Maximizing Service Uptime of Smartphone-based Distributed Real-time and Embedded Systems

1. MS thesis presentation, 19 November 2010 Anushi Shah anushi.shah@vanderbilt.edu Maximizing Service Uptime of Smartphone-based Distributed Real-time and Embedded Systems Department of Electrical Engineering & Computer Science Vanderbilt University, Nashville, TN, USA

2. Presentation Road-map • Case Study Example. • Problem Definition. • Challenges. • Related Work. • Current Techniques and their limitations. • Our Solution. • Experimental results. • Concluding Remarks and Future Work.

3. Case Study Example : Video Recognition Service For Disaster Monitoring System.

4. Problem : Maximizing Service Uptime • V1 = [1, 2, 2, 3, 3, 4] T1 = Min(24, 17.1, 33.3, 25) P1 P2 P3 P4 • V2 = [1, 2, 4, 3, 1, 2] T2 = Min(13.3, 50, 20, 50) ... , etc. Max Service Uptime T = (T1, T2,...) = (17.1, 13.3,..) Deployment topology (vector)

5. Challenges Complex hardware/software design constraints. Heterogeneity of available resources and execution constraints. System Scale

6. Related Research Scalability limitation. Different heuristic, unsuitable for maximizing Service uptime

7. Commonly Used Techniques and their limitationsBin Packing heuristics algorithms • The problem of packing a set of items into a number of bins such that the total weight, volume, etc. does not exceed some maximum value. • Worst – fit bin packing heuristic : Defines the placement of items into the largely empty existing bin. • Limitation : Gives valid solution but not necessarily optimal one for huge problem sizes. http://www.wiwi.uni-jena.de/Entscheidung/binpp/binpack.gif

8. Evolutionary algorithm : Particle Swarm Optimization (PSO) • Simulates the behavior of flocking birds in search of food. • Group of birds - Randomly searching food in an area. • Only one piece of food in the area being searched.  • Birds come nearer to food in each iteration. - The effective strategy is to follow the bird which is nearest to the food. 

9. Generate initial random particles (topology vector) PSO Calculate fitness value If the fitness value(present) is better than the best fitness value (pBest) in history            set current value as the new pBest Choose the particle with the best fitness value of all the particles as the gBest Calculate particle velocity.Update particle position. Maximum iterations or particle’s converge Yes No Display output result

10. Figure : Deployment Topology Vector http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.629

11. Evolutionary algorithm : Genetic Algorithm • The genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution ( genes or chromosomes).

12. Generate initial random chromosomes (topology vector) GA Calculate fitness value of each chromosome. Maximum generations next generations Display output result Select next generation. Perform reproduction using crossover. Perform mutation.

13. Limitations of Evolutionary Algorithms Poor behavior when solution space contains large number of points in search space corresponding to solutions do not meet design constraints.

14. Our approach : SmartDeploy Framework • Inspired from ScatterD– hybrid of first-fit bin packing heuristics and evolutionary algorithms (genetic and particle swarm optimization algorithms) to minimize power consumption in real time systems. • Determine initial vectors to maximize the probability that they correspond to valid deployment topologies. • Ensure that as vectors are evolved , the probability that they are invalid is minimized

15. SmartDeploy - Framework • Extends ScatterD Framework by providing worst-fit heuristic. • Hybrid algorithm that integrates two algorithms worst-fit bin packing heuristics with evolutionary algorithms (genetic and particle swarm optimization algorithms. • Generates the deployment plan which maximizes service uptime.

16. 2. Generation of initial random topologies (particles) SmartDeploy - Framework 1. Input values for experiment 5. Integration between bin-packer and PSO (Return optimized topology to PSO) 3. Integration between bin-packer and PSO (Give a portion of input topology to bin-packer 4. Worst-fit bin packer < max iterations 8. Output value if maximum iterations reached or process converges 6. Service uptime maximization objective / fitness function 7. Update particle’s position and velocity SmartDeploy portion WF-Bin packer + PSO Integrated portion between bin-packer and PSO Original ScatterD portion

17. Experimental Strategies and Execution Platform The five techniques we were compared : • Worst-fit bin packing • PSO • SmartDeploy PSO • Genetic • SmartDeploy Genetic Metric : • Service uptime. • Computational time. • Windows XP desktop with 2.19 GHz Intel Core 2 Duo processor and 2 GB RAM. • Java Virtual Machine (JVM) version 1.6. • Algorithms - Implemented in Java. • Uniform distribution for generating initial random vectors.

18. Experiment 1Homogeneous nodes : Power capacity – 2100 mAH Memory - 150 MB100 heterogeneous software components – Randomly generated power consumption rate and memory SmartDeploy – Up to 94 % more service uptime than other algorithms.

19. Experiment 2Heterogeneous nodes :Power capacity – 50% : 2100 mAH, 50 % : 1200 mAHMemory - 50 % : 150 MB, 50% : 350 MB100 heterogeneous software components – Randomly generated power consumption rate and memory SmartDeploy – Up to 162 % more service uptime than other algorithms.

20. Experiment 3100 – 200 heterogeneous software components : Randomly generated power capacity and memory100 Heterogeneous nodes – Power capacity – 50% : 2100 mAH, 50 % : 1200 mAHMemory - 50 % : 150 MB, 50% : 350 MB SmartDeployalgorithms give higher service uptime than other algorithms.

21. Experiment 4Comparison of computation time taken byeach of five algorithms to execute SmartDeploy algorithms is bit slower than other algorithms which is acceptable for offline deployment solution.

22. Experiment 5 Comparison of time taken by Brute force algorithm to achieve service uptime Since Brute force algorithm takes considerable amount of time to run for a small problem size, it is not practical to run for large problem size.

23. Conclusion • The experimental results show that SmartDeploy framework increased service uptime from 20% to 162% beyond that provided by worst-fit bin packer and evolutionary algorithms used independently. • SmartDeploy is slightly slower than the other algorithms, the slower speed is acceptable for offline computations of deployment. • Submitted paper to ISORC’ 2011. Future Work • Investigate the use of SmartDeploy framework in runtime deployment decisions. • Investigate other distribution techniques for generation of initial random topologies of evolutionary algorithms like Gaussian distribution.

24. Acknowledgement • Dr. AniruddhaGokhale for his constant guidance, encouragement and sharing knowledge during my research work. • Dr. Jules White, Dr. AbhishekDubey, Brian Dougherty, Kyoungho An and all DOC group members for sharing their knowledge during paper writing. • NSF CNS/SHF and NSF RAPIDS for funding the research work.