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Real-Time Cloud Computing
ChenyangLuCyber-PhysicalSystemsLaboratoryDepartmentofComputerScienceandEngineeringh<p://www.cse.wustl.edu/~lu/
Internet of Things
Ø Convergence of q Miniaturized hardware: integrate processor, sensors and radios.q Low-power wireless: connect millions of devices to the Internet.q Data analytics: make sense of sensor data.q Rea-time cloud: scalable real-time computing.
Ø Enable real-time control of physical environments
R.Dor,G.Hackmann,Z.Yang,C.Lu,Y.Chen,M.KollefandT.C.Bailey,ExperienceswithanEnd-To-EndWirelessClinicalMonitoringSystem,ConferenceonWirelessHealth(WH'12),October2012.
Real-TimeCloud
Ø Internet of Things à large-scale sensing and controlq Real-time data analytics (aka Big Data)
q Smart manufacturing, smart transportation, smart grid…
Ø Example: Intelligent Transportationq Data center collects data from cameras and roadside detectors.
q Control traffic signals and message signs in real-time.
q Transportation information feed to drivers.
q SCATS @ Sydney: controlling 3,400 signals at 1s round-trip latency.
Ø Latency-sensitive applications, e.g., cloud gamingq Xbox One: cloud offloading computation of environmental elementsq Sony acquired Gaikai, an open cloud gaming platform.
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EmbeddedSystemVirtualiza7on
Ø Consolidate 100 ECUs à ~10 multicore processors.Ø Integrate multiple systems on a common platform.
q Infotainment on Linux or Android
q Safety-critical control on AUTOSARq Virtualization: COQOS, Integrity Multivisor, Xen automotive
Ø Must preserve real-time performance on a virtualized platform!
10/14/16 4
Source:h<p://www.edn.com/design/automoYve/4399434/MulYcore-and-virtualizaYon-in-automoYve-environments
Ø Existing hypervisors provide no guarantee on latencyq Xen: credit scheduler, [credit, cap]
q VMware ESXi: [reservation, share, limitation]
q Microsoft Hyper-V: [reserve, weight, limit]
Ø Clouds lack service level agreement on latencyq EC2, Compute Engine, Azure: #VCPUs
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Cloudisreal-7metoday
Current clouds provision resources, not latency!
TowardsReal-TimeCloudsØ Support real-time applications in the cloud.
q Latency guarantees for tasks running in virtual machines (VMs).
q Real-time performance isolation between VMs.
q Resource sharing between real-time and non-real-time VMs.
Ø Multi-level real-time performance provisioning.q RT-Xen à real-time VM scheduling in a virtualized host.
q VATC à real-time network I/O in a virtualized host.q RT-OpenStack à real-time cloud resource management.
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VATC: Real-Time Communication
RT-O
penS
tack
XenVirtualiza7onArchitecture
Ø Xen: type-1, baremetal hypervisor q Domain-0: drivers, tool stack to control VMs.
q Guest Domain: para-virtualized or fully virtualized OS.
Ø Scheduling hierarchyq Xen schedules VCPUs on PCPUs.
q Guest OS schedules threads on VCPUs.
q Xen credit scheduler: round-robin with proportional share.
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PCPUs
OS Sched
Xen Scheduler
OS Sched OS Sched
VCPUReal-TimeTask
RT-Xen
Ø Real-time schedulers in the Xen hypervisor.Ø Provide real-time guarantees to tasks in VMs.
Ø Incorporated in Xen 4.5 as the real-time scheduler.
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RT-Xen
h<ps://sites.google.com/site/realYmexen/
S. Xi, M. Xu, C. Lu, L. Phan, C. Gill, O. Sokolsky and I. Lee, Real-Time Multi-Core Virtual Machine Scheduling in Xen, ACM International Conference on Embedded Software (EMSOFT'14), October 2014.
Composi7onalScheduling
Ø Analytical real-time guarantees to tasks running in VMs.Ø VM resource interfaces
q A set of VCPUs each with resource demand <period, budget >
q Hides task-specific informationq Computed based on compositional scheduling analysis
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Resource Interface Resource Interface
Resource Interface
Hypervisor
Virtual Machines
Workload Workload
Scheduler
Scheduler
Scheduler
Real-TimeSchedulerDesignØ Global scheduling
q Allow VCPU migration across cores
q Work conserving – utilize any available cores
q Migration overhead and cache penalty
Ø Partitioned schedulingq Assign and bind VCPUs to cores
q Cores may idle when others have work pending
q No migration overhead or cache penalty
Ø Enforce resource interface through budget managementq Periodic server vs. deferrable server
Ø Priority: Earliest Deadline First vs. Deadline Monotonic
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RT-Xenvs.Xen
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• Xen misses deadlines at 22% of CPU capacity.
• RT-Xen delivers real-time performance at 78% of CPU capacity.
VirtualizedNetworkI/O
Ø Xen handles all network traffic through Dom0Ø Real-time and non-real-time traffic share Dom0
q CPU and network contention
Ø Long delays for real-time traffic in virtualized hosts
XenHypervisor
NetworkComponents
Non Real-Time
App
Dom2
CPUMemory Storage
Real-Time App
Dom1Dom0
NIC
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NetworkI/OinVirtualizedHostsØ Linux Queueing Discipline
q Rate-limit and shape flows
q Prioritization or fair packet scheduling
Ø Priority inversion in virtualization componentsq between transmissionsq between transmission and reception
Ø VATC: Virtualization-Aware Traffic Controlq Process packets in prioritized kernel threadsq Dedicated packet queues per priority NIC
QueueingDiscipline
Dom0
Real-Time App
Dom1Non- Real-Time App
Dom2
Virtualiza7onComponents
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C. Li, S. Xi, C. Lu, C. Gill and R. Guerin, Prioritizing Soft Real-Time Network Traffic in Virtualized Hosts Based on Xen, IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'15), April 2015.
Real-TimeTrafficLatency
VATC reduces priority inversion à lower latency for real-time traffic.
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10 16 32 64 128 256 512 1024 Dyn Cons Dyn0
0.5
1
1.5
2
2.5
3
3.5
4
Roun
d−tri
p La
tenc
y ( m
s)
Interrupt Interval (µs)
Prio, Dom0−3.18FQ_CoDel, Dom0−3.18VATC
• Medianround-triplatencyofreal-Ymetraffic.• CPUcontenYonfromtwosmall-packetinterferingstreams.
VirtualizedHostàCloud
Ø Provide real-time performance to real-time VMsØ Achieve high resource utilization
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OpenStackLimita7ons
Ø Popular open-source cloud management system
Ø VM resource interfaceq Number of VCPUsq Not real-time
Ø VM-to-host mappingq Filtering (admission control)
• VCPU-to-PCPU ratio (16:1), max VMs per host (50)
• Coarse-grained admission control for CPU resources
q Ranking (VM allocation)
• Balance memory usage
• No consideration of CPU resources
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Manager
Host Host Host
VM VM VM VM VM
RT-OpenStack
Ø Co-hosting real-time VMs with non-real-time VMs
Ø Deliver real-time performanceq Support RT-Xen resource interfaceq Real-time-aware VM-to-host mapping
Ø Achieve high resource utilizationq Co-locate non-real-time VMs with real-time VMsq Non-real-time VMs consume remaining resources without affecting the
real-time performance of real-time VMs
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S. Xi, C. Li, C. Lu, C. Gill, M. Xu, L. Phan, I. Lee, O. Sokolsky, RT-OpenStack: CPU Resource Management for Real-Time Cloud Computing, IEEE International Conference on Cloud Computing (CLOUD'15), June 2015.
RT-OpenStack:VM-to-HostMapping
Ø Admission control: RT-Filterq Accept real-time VMs based on real-time schedulability and memory
q Consider only accepted real-time VMs
Ø VM allocation: RT-Weigherq Balance CPU utilization
q Consider only accepted real-time VMs
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ResourceInterface AdmissionControl VMAlloca7on
Real-TimeVMs {<period,budget>} Schedulability+Memory CPUUYlizaYon
Non-Real-TimeVMs BestEffort Memory Memory
OpenStack
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13%
36%
31% 61% 37%
75%30%29%
47% 32%
73%HadoopfinishYme:314seconds
Ø Overload four hosts with real-time VMs à deadline misses. Ø Two hosts running non-real-time VMs only.
Ø Unbalanced distribution of real-time domains.
RT-OpenStack
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0%
0%0%
0%
0% 0%
0%
0%0% 0%
0%
HadoopfinishYme:435seconds
Ø Schedulability guarantees for real-time VMs à no deadline miss.Ø Distribute real-time VMs across hosts.
Ø Hadoop makes progress using remaining CPU resources.
ConclusionsØ New applications demand real-time cloud services.
q Internet-scale monitoring and control.
q Latency-sensitive cloud applications.
Ø Towards real-time cloudØ RT-Xen: real-time VM scheduling in virtualized hosts.Ø VATC: real-time network I/O in virtualized hosts.
Ø RT-OpenStack: real-time guarantees at high CPU utilization.
Ø RTM: real-time messaging.
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VATC: Real-Time Communication
RT-O
penS
tack
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