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MAPCloud: Mobile Applications on an  Elastic and Scalable 2-Tier Cloud            Architecture    M. Reza Rahimi1, Nalini ...
Outline Cloud Resource  Allocation for                                 Introduction andMobile Applications                ...
Introduction and MotivationSensory Based Applications    Location Based        Mobile Music: 52.5%                        ...
Mobile Cloud Computing; What? Why?     Mobile Cloud Computing (MCC) = Using     Resources on Cloud to Empower Mobile      ...
Second Approach: Connect to LocalClouds (Local proxies, Cloudlets) inproximity of the users for resourceintensive tasks, [...
Tier 1: Public Cloud   (+) Scalable and Elastic        (-) Price, DelayTier 2: Local Cloud(+) Low Delay, Low Power,       ...
Mathematical Formulation of the Problem•                Decoder,                Compressor,                …              ...
Rn    R1         R2              Rj    R3•                        8
Workflow• It consists of number of logical and precise steps  known as a function (for application modeling).• Functions c...
Workflow (Cont.)•                                           3                        F3             P1   F6               ...
Quality of Service (QoS)    • The QoS could be defined in two different Levels:           • Atomic service level and Compo...
Normalization•   As it can be understood different QoSes have different    dimensions (Price->$, power->joule, delay->s)• ...
Normalization (Cont.)• The higher Normalized Power/Price/Delay are The better  services are (low power/price/delay).• The ...
Optimal Resource Allocation forMobile Applications• The main question in resource allocation  problem is:   • Knowing the ...
•    15
Cloud Resource Allocation for Mobile        Applications: CRAM• CRAM uses the combination of two main best  practices in h...
CRAM (Cont.)• Need Efficient way to retrieve information of  services on cloud in specific region.• Example Query: “Retrie...
CRAM (Cont.)SimulatedAnnealing                           18
CRAM• CRAM service selection could be as:                            Total Number of Services                             ...
MAPCloud Middleware Architecture                                                                                     R-Tre...
Experimental and Simulation Results: Mobile     Applications (Case Studies)  Video                                   OCR+ ...
Mobile Applications Profiling:                                                                    S1              large in...
Simulation Results• In simulation we try to answer two important  questions:   • The optimality of CRAM Algorithm in diffe...
Simulation Results                          OCRS                          VCAR                     24
Simulation Results(Cont.)• Local Cloud+Public Cloud:   • How could we measure the performance of 2-Tiered     Cloud Archit...
CRAM                  32%Constant Delay;  #Users 100                  7%                  CRAM                  28%Constan...
Conclusions and Future Directions• 2-Tier Cloud architecture has been reviewed.• CRAM was proposed and its optimality was ...
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Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture

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Presented In IEEE/ACM International Conference on Utility and Cloud Computing, Chicago, IL, USA.

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

  1. 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. 2. Outline Cloud Resource Allocation for Introduction andMobile Applications Motivation Mathematical Formulation of the Problem Experimental and MAPCloud Middleware Conclusion and Simulation Results Architecture Future Directions 2
  3. 3. Introduction and MotivationSensory Based Applications Location Based Mobile Music: 52.5% Services (LBS) Mobile Video:25.2% Mobile Gaming: 19.3% Augmented RealityMobile SocialNetworks andCrowdsourcing 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. 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 Mobile2011.[ Cavilla_2007] Lagar-Cavilla, Niraj Tolia, Eyal De Lara, M. Satyanarayanan, and David OHallaron. “ InteractiveResource-Intensive Applications Made Easy”, In Proceedings MIDDLEWARE2007 4
  5. 5. Second Approach: Connect to LocalClouds (Local proxies, Cloudlets) inproximity of the users for resourceintensive tasks, [Clone Cloud],[MAUI], [PARM], [Calling theCloud]. • (+) 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 Executionbetween 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 LastLonger 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 asInterfaces to Cloud Applications", In Middleware 2009. 5
  6. 6. Tier 1: Public Cloud (+) Scalable and Elastic (-) Price, DelayTier 2: Local Cloud(+) Low Delay, Low Power, Almost Free RTT: (-) Not Scalable and 3G Access ~290ms Point Elastic Wi-Fi Access Point RTT: ~80msM. 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 inIEEE WoWMoM 2012.M. Reza. Rahimi, N. Venkatasubramania "Cloud Based Framework for Rich Content Mobile Applications", poster in the IEEE/ACMCCGrid 2011.M. Satyanarayanan, P. Bahl, R. Cáceres, N. Davies " The Case for VM-Based Cloudlets in Mobile Computing", In PerCom 2009. 6
  7. 7. Mathematical Formulation of the Problem• Decoder, Compressor, … 7
  8. 8. Rn R1 R2 Rj R3• 8
  9. 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. 10. Workflow (Cont.)• 3 F3 P1 F6 1 Start End F1 F4 F5 F8 1 F2 P2 F7 10
  11. 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. 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. 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. 14. Optimal Resource Allocation forMobile 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. • 15
  16. 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. 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
  18. 18. CRAM (Cont.)SimulatedAnnealing 18
  19. 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. 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. 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 FramesYou 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. 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 4S1 . . 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. 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. 24. Simulation Results OCRS VCAR 24
  25. 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. 26. CRAM 32%Constant Delay; #Users 100 7% CRAM 28%Constant Power; #Users 100 10% CRAMConstant Price; 26% #Users 100 22% 26
  27. 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
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