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Reserch wearable devices ppt

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Shalini Toluchuri
Shalini Toluchuri

The document proposes a three-layer architecture for just-in-time code offloading from wearable devices to mobile devices and the cloud. It introduces an optimization problem to maximize the number of tasks on the wearable device that start within a time limit after the previous task. The document also presents a fast heuristic algorithm called OLGA based on genetic algorithms to solve this optimization problem. Simulation results show that OLGA quickly finds near-optimal solutions and outperforms a standard genetic algorithm in terms of runtime.

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JUSTINTIMECODEOFFLOADINGFORWEARABLE COMPUTING Publishers: Z.CHENG, PENG LI, J.WANG, SONG GUO Presented By: SHALINI TOLUCHURI (150913025)
CONTENTS ABSTRACT INTRODUCTION LITERATURE REVIEW PROBLEM STATEMENT PERFORMANCE EVALUATION CONCLUSION REFERENCES 6/29/2017 2
ABSTRACT Propose a three-layer architecture consisting of wearable devices, mobile devices, and a remote cloud for code offloading. Introduce just-in-time objective, to increase high performance Propose a fast heuristic algorithm based on the genetic algorithm to solve it. Extensive simulations are conducted to show the performance. 6/29/2017 3
INTRODUCTION 6/29/2017 4
LITERATURE REVIEW Mobile cloud computing- offloads the whole application to the cloud. Partition scheme has emerged to partially offload applications to cloud for achieving a better performance. CloneCloud Gaussian application Components of Java programs should be offloaded. 6/29/2017 5
APPLICATION MODEL 6/29/2017 6 A wearable application can be represented by a directed acyclic graph G (N ; A), where set N ={1, 2, ., n} denotes a number of tasks. The relationship among tasks is represented by directed link in setA. The tasks in set N can be divided intotwo subsets NW and NnoW, which include w-tasks and non-w-tasks .
PROBLEM STATEMENT JCOW (Just-in-time Code Offloading for Wearable Computing) Given a network consisting of a wearable device, several mobile devices, and a remote cloud, and an application represented by a task graph, we attempt to find a code offloading scheme that maximizes the number of w-tasks, each of which starts within 則 time after its previous w-task.
Optimization framework for the JCOWproblemcan be formulated as a mixed-integer nonlinear programming (MINLP) problem as follows. 6/29/2017 8
ALGORITHM OLGA 6/29/2017 9
PERFORMANCE EVALUATION 6/29/2017 10
The performance of our proposed algorithm increases as the number of mobile devices grows. 6/29/2017 11
The execution time of GAis significantly higher than OLGA because GAspends a large portionof time to generate feasible chromosomes.
When there are 10 tasks, GA needs more then 2 minutes to guarantee just-in-time w-tasks, while OLGA achieves within 5 seconds.
CONCLUSION Instead of offloading all codes directly to the remote cloud, we employ mobile devices nearby to form a local mobile cloud with low communication delay with the wearable device. Propose an enhanced algorithm creating chromosomes to get optimized solution. Simulations show that algorithm quickly converge to performance close to optimal solution. 6/29/2017 14
REFERENCES A. R. Khan, M. Othman, S. A. Madani, and S. U. Khan, ``A survey of mobile cloud computing application models,'' IEEE Commun, Feb. 2014. G. Ngai, S. C. Chan, J. C. Y. Cheung, and W. W. Y. Lau, ``Deploying a wearable computing platform for computing education,'' IEEE Trans.Learn. Technol, Jan./Mar. 2010. Lei JIAO, Roy FRIEDMAN, Xiaoming FU, Stefano SECCI, Cloud-based Computation Offloading for Mobile Devices: State of the Art, Challenges and Opportunities, Future Network Summit ,2013 6/29/2017 15
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Reserch wearable devices ppt

  • 1. JUSTINTIMECODEOFFLOADINGFORWEARABLE COMPUTING Publishers: Z.CHENG, PENG LI, J.WANG, SONG GUO Presented By: SHALINI TOLUCHURI (150913025)
  • 3. ABSTRACT Propose a three-layer architecture consisting of wearable devices, mobile devices, and a remote cloud for code offloading. Introduce just-in-time objective, to increase high performance Propose a fast heuristic algorithm based on the genetic algorithm to solve it. Extensive simulations are conducted to show the performance. 6/29/2017 3
  • 5. LITERATURE REVIEW Mobile cloud computing- offloads the whole application to the cloud. Partition scheme has emerged to partially offload applications to cloud for achieving a better performance. CloneCloud Gaussian application Components of Java programs should be offloaded. 6/29/2017 5
  • 6. APPLICATION MODEL 6/29/2017 6 A wearable application can be represented by a directed acyclic graph G (N ; A), where set N ={1, 2, ., n} denotes a number of tasks. The relationship among tasks is represented by directed link in setA. The tasks in set N can be divided intotwo subsets NW and NnoW, which include w-tasks and non-w-tasks .
  • 7. PROBLEM STATEMENT JCOW (Just-in-time Code Offloading for Wearable Computing) Given a network consisting of a wearable device, several mobile devices, and a remote cloud, and an application represented by a task graph, we attempt to find a code offloading scheme that maximizes the number of w-tasks, each of which starts within 則 time after its previous w-task.
  • 8. Optimization framework for the JCOWproblemcan be formulated as a mixed-integer nonlinear programming (MINLP) problem as follows. 6/29/2017 8
  • 11. The performance of our proposed algorithm increases as the number of mobile devices grows. 6/29/2017 11
  • 12. The execution time of GAis significantly higher than OLGA because GAspends a large portionof time to generate feasible chromosomes.
  • 13. When there are 10 tasks, GA needs more then 2 minutes to guarantee just-in-time w-tasks, while OLGA achieves within 5 seconds.
  • 14. CONCLUSION Instead of offloading all codes directly to the remote cloud, we employ mobile devices nearby to form a local mobile cloud with low communication delay with the wearable device. Propose an enhanced algorithm creating chromosomes to get optimized solution. Simulations show that algorithm quickly converge to performance close to optimal solution. 6/29/2017 14
  • 15. REFERENCES A. R. Khan, M. Othman, S. A. Madani, and S. U. Khan, ``A survey of mobile cloud computing application models,'' IEEE Commun, Feb. 2014. G. Ngai, S. C. Chan, J. C. Y. Cheung, and W. W. Y. Lau, ``Deploying a wearable computing platform for computing education,'' IEEE Trans.Learn. Technol, Jan./Mar. 2010. Lei JIAO, Roy FRIEDMAN, Xiaoming FU, Stefano SECCI, Cloud-based Computation Offloading for Mobile Devices: State of the Art, Challenges and Opportunities, Future Network Summit ,2013 6/29/2017 15