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Mobile gpu cloud computing

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explosive growth of mobile devices usage and the quick
increase of the mobile applications are facing many challenges in
their resources as low computing power, battery life, limited
bandwidth, and storage. Mobile Cloud Computing (MCC) has
been introduced to be a potential technology for mobile services
and to solve the mobile resources problem by moving the
processing and the storage of data out from mobile devices to the
cloud. The cloud enables the integration with additional
development tool as graphical processing power (GPU) to
increase the computational power. This paper presents a novel
approach for real time face detection using GPU acceleration.
The results of developed Applications demonstrate that the
proposed Mobile GPU cloud computing increase both speed and accuracy of facial detection systems.

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Mobile gpu cloud computing

  1. 1. Mobile GPU Cloud Computing Marwa Ayad Prof. Ashraf Salem Dr. Mohamed Taher GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  2. 2. Agenda • Introduction • Motivation • Objectives • Cloud computing • Mobile Cloud Computing • Real Time face Recognition MCC • Face Recognition MCC enhancement • Conclusions 2 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  3. 3. Introduction • Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing. It refers to an infrastructure where the data storage and data processing can happen outside of the mobile device. 3 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  4. 4. Motivation • Mobile devices are becoming an essential part of human life. (e.g., Smartphone, tablet PCs, etc) • Mobile devices still lack in resources. • MCC overcome the mobile challenges (battery lifetime, processing power, availability) • Integrate mobile device with cloud computing – Accessing to multimedia and sensors data. • MCC provide Efficient and effective application – Real-time response. – Minimum communication cost with mobile platform – Limited computation overhead 4 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  5. 5. Motivation 5 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  6. 6. Motivation 6 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  7. 7. Motivation 7 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  8. 8. Objective • The objective of this work is to: – Build Mobile Cloud Computing environment with open source cloud OS. – Develop Mobile Cloud Computing Application. – Enhance Mobile Cloud Computing Application. 8 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  9. 9. Agenda • Introduction • Motivation • Objectives • Cloud computing – CC service module – CC deployment module – Build private cloud (opensource Software) • Mobile Cloud Computing • Real Time face Recognition MCC • Face Recognition MCC enhancement • Conclusions 9 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  10. 10. Cloud Computing • Cloud Computing provider of pooled network resources such as CPU, RAM, Storage and software over the web. These services are easily provides and released on demand. 10 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  11. 11. Cloud Computing service models • Infrastructure as a Service (IaaS): • Platform as a Service (PaaS). • Software as a Service (SaaS). 11
  12. 12. Cloud Computing deployment models • Private Cloud. • Public Cloud. • Hybrid Cloud. • Community Cloud 12 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  13. 13. OpenStack (IaaS) • OpenStack: Open source cloud operating system that controls large pools of compute, storage, and networking resources throughout a datacenter. 14 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  14. 14. OpenShift (PaaS) • OpenShift: o Open Source Software to PaaS that enables developers to build and deploy web applications without constraints. 15 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  15. 15. Cloud Service models IaaS vs PaaS • Experiment Goal: – Evaluate application deployment in deferent cloud service models. • Experiment Description: – retrieve the data of ten persons from MySQL database. • {"personData":[{"address":"nasrcity","age":"20","city":"Cairo","country":"Egypt","description":"Marwa has traditional and practical personality. she is hardworking and has leadership. ","email":"EngMarwaAyad@gmail.com", "gender":"female", "image":" ","job":"Senior Software Engineer ","name":"Marwa","telephonNumber":"0245358249329 "}]} • Experiment Result: Total request Timeservice models 1.75 secIaas 2.83 secPaas The platform as a service module adds a little overhead on response time. The overhead is mainly caused by managing and monitoring development software. 16 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  16. 16. Agenda • Introduction • Motivation • Objectives • Cloud computing • Mobile Cloud Computing – Mobile cloud computing architecture – Mobile cloud computing Application • Real Time face Recognition MCC • Face Recognition Mcc enhancement • Conclusions 17 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  17. 17. Mobile cloud Computing Architecture • Mobile Network • Cloud Computing • Mobile Device (with developed Application ) 18 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  18. 18. Mobile cloud Computing Application • Native Mobile applications – Developed using mobile platform supported programming languages • Embedded browser applications: – developed using standard web development languages. 19 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  19. 19. Mobile Android Vs. Blackberry • Experiment goal: – Measure the effect of mobile client resources with multiple requests to cloud backend application. • Experiment Description: – The native mobile application for android and blackberry generates and sends 20 request in closed loop (sequentially) and record the time of whole process. • Experiment Result: Average time per request Total time for 20 sequential request 1.40 s28.1746 sSONY ((Android 10.5 s210.6431msBlackberry (blackberry API) Android 10 times faster than blackberry. Blackberry OS 5.0 has 6 network transport types [Blackberry Internet Service, mobile data service, Direct TCP, WIFI TCP , WAP 1.0, and WAP 2.0]. Android makes http request faster by using the apache HTTP client. 20 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  20. 20. Mobile Android Vs. Blackberry in Different Cloud structure • Experiment goal: – Measure the effect of mobile client resources with one request to different cloud structure. • Experiment Description: – The native mobile application for two android devices and blackberry generates one request to retrieve person data. • Experiment Result: 21 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  21. 21. Agenda • Introduction • Motivation • Objectives • Cloud computing) • Mobile Cloud Computing • Real Time face Recognition MCC – Face Detection – Face recognition • Face Recognition MCC enhancement • Conclusions 22 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  22. 22. Real Time face Recognition MCC (SaaS) • High computational power. • Required large storage • Using mobile resource. • Real Time response. • Multiple accessing. • Useful application. 23 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  23. 23. Face Recognition Flow Diagram 24 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  24. 24. Face Detection (Viola and Jones call Haar Classifier Detection Algorithm ) • Haar Classifier Detection: o The Algorithm looks for specific features of a human face o When one feature is found, the algorithm allows to pass to the next stage of detection. o Uses a cascade of stages to eliminate non-face candidates quickly. o Our implementation Consist of 24 stages and each stage contains number of features. 25 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  25. 25. Face Recognition (The Eigenface Algorithm ) Image database consist of 50 face image for 10 unique people with 5 photos / person 26 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  26. 26. Agenda • Introduction • Motivation • Objectives • Cloud computing • Mobile Cloud Computing • Real Time face Recognition MCC • Face Recognition MCC enhancement – Face Detection filter – Multithread face Detection – GPU accelerations • Conclusions 27 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  27. 27. Face recognition MCC enhancements • Face recognition algorithm enhancement – Apply skin filter after detection . • Programming enhancement – Multithread application. • GPU Acceleration 28 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  28. 28. Face Detection Filters • Face Detection Filter (Skin Color Filter) – Pixel in skin image is more or equal to estimated threshold value. Total pixel skin more than 50% – Increase the accuracy of the detection from 95% to 97%. 29 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  29. 29. Evaluate Face Recognition MCC Application in Different Cloud Structures • Experiment goal: – Measure the performance of face recognition application with different image size deployed in different cloud structure. • Experiment Description: – The images database consists of 50 photos for 10 persons. • Experiment Result: •The presented time includes the communication time between mobile and cloud instance and it is similar in one and multiple servers with quite increasing in VM cloud instance. • Increasing number of faces in image not only increases the recognition time but also increases time of detection. 30
  30. 30. Multithread Face Detection • Detection window parallelization: – Detection window moving over input image can be parallelized. 31 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  31. 31. Evaluate Effect of Multiple Thread Face Detection Application • Experiment goal: – Prove that distribute face detection tasks on multiple threads improve algorithm computation and provide better utilization to cloud resources. • Experiment Description: – Implement android face detection mobile application. • Experiment Result: The experiment proves that four threads will reduce execution time by 2.8 times as still communication spent fixed time and switch between thread overhead. 32
  32. 32. GPU Acceleration • Executing data parallel code fragments on the GPU rather than being confined to the local CPU 33 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  33. 33. NVIDIA GTX 480 GPU_MCC Marwa Ayad, Recall, there are 15 cores on the GTX 480: 15*32 34
  34. 34. Simple Processing Flow 1. Copy input data from CPU memory to GPU memory PCI Bus 35 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  35. 35. Simple Processing Flow 1. Copy input data from CPU memory to GPU memory 2. Load GPU program and execute, caching data on chip for performance PCI Bus 36 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  36. 36. Simple Processing Flow 1. Copy input data from CPU memory to GPU memory 2. Load GPU program and execute, caching data on chip for performance 3. Copy results from GPU memory to CPU memory PCI Bus 37 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  37. 37. Face Recognition GPU Vs. CPU • Experiment goal: – Measure the effect of using GPU instead of using CPU Only. • Experiment Description: – CUDA face recognition implementations vs. CPU face recognition implantation. • Experiment Result: We can see that CUDA based implementation on GEForce GT 525M(96 core) is 9 times faster than the CPU version. 38
  38. 38. Face Detection mobile Vs. CPU Cloud Vs.GPU Cloud • Experiment Goal: – measure the execution of face detection application on local on mobile device and remotely on deferent cloud platform. • Experiment Description: – Mobile face detection implantation Vs. MCC face recognition • Experiment Result: – Cloud 40x Vs. mobile – One server = multiple Server with low processing Power. 39
  39. 39. Conclusions • A private Cloud system has been constructed • Face recognition MCC Application has been developed . • Performance improved by using GPUs – Speedup of 9x vs. CPU • Results show 40x speedup on cloud compared to mobile GPU_MCC Marwa Ayad, ICEAC 2015, Cairo 40
  40. 40. Thank You 41 engmarwaayad@gmail.com
  41. 41. Experimental Setup Cloud ServerMobile Device GPU Acceleration : Nvidia: GEForce GT 525M CUDA Driver Version 6.5 Number of multiprocessor : 2 Number of CUDA Cores/Mp: 48 Total (96 CUDA Cores) Development Tool 42 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  42. 42. MCC Security Platform chain security mechanism 43 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  43. 43. CSM Security Application platform • Mobile Generates Asymmetric Key (MPK & MSK) • Register with ID & MPK • Cloud check Generate (SK) • Cloud encrypted authorization message & SK by MPK. • Mobile Divided data and encryption SK + part number. • Cloud Decryption & processing. • Cloud response Encrypted with (sk). • Authentication & Authorization (second time) 44 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  44. 44. CSM Security Data sharing platform 45 GPU_MCC Marwa Ayad, ICEAC 2015, Cairo
  • tkbible

    Jul. 2, 2016

explosive growth of mobile devices usage and the quick increase of the mobile applications are facing many challenges in their resources as low computing power, battery life, limited bandwidth, and storage. Mobile Cloud Computing (MCC) has been introduced to be a potential technology for mobile services and to solve the mobile resources problem by moving the processing and the storage of data out from mobile devices to the cloud. The cloud enables the integration with additional development tool as graphical processing power (GPU) to increase the computational power. This paper presents a novel approach for real time face detection using GPU acceleration. The results of developed Applications demonstrate that the proposed Mobile GPU cloud computing increase both speed and accuracy of facial detection systems.

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