1、,Xiao Ling1, Shadi Ibrahim2, Hai Jin1, Song Wu1, Songqiao Tao 11Cluster 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,Exploiting Spatial Lo
2、cality to Improve Disk Efciency in Virtualized Environments,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
3、 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,Why difficult?,Complicated I/O behavior of VMs More than one process running on VMs (e.g. Virtual desktop, data inten
4、sive application)-mixed applications Transparency of Virtualization,Block layer Lacks : a goral view of I/O access patterns of processes in the virtualized environment,Hypervisor,Software,Shared disk,Shoulders of Giants,Invasive mode scheduling Selecting the disk scheduler pair within both the hyper
5、visor and VMs according to access pattern of applicationsICPP11, SIGOPS Oper. Syst. Rev. 10 An additional Hypervisor-to-VM interference Non-invasive mode scheduling Streaming scheduling Fast11, AntfarmUSENIX ATC06 All VM with similar read applications Grabbing bandwidth among VMs Analysis of data ac
6、cesses of VMs Only a specific(one) application is running within a VM,Studies on improving I/O performance of applications proceed us,What do we solve?,Considering mixed applications and the transparency feature of virtualizationExploring the benefit of the spatial locality and regularity of data ac
7、cessesDisk scheduling how to exploit spatial locality to maximize disk efficiency while preserving the transparency of virtualization?,Outline,Problem Description Related Work Observe Disk Access patterns of VMs Prediction Model Design of Pregather Performance Evalution Conclusions and Future Work,D
8、ifference of Data Access,Traditional Environment,Virtualized Environment,simultaneously accessing different parts of data blocks in the range of VM image space,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
9、 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,Access Patterns of VMs,Regions across VMs requests from the same VM
10、Sub-regions within VM different ranges and frequencies of access,Our observations:,Access Patterns of VMs,Regional Spatial Locality,Sub-regional Spatial Locality,Sub-regions without spatial locality,Observations,Special spatial locality Regional spatial locality-bounded by VM image Sub-regional spat
11、ial 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.,How to exploit these
12、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-re
13、gions with spatial locality? Access interval of these sub-regions?,Prediction Model,Outline,Problem Description Related Work Zoom Disk Access patterns of VMs Prediction Model Design of Pregather Performance Evalution Conclusions and Future Work,Prediction Model,Challenges the distribution of sub-reg
14、ions 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 regularityAnalyzing historical data access within a VM image to predict sub-regional spatial locality,Predic
15、tion Model-vNavigator,Quantization of Access Frequency contributions of historical requests for prediction Temporal access-density of zone,Prediction Model-vNavigator,Explore Sub-regional Spatial Locality temporal access-density threshold of a VM where Clustering zones,Prediction Model-vNavigator,Ac
16、cess Regularity of Sub-regional Spatial Locality The range of a sub-region unit Future access interval of the sub-region unit,where,is the average access interval,Design of Pregather,An adaptive non-work-conserving disk scheduling in the hypervisor whether or not to dispatch the pending request with
17、out 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
18、 less than seek time,The SPLA Algorithm,Setting timer according to position of disk head Whether setting Coarse waiting time for regional spatial localityWhether setting Fine waiting time for sub-regional spatial locality,no pending request from the current serving VMx,AvgD(VMx ) D|neighor VM-LBA of
19、 completed request |,CoarseTimer= AvgT(VMx ),pending request from the the current serving VMx,Existing SR(Ui ) including LBA of completed request,FineTimer= ST (Ui ),The SPLA Algorithm,Dispatching request or continuing to wait Seektime(closest pending request, completed request) Within coarse waitin
20、g timeWithin fine waiting timetill over timer or deadline of pending request or a suitable new request,Seektime AvgT(VMx ),Request from VMx,Dispatch the request and turn off timer,OR,Seektime ST (Ui ),LBA of Request in SR(Ui ),Dispatch the request and turn off timer,OR,Implementation of Pregather,Pr
21、egather allocates each VM an equal serving time slice and serves VMs in a round robin fashion,In Xen-hosted platform,Outline,Problem Description Related Work Zoom Disk Access patterns of VMs Prediction Model Design of Pregather Performance Evolution Conclusions and Future Work,Performance Evolution,
22、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; : 2; Time slice: 200ms Benchmark Sysbench-file I/O, hadoop, tpch,Verification of vNavigator Mo
23、del,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%),33%,31%,38%,22%,10%,VMs with Different Access Patterns,Pregather for Multiple VMs
24、,1.6x,2.6x,Pregather for Multiple VMs,Disk I/O efficiency for Data Intensive Applications, 26% CFQ 28%AS 38%Deadline,18%,At Zero: Pregather: 65% CFQ: 53% AS: 36%,20%,Pregather for Multiple VMs,Disk I/O efficiency for Data Intensive Applications with other applications,Compared with CFQ: Q2: 10%, Q19
25、: 8%, Sort: 12%,Pregather: 63%,Pregather for Multiple VMs,Memory Overheads,916KB,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,Thanks!,
