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

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

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

Problem : Maximizing Service Uptime V 1 = [1, 2, 2, 3, 3, 4] T1 = Min(24, 17.1, 33.3, 25) P1 P2 P3 P4 V 2 = [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)

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

Related Research ApproachRelated Research Evolutionary algorithms.W. Xiaoling, S. Lei, Y. Jie, X. Hui, J. Cho, and S. Lee, “Swarm based sensor deployment optimization in ad hoc sensor networks.” Integer Programming.B. Powell and A. Perkins. Fleet Deployment Optimization for Liner Shipping: An Integer Programming Model. Maritime Policy & Management, 24(2):183–192, Constraint satisfaction programming (CSP). F. Laburthe, N. Jussien, H. Cambazard, and G. Rochart, “choco: an open source java constraint programming library.” Bin packing heuristic algorithms.B. Dougherty, J. White, J. Balasubramanian, C. Thompson, and D. C. Schmidt, “Deployment Automation with BLITZ,” in Emerging Results track at the 31st International Conference on Software Engineering, Vancouver, CA, May Hybrid algorithms.Jules White and Brian Dougherty and Chris Thompson and Douglas C. Schmidt, “ScatterD: Spatial Deployment Optimization with Hybrid Heuristic / Evolutionary Algorithms,” ACM Transactions on Autonomous and Adaptive Systems Special Issue on Spatial Computing(to appear), Scalability limitation. Different heuristic, unsuitable for maximizing Service uptime

Commonly Used Techniques and their limitations Bin 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. jena.de/Entscheidung/binpp/binpack.gif

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.

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 Generate initial random particles (topology vector) 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 Display output result Yes No

Figure : Deployment Topology Vector

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

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

Limitations of Evolutionary Algorithms 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.

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

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.

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

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.

Experiment 1 Experiment 1 Homogeneous nodes : Power capacity – 2100 mAH Memory MB 100 heterogeneous software components – Randomly generated power consumption rate and memory SmartDeploy – Up to 94 % more service uptime than other algorithms.

Experiment 2 Experiment 2 Heterogeneous nodes : Power capacity – 50% : 2100 mAH, 50 % : 1200 mAH Memory - 50 % : 150 MB, 50% : 350 MB 100 heterogeneous software components – Randomly generated power consumption rate and memory SmartDeploy – Up to 162 % more service uptime than other algorithms.

Experiment 3 Experiment – 200 heterogeneous software components : Randomly generated power capacity and memory 100 Heterogeneous nodes – Power capacity – 50% : 2100 mAH, 50 % : 1200 mAH Memory - 50 % : 150 MB, 50% : 350 MB SmartDeploy algorithms give higher service uptime than other algorithms.

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

Experiment 5 Experiment 5 Comparison of time taken by Brute force algorithm to achieve service uptime NodesSoftware components Time taken (msec) (1.2 secs) (33.3 secs) (21 minutes) 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.

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’ 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.

Acknowledgement Dr. Aniruddha Gokhale for his constant guidance, encouragement and sharing knowledge during my research work. Dr. Jules White, Dr. Abhishek Dubey, 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.