1、Xiao Ling1, Shadi Ibrahim2, Hai Jin1, Song Wu1, Songqiao Tao 11Cluster and Grid Computing LabServices Computing Technology and System LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology2INRIA Rennes - Bretagne AtlantiqueRennes, FranceExploiting Spatial Locality
2、to Improve Disk Efciency in Virtualized EnvironmentsDisk 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 localit
3、y Disk scheduling exploits spatial locality Reducing disk seek and rotational overheadsBut 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 intensive appl
4、ication)-mixed applications Transparency of VirtualizationBlock layer Lacks :a goral view of I/O access patterns of processes in the virtualized environment HypervisorSoftwareShared diskGuest OSStreaming AppFile editingGuest OSProcess AProcess BGuest OSProcess CProcess DShoulders of Giants Invasive
5、mode scheduling Selecting the disk scheduler pair within both the hypervisor 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 sim
6、ilar read applications Grabbing bandwidth among VMs Analysis of data accesses of VMs Only a specific(one) application is running within a VMStudies on improving I/O performance of applications proceed usWhat do we solve? Considering mixed applications and the transparency feature of virtualization E
7、xploring 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?Outline Problem Description Related Work Observe Disk Access patterns of VMs Prediction Model D
8、esign of Pregather Performance Evalution Conclusions and Future WorkDifference of Data AccessTraditional Environment Virtualized Environmentsimultaneously accessing different parts of data blocks in the range of VM image spaceExperiment settings Physical server four quad-core 2.40GHz Xenon processor
9、, 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/writeAccess Patterns of VMs Regions across VMs requests from the same VM Sub-regions within VM different ranges and frequencies of accessOur observations:Access Patterns of VMsRegional Spatial LocalitySub-regional Spatial LocalitySub-regions without spatial locality
