, pipelines) • Predictability benefits providers • Can charge for exactly the resources used • Direct relationship between resources and performance • Predictability benefits users • Can implement apps that need predictability • Predictable cloud costs

  5. Outline • Motivation • Quantifying unpredictability • VirtualFence • Evaluation • Conclusions

  6. How to measure unpredictability? • Performance deviation • Relative change in performance • Stand-alone (PI) vs. co-located (PD) • Average throughput or average response time

  7. Quantifying performance deviation • Studied deviation across VMMs, storage devices, etc • I/O performance deviation is endemic • Some main sources of deviation: • Resource allocation policy (e.g., work-conserving) • Device-specific characteristics (e.g., SSD erasure) • More findings in Rutgers DCS-TR-697

  8. Outline • Motivation • Quantifying unpredictability • VirtualFence • Evaluation • Conclusions

  9. VirtualFence • Predictable-performance storage system for Xen Dom0 VirtualFence VM Disk Scheduler Virtual Device Driver Kernel SSD cache VMM

  10. VirtualFence techniques • Non-work-conserving time partitioning • Each VM is assigned a fixed amount of I/O time • Avoids interleaving requests from multiple VMs Stand-alone scenario Co-located scenario … VM1 ` ` ` VM1 ` ` ` T1 T2 T3 T4 T5 T6 T7 T8 … VM1 VM2 VM3 VM4 VM1 VM2 VM3 VM4 T1 T2 T3 T4 T5 T6 T7 T8

  11. VirtualFence techniques • Small SSD cache in front of the HDD • Targets the HDD seek at the beginning of each time slot • Non-work-conserving space partitioning • Fixed size SSD cache per VM • Guarantees each VM’s cache space share Users can purchase multiple time and space slots! VM 1 VM 2 VM 3 VM 4 SSD cache co-located: SSD cache stand-alone: VM 1

  12. Outline • Motivation • Quantifying unpredictability • VirtualFence • Evaluation • Conclusions

  13. Experimental environment • Aggressive consolidation (80%utilization) • Filebench workloads • Webserver: read-only • Mailserver: mixed reads/sync writes • Physical machine: 4-core Xeon, 1 SSD, 1 HDD (22ms) VM3 (24%) VM1( 8%) VM2 (24%) VM4 (24%)

  14. VirtualFence evaluation • VirtualFence benefits • Contribution of each technique • Impact of the workload • Absolute performance and VirtualFence overheads • Performance vs. deviation tradeoff

  15. VirtualFence results • VirtualFence combines all three techniques • Approaches the deviation of SSD+TP at lower cost VirtualFence SSD+TP

  16. Impact of number of time slots • More slots decrease deviation, degrade performance • Ideal: fewest slots that allow enough consolidation

  17. Impact of time slot length • Longer slots decrease deviation, degrade performance • Ideal: shortest slot that produces enough predictability

  18. Conclusions • Consolidation leads to unpredictability • VirtualFence • Software/hardware solution • Improves I/O predictability significantly • Provider selects best predictability vs. performance tradeoff • User rents as many slots as needed for good performance

  19. Q&A Quantifying and Improving I/O Predictability in Virtualized Systems Cheng Li, InigoGoiri, AbhishekBhattacharjee, Ricardo Bianchini, Thu D. Nguyen

More Related