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MAPCloud: Mobile Applications on an
  Elastic and Scalable 2-Tier Cloud
            Architecture

    M. Reza Rahimi1, Nalini Venkatasubramanian1, Sharad Mehrotra1 and
                          Athanasios V. Vasilakos2
                   1. University of California, Irvine, CA.
        2. National Technical University of Athens, Athens, Greece.




                       in IEEE/ACM UCC 2012, Chicago, IL, USA.
Outline

 Cloud Resource
  Allocation for                                 Introduction and
Mobile Applications                                 Motivation

                      Mathematical Formulation
                           of the Problem




                          Experimental and    MAPCloud Middleware
  Conclusion and
                         Simulation Results       Architecture
 Future Directions




                                                                    2
Introduction and Motivation
Sensory Based Applications    Location Based        Mobile Music: 52.5%
                              Services (LBS)        Mobile Video:25.2%
                                                    Mobile Gaming: 19.3%




                             Augmented Reality

Mobile Social
Networks and
Crowdsourcing
                               Multimedia and
                               Data Streaming




•   ABI Research shows that mobile cloud
    computing will be rising from 42.8 million
    subscribers in 2008, to just over 998 million
    in 2014 (nearly 19%).
                                                                           3
Mobile Cloud Computing; What? Why?
     Mobile Cloud Computing (MCC) = Using
     Resources on Cloud to Empower Mobile
                  Applications
• Cellphones have limited resources such as Battery,
  Memory and Computation.
  First Approach: Connect to Public Cloud for resource intensive
  tasks!
         • (-) Long WAN delay [Satyanarayanan_2011] , [ Cavilla_2007] :
                • unlikely to be improved while the prime target of WAN improvement
                  is bandwidth, security, management.
         • (+) Scale up Very well.

[Satyanarayanan_2011] Mahadev Satyanarayanan, “Mobile Computing: The Next Decade”, in SIGMOBILE Mobile
2011.
[ Cavilla_2007] Lagar-Cavilla, Niraj Tolia, Eyal De Lara, M. Satyanarayanan, and David O'Hallaron. “ Interactive
Resource-Intensive Applications Made Easy”, In Proceedings MIDDLEWARE2007
                                                                                                                   4
Second Approach: Connect to Local
Clouds (Local proxies, Cloudlets) in
proximity of the users for resource
intensive tasks, [Clone Cloud],
[MAUI], [PARM], [Calling the
Cloud].
       • (+) LAN delay is always order of
         magnitude better that WAN delay
           [Satyanarayanan_2011] .
       • (-) Near user resources and wireless
         bandwidth could not scale up well.
[PARM] S. Mohapatra, M. Reza Rahimi, N. Venkatasubranian ”Power-Aware Middleware for Mobile Applications”,
Chapter 10 of the Handbook of Energy-Aware and Green Computing, Chapman Hall/CRC, 2011.
[Clone Cloud] Byung-Gon Chun, Sunghwan Ihm, Petros Maniatis, Mayur Naik, Ashwin Patti " CloneCloud: Elastic Execution
between Mobile Device and Cloud", In EuroSys 2011.
[MAUI] E. Cuervo, A. Balasubramanian, D. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl " MAUI: Making Smartphones Last
Longer with Code Offload", In MobiSys 2010.
[Calling the Cloud] Giurgiu, O. Riva, D. Juric, I. Krivulev and G. Alonso " Calling The Cloud: Enabling Mobile Phones as
Interfaces to Cloud Applications", In Middleware 2009.                                                                    5
Tier 1: Public Cloud
   (+) Scalable and Elastic
        (-) Price, Delay




Tier 2: Local Cloud
(+) Low Delay, Low Power,
         Almost Free                                                                                                    RTT:
    (-) Not Scalable and                                                                                3G Access      ~290ms
                                                                                                          Point
           Elastic
                                                     Wi-Fi Access
                                                         Point


                                     RTT:
                                     ~80ms




M. Reza. Rahimi, N. Venkatasubramania "MAPCloud: Mobile Applications on an Elastic 2-Tier Cloud Architecture", UCC 2012.
M. Reza. Rahimi, Nalini Venkatasubramania "Exploiting an Elastic 2-Tiered Cloud Architecture for Rich Mobile Applications", poster in
IEEE WoWMoM 2012.
M. Reza. Rahimi, N. Venkatasubramania "Cloud Based Framework for Rich Content Mobile Applications", poster in the IEEE/ACM
CCGrid 2011.
M. Satyanarayanan, P. Bahl, R. Cáceres, N. Davies " The Case for VM-Based Cloudlets in Mobile Computing", In PerCom 2009.           6
Mathematical Formulation of the Problem

•




                Decoder,
                Compressor,
                …




                                      7
Rn
    R1
         R2
              Rj
    R3



•




                        8
Workflow
• It consists of number of logical and precise steps
  known as a function (for application modeling).
• Functions could be composed together in different
  patterns [Mabrouk_2009] , [Zheng_2004] :
                                                                                      k

         F1             F2             F3                                             F1


                      SEQ                                                           LOOP

                        F3                                                  P1        F3
              1

         F1                            F4                             F1                             F4

              1         F2                                                            F2
                                                                            P2

     AND: CONCURRENT FUNCTIONS                                     XOR: CONDITIONAL FUNCTIONS

  N. B. Mabrouk, S. Beauche, E. Kuznetsova, N. Georgantas, and V. Issarny " QoS-aware Service Composition in Dynamic
  Service Oriented Environments", In Middleware 2009.
  L. Zeng, B. Benatallah, A. H. NGU, M. Dumas, J. Kalagnanam, and H. Chang "QoS-Aware Middleware for Web Services
  Composition ", In IEEE Trans. Software. Eng., 2004.
                                                                                                                       9
Workflow (Cont.)
•                                           3

                        F3             P1   F6
                    1

       Start                                          End
               F1            F4   F5             F8


                    1   F2             P2   F7




                                                            10
Quality of Service (QoS)
    • The QoS could be defined in two different Levels:
           • Atomic service level and Composite service level or
             workflow level.
    •     Atomic service level could be defined as:




•       The workflow QoS is defined based on different patterns as:
        Qos   SEQ             AND             XOR           LOOP




                                                                      11
Normalization
•   As it can be understood different QoSes have different
    dimensions (Price->$, power->joule, delay->s)
•   We need the normalization process to make them
    comparable.
•   It could be defined in different levels:
    • Service,
    • Workflow.
• Services, Max and Min Services (example):




                                                             12
Normalization (Cont.)



• The higher Normalized Power/Price/Delay are The better
  services are (low power/price/delay).
• The same procedure could be used to define the normalized
  workflow as:




                                                              13
Optimal Resource Allocation for
Mobile Applications
• The main question in resource allocation
  problem is:
   • Knowing the mobile user workflow; what is the
     optimal service allocation considering price, power
     and delay?
• To formally formulate this problem; we need to
  have utility function.
• Many has been defined in the operational
  research literature, we use the fairness utility
  for our problem.



                                                           14
•




    15
Cloud Resource Allocation for Mobile
        Applications: CRAM
• CRAM uses the combination of two main best
  practices in heuristic algorithm design:
   • Simulated Annealing (Good Global Optima Finder)
   • Greedy Approach(Good Local Optima Finder)
• It then uses the following observation to customize
  for pervasive environment:
   • Near user resources usually have better QoS.



                               Qos




                                                        16
CRAM (Cont.)
• Need Efficient way to retrieve information of
  services on cloud in specific region.
• Example Query: “Retrieve all MPEG to AVI decoder
   services in distance R of mobile user “
• R-Tree is an efficient way to answer these queries.
                                         R2 R1                                           R
                                   S2                    S8



                                                  S1
                                                                                   R1         R2
                      S6
                                        S4
                                             R3
                                                                       R3          R4         R5   R6
     R5                                      R4
                                                  S3
     R6               S5
                                                                       S1          S1         S2   S5


                                                                       S8          S3         S4   S7

         S7         S9
                                              S11                                  S11        S6   S9


                             S10                                                                   S10




 A. Silberschatz, H. F. Korth, S. Sudarshan, "Database System Concepts", McGraw-Hill, 2010.              17
CRAM (Cont.)




Simulated
Annealing




                           18
CRAM
• CRAM service selection could be as:

                            Total Number of Services




                                                     Randomly select and assigned
                                                   services to uk workflow with high
                                                  normalized price, normalized power,
                                                     normalized delay and average
                                                            normalized QoS.



                           Fi

                    S1,S12,S20,S28,…
                                                                               19
MAPCloud Middleware Architecture
                                                                                     R-Tree
                             Cloud Service Registry                                 Indexing
                                                                                    Structure

                                                      QoS-Aware Cloud
                                                            DB
           Mobile User Log
                 DB

                                                         MAPCloud
                                                        Analytics DB
                                                                        Local
                                                                         and
  Mobile          Mobile Profile
                                                        Mobile User     Public
  Client            Analyzer
                                                       Space-Time DB    Cloud
                                                                         Pool


                     Admission Control and Scheduling




                         MAPCloud Middleware                                     CRAM Core



                                                                                        20
Experimental and Simulation Results: Mobile
     Applications (Case Studies)

  Video                                   OCR+ Speech:    Preprocessing:
                      Decode Video                       Noise cancelation,
Augmented
                                                           Binarization,
 Reality                                                  Area Detection
 (VAR):            Search for Symbol in
                      Video Frames
You Tube                                                     Feature
                                                            Extraction
  Link
                   Compute its Position
                     and Orientation
                                                           Classification


                    Extract Symbol in
                        all Frames                           Language
                                                             Processing


                   Render 3D object in
                       all Frames                         Text to Speech



                      Encode Video
                                                          Audio Decoding




                                                                              21
Mobile Applications Profiling:
                                                                    S1              large instance:
                                                                     .
                                          Amazon EC2,S3              .           equivalent to a PC with
                                                                     .             7.5GB of memory,
                                                                    Sn
                                                                                   850 GB of storage




                                                                 Local Cloud 4
S1
 .
 .     Local Cloud 1                                                                       Local Cloud 5
 .
Sn                                          Local Cloud:
                                       64bit Windows dual-core
           LAN Speed
                                                server,
                                         with 8GB of memory                                                S1
                                                                                                            .
           S1                           and 500GB of storage.                                               .
            .          Local Cloud 2                                                                        .
            .                                                                                              Sn
            .
           Sn
                                                                                  Local Cloud n


                                                                   S1
                                                                    .
                                              Local Cloud 7         .
                                                                    .
                                                                   Sn


                                                                                                                22
Simulation Results
• In simulation we try to answer two important
  questions:
   • The optimality of CRAM Algorithm in different scenarios.
   • The optimality of 2-Tier Architecture in comparison to
     only using public cloud.




• Simulation Setup:
   •   MATLAB and CloudSim: Simulation Platforms.
   •   15 15 : 100m length of each cell
   •   # Wi-Fi Access point 50 (Uniform Dist.), 3G ubiquitous connectivity.
   •   #Amazon Instances: [5-10]
   •   #Local Cloud Instances:[5-10]
   •   RWP as the Mobility model U[0-10ms]
                                                                              23
Simulation Results


                          OCRS




                          VCAR




                     24
Simulation Results(Cont.)
• Local Cloud+Public Cloud:
   • How could we measure the performance of 2-Tiered
     Cloud Architecture?
   • What are the reasonable metrics?

 Local Cloud+          Local Cloud+         Local Cloud+
 Public Cloud          Public Cloud         Public Cloud

  Same Delay            Same Power           Same Price

 Public Cloud          Public Cloud         Public Cloud




                                                           25
CRAM

                  32%
Constant Delay;
  #Users 100
                  7%



                  CRAM

                  28%
Constant Power;
  #Users 100
                  10%



                  CRAM


Constant Price;   26%
  #Users 100
                  22%

                         26
Conclusions and Future Directions
• 2-Tier Cloud architecture has been reviewed.
• CRAM was proposed and its optimality was
  investigated.
• MAPCloud middleware is reviewed for optimal
  service allocation.
• Future Work:
   1.   Extending the workflow concept to space-time
        workflow which capture the user mobility effects.
   2.   More class of mobile application such as video
        streaming and content sharing with CRAM extension.




                                                             27

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Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture

  • 1. MAPCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture M. Reza Rahimi1, Nalini Venkatasubramanian1, Sharad Mehrotra1 and Athanasios V. Vasilakos2 1. University of California, Irvine, CA. 2. National Technical University of Athens, Athens, Greece. in IEEE/ACM UCC 2012, Chicago, IL, USA.
  • 2. Outline Cloud Resource Allocation for Introduction and Mobile Applications Motivation Mathematical Formulation of the Problem Experimental and MAPCloud Middleware Conclusion and Simulation Results Architecture Future Directions 2
  • 3. Introduction and Motivation Sensory Based Applications Location Based Mobile Music: 52.5% Services (LBS) Mobile Video:25.2% Mobile Gaming: 19.3% Augmented Reality Mobile Social Networks and Crowdsourcing Multimedia and Data Streaming • ABI Research shows that mobile cloud computing will be rising from 42.8 million subscribers in 2008, to just over 998 million in 2014 (nearly 19%). 3
  • 4. Mobile Cloud Computing; What? Why? Mobile Cloud Computing (MCC) = Using Resources on Cloud to Empower Mobile Applications • Cellphones have limited resources such as Battery, Memory and Computation. First Approach: Connect to Public Cloud for resource intensive tasks! • (-) Long WAN delay [Satyanarayanan_2011] , [ Cavilla_2007] : • unlikely to be improved while the prime target of WAN improvement is bandwidth, security, management. • (+) Scale up Very well. [Satyanarayanan_2011] Mahadev Satyanarayanan, “Mobile Computing: The Next Decade”, in SIGMOBILE Mobile 2011. [ Cavilla_2007] Lagar-Cavilla, Niraj Tolia, Eyal De Lara, M. Satyanarayanan, and David O'Hallaron. “ Interactive Resource-Intensive Applications Made Easy”, In Proceedings MIDDLEWARE2007 4
  • 5. Second Approach: Connect to Local Clouds (Local proxies, Cloudlets) in proximity of the users for resource intensive tasks, [Clone Cloud], [MAUI], [PARM], [Calling the Cloud]. • (+) LAN delay is always order of magnitude better that WAN delay [Satyanarayanan_2011] . • (-) Near user resources and wireless bandwidth could not scale up well. [PARM] S. Mohapatra, M. Reza Rahimi, N. Venkatasubranian ”Power-Aware Middleware for Mobile Applications”, Chapter 10 of the Handbook of Energy-Aware and Green Computing, Chapman Hall/CRC, 2011. [Clone Cloud] Byung-Gon Chun, Sunghwan Ihm, Petros Maniatis, Mayur Naik, Ashwin Patti " CloneCloud: Elastic Execution between Mobile Device and Cloud", In EuroSys 2011. [MAUI] E. Cuervo, A. Balasubramanian, D. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl " MAUI: Making Smartphones Last Longer with Code Offload", In MobiSys 2010. [Calling the Cloud] Giurgiu, O. Riva, D. Juric, I. Krivulev and G. Alonso " Calling The Cloud: Enabling Mobile Phones as Interfaces to Cloud Applications", In Middleware 2009. 5
  • 6. Tier 1: Public Cloud (+) Scalable and Elastic (-) Price, Delay Tier 2: Local Cloud (+) Low Delay, Low Power, Almost Free RTT: (-) Not Scalable and 3G Access ~290ms Point Elastic Wi-Fi Access Point RTT: ~80ms M. Reza. Rahimi, N. Venkatasubramania "MAPCloud: Mobile Applications on an Elastic 2-Tier Cloud Architecture", UCC 2012. M. Reza. Rahimi, Nalini Venkatasubramania "Exploiting an Elastic 2-Tiered Cloud Architecture for Rich Mobile Applications", poster in IEEE WoWMoM 2012. M. Reza. Rahimi, N. Venkatasubramania "Cloud Based Framework for Rich Content Mobile Applications", poster in the IEEE/ACM CCGrid 2011. M. Satyanarayanan, P. Bahl, R. Cáceres, N. Davies " The Case for VM-Based Cloudlets in Mobile Computing", In PerCom 2009. 6
  • 7. Mathematical Formulation of the Problem • Decoder, Compressor, … 7
  • 8. Rn R1 R2 Rj R3 • 8
  • 9. Workflow • It consists of number of logical and precise steps known as a function (for application modeling). • Functions could be composed together in different patterns [Mabrouk_2009] , [Zheng_2004] : k F1 F2 F3 F1 SEQ LOOP F3 P1 F3 1 F1 F4 F1 F4 1 F2 F2 P2 AND: CONCURRENT FUNCTIONS XOR: CONDITIONAL FUNCTIONS N. B. Mabrouk, S. Beauche, E. Kuznetsova, N. Georgantas, and V. Issarny " QoS-aware Service Composition in Dynamic Service Oriented Environments", In Middleware 2009. L. Zeng, B. Benatallah, A. H. NGU, M. Dumas, J. Kalagnanam, and H. Chang "QoS-Aware Middleware for Web Services Composition ", In IEEE Trans. Software. Eng., 2004. 9
  • 10. Workflow (Cont.) • 3 F3 P1 F6 1 Start End F1 F4 F5 F8 1 F2 P2 F7 10
  • 11. Quality of Service (QoS) • The QoS could be defined in two different Levels: • Atomic service level and Composite service level or workflow level. • Atomic service level could be defined as: • The workflow QoS is defined based on different patterns as: Qos SEQ AND XOR LOOP 11
  • 12. Normalization • As it can be understood different QoSes have different dimensions (Price->$, power->joule, delay->s) • We need the normalization process to make them comparable. • It could be defined in different levels: • Service, • Workflow. • Services, Max and Min Services (example): 12
  • 13. Normalization (Cont.) • The higher Normalized Power/Price/Delay are The better services are (low power/price/delay). • The same procedure could be used to define the normalized workflow as: 13
  • 14. Optimal Resource Allocation for Mobile Applications • The main question in resource allocation problem is: • Knowing the mobile user workflow; what is the optimal service allocation considering price, power and delay? • To formally formulate this problem; we need to have utility function. • Many has been defined in the operational research literature, we use the fairness utility for our problem. 14
  • 15. 15
  • 16. Cloud Resource Allocation for Mobile Applications: CRAM • CRAM uses the combination of two main best practices in heuristic algorithm design: • Simulated Annealing (Good Global Optima Finder) • Greedy Approach(Good Local Optima Finder) • It then uses the following observation to customize for pervasive environment: • Near user resources usually have better QoS. Qos 16
  • 17. CRAM (Cont.) • Need Efficient way to retrieve information of services on cloud in specific region. • Example Query: “Retrieve all MPEG to AVI decoder services in distance R of mobile user “ • R-Tree is an efficient way to answer these queries. R2 R1 R S2 S8 S1 R1 R2 S6 S4 R3 R3 R4 R5 R6 R5 R4 S3 R6 S5 S1 S1 S2 S5 S8 S3 S4 S7 S7 S9 S11 S11 S6 S9 S10 S10 A. Silberschatz, H. F. Korth, S. Sudarshan, "Database System Concepts", McGraw-Hill, 2010. 17
  • 19. CRAM • CRAM service selection could be as: Total Number of Services Randomly select and assigned services to uk workflow with high normalized price, normalized power, normalized delay and average normalized QoS. Fi S1,S12,S20,S28,… 19
  • 20. MAPCloud Middleware Architecture R-Tree Cloud Service Registry Indexing Structure QoS-Aware Cloud DB Mobile User Log DB MAPCloud Analytics DB Local and Mobile Mobile Profile Mobile User Public Client Analyzer Space-Time DB Cloud Pool Admission Control and Scheduling MAPCloud Middleware CRAM Core 20
  • 21. Experimental and Simulation Results: Mobile Applications (Case Studies) Video OCR+ Speech: Preprocessing: Decode Video Noise cancelation, Augmented Binarization, Reality Area Detection (VAR): Search for Symbol in Video Frames You Tube Feature Extraction Link Compute its Position and Orientation Classification Extract Symbol in all Frames Language Processing Render 3D object in all Frames Text to Speech Encode Video Audio Decoding 21
  • 22. Mobile Applications Profiling: S1 large instance: . Amazon EC2,S3 . equivalent to a PC with . 7.5GB of memory, Sn 850 GB of storage Local Cloud 4 S1 . . Local Cloud 1 Local Cloud 5 . Sn Local Cloud: 64bit Windows dual-core LAN Speed server, with 8GB of memory S1 . S1 and 500GB of storage. . . Local Cloud 2 . . Sn . Sn Local Cloud n S1 . Local Cloud 7 . . Sn 22
  • 23. Simulation Results • In simulation we try to answer two important questions: • The optimality of CRAM Algorithm in different scenarios. • The optimality of 2-Tier Architecture in comparison to only using public cloud. • Simulation Setup: • MATLAB and CloudSim: Simulation Platforms. • 15 15 : 100m length of each cell • # Wi-Fi Access point 50 (Uniform Dist.), 3G ubiquitous connectivity. • #Amazon Instances: [5-10] • #Local Cloud Instances:[5-10] • RWP as the Mobility model U[0-10ms] 23
  • 24. Simulation Results OCRS VCAR 24
  • 25. Simulation Results(Cont.) • Local Cloud+Public Cloud: • How could we measure the performance of 2-Tiered Cloud Architecture? • What are the reasonable metrics? Local Cloud+ Local Cloud+ Local Cloud+ Public Cloud Public Cloud Public Cloud Same Delay Same Power Same Price Public Cloud Public Cloud Public Cloud 25
  • 26. CRAM 32% Constant Delay; #Users 100 7% CRAM 28% Constant Power; #Users 100 10% CRAM Constant Price; 26% #Users 100 22% 26
  • 27. Conclusions and Future Directions • 2-Tier Cloud architecture has been reviewed. • CRAM was proposed and its optimality was investigated. • MAPCloud middleware is reviewed for optimal service allocation. • Future Work: 1. Extending the workflow concept to space-time workflow which capture the user mobility effects. 2. More class of mobile application such as video streaming and content sharing with CRAM extension. 27