The document discusses the integration of cloud computing within cyber-physical systems (CPS), emphasizing the need for resilience in design and operation. It outlines various applications of CPS in fields such as healthcare and transportation while highlighting the importance of big data analytics in these systems. The paper also explores concepts such as resource pooling, performance evaluation, and the challenges of managing variability and real-time processing in a cloud environment.

1. Departmentof Measurementand InformationSystems
Budapest University of Technologyand Economics, Hungary
Measurement-Driven Resilience Design of Cloud-Based Cyber-Physical Systems
Imre Kocsis
ikocsis@mit.bme.hu
SERENE’14 AutumnSchool
2014.10.14.

2. A Viewof Cyber-PhysicalSystems

3. Cyber-PhysicalSystems (CPSs)
3
Ubiquitousembeddedand networkedsystems thatcan monitor and control the physical world witha high level of intelligenceand dependabilityNetworkedembeddedsystemseverywhereClouds, „infusable” analytics, Big Data

4. FromembeddedtoCPS
4
Directmanualcontrol,
„closedworld” engineering

5. FromembeddedtoCPS
5
Directmanualcontrol,
„closedworld” engineering
Highlyautonomous,
„cyber” backend, environment, swarms, …

6. FromembeddedtoCPS
6
Directmanualcontrol,
„closedworld” engineering
Highlyautonomous,
„cyber” backend, environment, swarms, …

7. Cyber-PhysicalSystems
Differentflavors
oNSF, EU, academia, industry…
Still: itis here
oFromsmartcities& IoTtoself- drivingcars
oScalable, reconfigurablebackendis a must
7
Health CareTransportationEnergy

8. „Classical” caseforcloudcomputing: a brainforaCPS
Video
surveillance
Citizen
devices
Env. sensors
…
Trafficcontrol
Situationalawareness
Deep analyticsNormaldayDisaster
See: Naphadeet. al(IBM), „SmarterCitiesand TheirInnovationChallanges”, Computer, 2011Elastic, reconfigurablecomputing Reconfiguration

9. Converging domains
CPS
Cloud
computing
Big Data
9

10. Detour1: CloudComputing

11. Cloudcomputing: leasedresources
Source: http://cloud.dzone.com/articles/introduction-cloud-computing

12. Definition?
NIST 800-145
Cloudcomputingisamodelforenablingubiquitous, convenient,on-demandnetworkaccesstoashared
poolofconfigurablecomputingresources(e.g.,networks, servers,storage,applications,andservices)that
canberapidlyprovisionedandreleasedwithminimalmanagementeffortorserviceproviderinteraction.

13. Properties
On-demandself-service
Broadnetworkaccess
Resourcepooling
Rapid elasticity
Measuredservice
13

14. Ontheproviderside… ~?

15. Whyis itgoodfortheprovider?
(WithoutCLT)
푋푖independentprob. Varswith휇andσ2
Coefficientof variation: 휎 휇
Exp. valueof sum: sumof exp. values
Varianceof sum: sumof variances
CV푋푠푢푚= 푛휎2 푛휇 = 1 푛 휎 휇 = 1 푛 퐶푉(푋푖)

16. „Statisticalmultiplexing”
Variancew.r.t. meangetssmaller
1 푛 : quick–smallerprivateclouds
Realityis a bit different
Source: http://en.wikipedia.org/wiki/Central_limit_theorem

17. Gartner, 2013
„For larger businesseswith existing internal data centers, well-managed virtualized infrastructure and efficient IT operations teams, IaaSfor steady-state workloads is often no less expensive, and may be more expensive, than an internal private cloud.”
„I needitnow, and needitfast…”?

18. Parallellizableloads
More and more embarrassinglyparallel, „scale- out” applicationcategoriesexist
NYT TimesMachine: publicdomainarchive
oConversiontoweb-friendlyformat: ApacheHadoop, a fewhundredVMs, 36 hours
Inthecloud: coststhesameaswithoneVM
Practically: „speedupforfree”

19. Scalingresources
„Scaleup”
„Scaleout”
oAlgorithmics?
o„webscale”technologies

20. Detour2: Big Data

21. 1.) Big Data atRest
Distributedstorage
„Computationtodata”
„Atrest Big Data”
oNo update
oNo sampling
„Not true, but a very, very good lie!”
(T.Pratchett, Nightwatch)

22. MapReduce(ApacheHadoop)
Distributed File System[ , ] [ , ] [ , ] [ , ] [ , ] [ , ] [ , ] [ , ] [ , ] [ , ] [ , ] [ , ] [ , ] [ , ] [ , ] [ ,[ , , ]] [ ,[ , , ]] [ ,[ , , ]] [ ,[ , , ]] [ ,[ , , ]] SHUFFLE Map Reduce [ , ][ , ][ , ][ , ][ , ]

23. 2.) „Big Data inMotion”
Streamprocessing
Inherentlyscalablethesameway

24. Streamingdata
Sensordata
oFromsmartgridtoturbinetesting
Images
oSatellites: nTB/day
Web services
Network traffic
Trading
…

25. The streamprocessormodel
Source: Rajaraman, A., & Ullman, J. D. (2011). Mining of Massive Datasets. Cambridge: Cambridge University Press. p130

26. Design & composition
Source: International Technical Support Organization. IBM InfoSphereStreams: Harnessing Data in Motion. September 2010, p76

27. Whenwehavea WCET constraint…
Emphasisin„plain” Big Data: keepingstepwithingress
oButlargelythesamefordirecttimeliness
No (direct) diskaccess
Memory: bounded
Per-tupleprocessing: bounded
Algorithmicpatterns:
oPer-tupleprocessing
oSlidingwindowstorageand processing
oSpecializedsampling
•Getsuglyfast
oVariousheuristics

28. Applicationclasses
Source: International Technical Support Organization. IBM InfoSphereStreams: Harnessing Data in Motion. September 2010, p80

29. Takesoncyber-physicalclouds: Cloud-in-CPS…

30. Converging domains
CPS
Cloud
computing
Big Data
30
standard link
Intelligence Reconfigurability

31. CloudsinCPS –reality, notpromise
31
SENSORSACTUATORS

32. Architecturallandscape
32

33. Takesoncyber-physicalclouds: …CPS-in-cloud

34. ExtendingApacheVCL forCPS
34
Apache VCLVirtualized Data Center... VirtualmachinesInternet/CAN/LANRemote clientReservationEstablishing connectionRemote desktop or terminal access

35. Proofof Concept
35
Time-shareable arrangements Cloud-on-Cloud Apache VCLVCL management networkVCL public networkCloud instanceNetwork-attachedphys. devicesExperiment video stream

36. „CloudonCloud” capability
36
Apache VCLVCL management networkVCL public networkApache VCL/OpenStack/... CoC virtual networks

37. „Cloud on Cloud” capability
37
Apache VCL
VCL management network
VCL public network
Apache VCL/OpenStack/...
CoC virtual networks
Bootstrap &
capture XaaS
Hypervisors

38. „CloudonCloud” (CoC)
38
Withnestedvirtualization
Wehave… ovirtualesxi
oVCL over VCL onthat
Somerestrictionsapply;inVCL, no…
ostoragevirtualization
onetworkvirtualization
odynamicreservations

39. Integratinga fielddevice: RaspberryPi
39
Surprisinglypopular
oInthetargetdemographic
Almost a labPC:rpiVCL module
Linux
ogentlerlearningcurve
oInreservation: SSH access
Usefulsetof interfaces
ASM
C
scripting
Java
Wolfram

40. Integratingfielddevices?
Otherdevicetypes: adapter computer needed
oE.g. a RasberryPi foran Arduino
oScopes/spectrometers/…: alreadythere
oAutonomouscameras/meshGWs/…: alreadyinside
Lab.pm: starting point,needsrework
oFielddevices: „sanitization” is strongerconcept
oHarderwork-Pi: reset+ read-onlySD netboot
40

41. Container/VM
Container/VM
Future: fielddevicesastruecloudhosts
Real-time/embeddedvirtualizationis maturing
oCheckout: Siemens Jailhouse
oXenforARM
o…
Alsosee: carrierclouds
RaspberryPi alreadyhas containers!
41
Container/VM

42. Educationalprototype
42

43. Immediateapplications: cloudengineering
CoC:teachingvirt. & cloud
oE.g. weuseitforan ESXilab;
osupportforlocal VCL develinprogress
Real-life: faults, errors, failures
oCPS: performance!
Virtualizationintheloop
oThereareexistingSWIFI tools…
o… and VCL canbe a harness
43

44. Immediate applications: people & labs
44
Internet/CAN/LAN
Remote
client
We have EE/CE in view;
chemistry, biology,
physics, …?

45. Trustingyourcloudwithdeadlines-is ita goodidea?

46. Cloudsfor demanding applications?
Standard infrastructure
vs
demanding application?

47. Cloudsfor demanding applications?
Virtual Desktop
Infrastructure
Telecommunications
Extra-functional reqs:
throughput, timeliness, availability
„Small problems” have high impact
(soft real time)

48. Test automation
Hypervisor
Interference
Lab
OS and
hypervisor
metrics
LLO
HHII
Experimental setup

49. Short transient faults –long recovery
8 sec platform overload
30 sec service outage
120 sec SLA violation
As if you unplug your desktop for a second...

50. Deterministic (?!) run-time in the public cloud...
Variance tolerable by overcapacity
Performance outage intolerable by overcapacity

51. The noisy neighbour problem
Hypervisor
Tenant Neighbor

52. Tenant-side measurability and observability
Hypervisor
Tenant Neighbor

53. CharacterizingIaaSperformance

54. IaaSperformance
HW notnecessarilyknown
Unknown/ uncontrollabledeployment
Unknown/ uncontrollable
scheduling
„Noisyneighbors”
Also: management actionperformance?

55. IaaSperformance
Deploymentdecisions
oShouldI usethiscloud?
Capacityplanning
oTypeand amountof res.
Perf. prediction
oQoStobe expected
oAnd itsdeviances
Benchmarking!

56. Benchmarking (a pragmatictakeon)
(De-facto) standard applications
withwelldefinedexecutionmetrics
thatmayexercisespecificsubsystems
tocompareIT systemsviasaidmetrics.
Popularbenchmarks: e.g. PhoronixTest Suite
Benchmarking asa Service: cloudharmony.com

57. Whytraditionalbenchmarking is notenough
Stability
Homogeneity
Rareevents
Repeatability?
oProvider/tenant
Micro/componentbenchmarks?
oApplicationsensitivity?
oCloudfunctions(scaleinand out)?

58. TowardsMeasurement-DrivenResilienceDesign forClouds

59. A performance featuremodel
+ exp. behavior, homogeneity, stability
Li, Z., OBrien, L., Cai, R., & Zhang, H. (2012). Towards a Taxonomy of Performance Evaluation of Commercial Cloud Services.
In 2012 IEEE Fifth International Conference on Cloud Computing (pp. 344–351). IEEE. doi:10.1109/CLOUD.2012.74

60. ModelingIaaSperformanceexperiments
Li, Z., OBrien, L., Cai, R., & Zhang, H. (2012). Towards a Taxonomy of Performance Evaluation of Commercial Cloud Services.
In 2012 IEEE Fifth International Conference on Cloud Computing (pp. 344–351). IEEE. doi:10.1109/CLOUD.2012.74

61. „Cloudmetrology” and itsapplication
Full stack instrumentationFull adaptive data acquisitionFine-grained storageExploratory Data AnalysisConfirmatory Data Analysis Mystery shoppers and routine excercisesApplication sensitivity model(Platform) fault modelPerformance/capacity modelStructural defensesDynamic defenses MONITORING BENCHMARKING

62. Example:characterizingVDI „CPU ReadyTime”
„Ready”: VM readytorun, butnotscheduled
oVDI: „stutter”
Rareevents
oSampling
Needsfinegranularity!
+ atleasta fewmonths
Very„wide” data
Result: ~QoEcapacity+ load
Big Data tooling

63. EDA: hypothesesfrom„visualtours” of thedata
Cloudresponsetime~ nwdelay
clientID ~ loc
ClientlocationsDoesnotscaleforBig Data (yet)

64. Workflow? (As of now)
 Classical
tools
Slow EDA
On Big Data
Interactive EDA
On samples
statistics
on samples
Big Data
statistics
 Hadoop,
Storm,
Cassandra,
…

65. The effectof CPS cloudbackendinstability

66. Experimentalenvironment
Host1
Host2
Workstation
Workstation
OS_contr
OS_compute
nimbus
OS_network
CollectD
replay
superv2
superv1
Application

67. Applicationtopology
Redisspout
Gatherer1
Gatherer2
Aggregator
Timerspout
Sweeper
<ts, city, delay>
<city, delay>

68. Workload
Baselineworkload
Start of stress
End of stress

69. CPU utilization

70. Processlatency
Relationshipwithguestresourceusage?

71. Correlation: 0.890

72. Acknowledgements
Specialthanksgo fortheexperimentalenvironmentand datatoourOpenStackMeasurement„taskforce”:
Ágnes Salánki, Dávid Zilahi, Tamás Nádudvari, György Nádudvari, Gábor Kiss (BME) and
Gábor Urbanics(QuanoptLtd, ourspinoff)
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