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Exploiting Spatial Locality to Improve Disk Efficiency in Virtualized Environments

Exploiting Spatial Locality to Improve Disk Efficiency in Virtualized Environments. Xiao Ling 1 , Shadi Ibrahim 2 , Hai Jin 1 , Song Wu 1 , Songqiao Tao 1 1 Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology

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Exploiting Spatial Locality to Improve Disk Efficiency in Virtualized Environments

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  1. Exploiting Spatial Locality to Improve Disk Efficiency in Virtualized Environments Xiao Ling1, Shadi Ibrahim2, Hai Jin1, Song Wu1, Songqiao Tao 1 1Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology 2INRIA Rennes - Bretagne Atlantique Rennes, France

  2. Disk efficiency in virtualized environments • VMs with multiple OSs and applications running on a physical server • Disk I/O utilization impacts I/O performance of applications running on VMs • Disk efficiency depending on exploitation of spatial locality • Disk scheduling exploits spatial locality • Reducing disk seek and rotational overheads But achieving high spatial locality is a challenging task in a virtualized environment

  3. Why difficult? • Complicated I/O behavior of VMs • More than one process running on VMs (e.g. Virtual desktop, data intensive application)--mixed applications • Transparency of Virtualization Streaming App File editing Process A Process B Process C Process D Block layer Lacks : a goral view of I/O access patterns of processes in the virtualized environment Guest OS Guest OS Guest OS Hypervisor Software Shared disk

  4. Shoulders of Giants Studies on improving I/O performance of applications proceed us • Invasive mode scheduling • Selecting the disk scheduler pair within both the hypervisor and VMs according to access pattern of applications[ICPP’11, SIGOPS Oper. Syst. Rev. ’10] • An additional Hypervisor-to-VM interference • Non-invasive mode scheduling • Streaming scheduling [Fast’11], Antfarm[USENIX ATC’06] • All VM with similar read applications • Grabbing bandwidth among VMs • Analysis of data accesses of VMs • Only a specific(one) application is running within a VM

  5. What do we solve? • Considering mixed applications and the transparency feature of virtualization • Exploring the benefit of the spatial locality and regularity of data accesses • Disk scheduling how to exploit spatial locality to maximize disk efficiency while preserving the transparency of virtualization?

  6. Outline • Problem Description • Related Work • Observe Disk Access patterns of VMs • Prediction Model • Design of Pregather • Performance Evalution • Conclusions and Future Work

  7. Difference of Data Access Virtualized Environment Traditional Environment simultaneously accessing different parts of data blocks in the range of VM image space

  8. Experiment settings • Physical server • four quad-core 2.40GHz Xenon processor, • 22GB of memory and one dedicated SATA disk of 1TB • Xen 4.0.1 with kernel 2.6.18 , Ext3 file system • Configuration of VMs • RHEL5 with kernel 2.6.18, Ext3 file system, 1GB memory and 2 VCPU, 12GB virtual disk • Defaut Noop scheduler • workloads • Sysbench-file I/O: sequential read/write, random read/write

  9. Access Patterns of VMs Our observations: • Regions across VMs • requests from the same VM • Sub-regions within VM • different ranges and frequencies of access

  10. Access Patterns of VMs Regional Spatial Locality Sub-regional Spatial Locality Sub-regions without spatial locality

  11. Observations • Special spatial locality • Regional spatial locality->bounded by VM image • Sub-regional spatial locality->access patterns of applications • Ignoring of these spatial locality • Seeking among VM • increasing disk head seeks among sub-regions (e.g. CFQ, AS) • Our goal • taking advantage of special spatial locality to improve physical disk efficiency in the virtualized environment.

  12. How to exploit these spatial locality • Batch Processing requests with special spatial locality with adaptive non-working-conserving mode • Easy capturing regularity of regional spatial locality • Hardly perceiving the regularity of Sub-regional spatial locality due to transparency of virtualization The distribution of sub-regions with spatial locality? Access interval of these sub-regions? Prediction Model

  13. Outline • Problem Description • Related Work • Zoom Disk Access patterns of VMs • Prediction Model • Design of Pregather • Performance Evalution • Conclusions and Future Work

  14. Prediction Model • Challenges • the distribution of sub-regions with spatial locality is changing with time and the access patterns of applications • Interference from background processes running on a VM • different sub-regions may have different access regularity • Analyzing historical data access within a VM image to predict sub-regional spatial locality

  15. Prediction Model-vNavigator • Quantization of Access Frequency • contributions of historical requests for prediction • Temporal access-density of zone

  16. Prediction Model-vNavigator • Explore Sub-regional Spatial Locality • temporal access-density threshold of a VM where • Clustering zones

  17. Prediction Model-vNavigator • Access Regularity of Sub-regional Spatial Locality • The range of a sub-region unit • Future access interval of the sub-region unit is the average access interval where

  18. Design of Pregather • An adaptive non-work-conserving disk scheduling in the hypervisor • whether or not to dispatch the pending request without starving other requests. • How long wait for future request with spatial locality • A spatial-locality-aware heuristic algorithm • the regional spatial locality across VMs and the prediction of sub-regional spatial locality from the vNavigator model • Guide Pregather to make the decision • waiting time is less than seek time

  19. The SPLA Algorithm • Setting timer according to position of disk head • Whether setting Coarse waiting time for regional spatial locality • Whether setting Fine waiting time for sub-regional spatial locality • AvgD(VMx ) <D|neighor VM-LBA of completed request | • no pending request from the current serving VMx • CoarseTimer= • AvgT(VMx ) • pending request from the the current serving VMx • Existing SR(Ui ) including LBA of completed request • FineTimer= • ST (Ui )

  20. The SPLA Algorithm • Dispatching request or continuing to wait • Seektime(closest pending request, completed request) • Within coarse waiting time • Within fine waiting time • till over timer or deadline of pending request or a suitable new request • Dispatch the request and turn off timer • Seektime< • AvgT(VMx ) • Request from VMx OR • Seektime< • ST (Ui ) • LBA of Request in SR(Ui ) • Dispatch the request and turn off timer OR

  21. Implementation of Pregather In Xen-hosted platform Pregather allocates each VM an equal serving time slice and serves VMs in a round robin fashion

  22. Outline • Problem Description • Related Work • Zoom Disk Access patterns of VMs • Prediction Model • Design of Pregather • Performance Evolution • Conclusions and Future Work

  23. Performance Evolution • Goal of Experiments • Verifying the vNavigator model • the overall performance of Pregather for multiple VMs • Evaluating the overhead of memory • Setting Parameters • The size of zone: 2000; prediction window:20ms; λ: 2; • Time slice: 200ms • Benchmark • Sysbench-file I/O, hadoop, tpch

  24. Verification of vNavigator Model • The ratio of successful waiting • VM with Sequential applications has clear sub-regional locality (e.g. success ratio 90.3%) • VM with only random applications has weak sub-regional locality (e.g. success ration 80.4%) 10% 33% 31% 38% 22%

  25. Pregather for Multiple VMs • VMs with Different Access Patterns 1.6x 2.6x

  26. Pregather for Multiple VMs • Disk I/O efficiency for Data Intensive Applications ↑ 26% CFQ ↑ 28%AS ↑38%Deadline At Zero: Pregather: 65% CFQ: 53% AS: 36% ↓18% ↓20%

  27. Pregather for Multiple VMs • Disk I/O efficiency for Data Intensive Applications with other applications Compared with CFQ: Q2: ↓10%, Q19: ↓8%, Sort: ↓12% Pregather: 63%

  28. Pregather for Multiple VMs • Memory Overheads 916KB

  29. Conclusion and Future Work • Contributions • Observing regional spatial locality and sub-regional spatial locality • an intelligent prediction model to predict the regularity of sub-regional spatial locality • Pregather with a spatial-locality-aware heuristic algorithm in the hypervisor to improve disk I/O efficiency without any prior knowledge of applications • Future work • extend Pregather to enable an intelligent allocation of physical blocks • Qos guarantee for VMs

  30. Thanks!

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