1. Resource Optimization for Publisher/Subscriber-based Avionics Systems Institute for Software Integrated Systems Vanderbilt University Nashville, Tennessee Jules White, Douglas C. Schmidt, & Brian Dougherty Distributed Object Computing (DOC) Group

2. Before SPRUCE: AFRL Funded Research GUTS: System Execution Modeling Technologies for Large-scale Net-centric DoD Systems 1.Existing research effort funded by AFRL 2.Goal: Build tools to identify system deployment topologies that meet performance requirements

3. Deployment & Performance The performance of a software architecture (in terms of end-to- end response times) is significantly impacted by deployment, e.g.: 1.Competition between application components for local resources (e.g., CPU & memory) 2.Differing levels of network bandwidth & latencies between nodes Once the software architecture is designed, how do we know if it meets our end-to-end timeliness goals?

4. Deployment & Performance Once the software architecture is designed, how do we know if it meets our end-to-end timeliness goals? 1.Empirically identify deployment(s) that meet the timeliness goals 2.Perform a smart sampling of potential deployment topologies to instill confidence that either more hardware is needed or the software architecture needs to be changed

5. SPRUCE Impact SPRUCE Challenge Problem: Multi-dimensional Resource Optimization for Publisher/Subscriber- based Avionics Systems Very closely related problem to some of the problems we were solving in GUTS Key Differences: 1.How do you find a deployment of pub/sub components to processors that meets real-time scheduling constraints 2.Furthermore, how do you maximize idle processors or minimize network utilization

6. SPRUCE Impact SPRUCE helps to focus our research efforts: 1.Challenge problems provide real data for us to test our deployment techniques on a)Previously relied on generated data for large-scale problems 2.Challenge problem definitions help us to plan research extensions that will have the most impact 3.Papers are much easier for us to write, motivate, and provide empirical results for

7. SPRUCE Impact on GUTS Research Return Because we are working with real data: 1.Challenge problems provide real data for us to test our deployment techniques on a)Previously relied on generated data for large-scale problems 2.Challenge problem definitions help us to plan research extensions that will have the most impact 3.Papers are much easier for us to write, motivate, and provide empirical results for Research Effort 1 Duplicated Research Effort 2 Without SPRUCE, there is the potential for duplication and rediscovery of key infrastructure/research needed to solve research challenges

8. SPRUCE Impact on GUTS Research Return Because we are working with real data: 1.Challenge problems provide real data for us to test our deployment techniques on a)Previously relied on generated data for large-scale problems 2.Challenge problem definitions help us to plan research extensions that will have the most impact 3.Papers are much easier for us to write, motivate, and provide empirical results for GUTS Research Effort With SPRUCE, research teams can find small ways of modifying their research to address other key research challenges that they were not aware of Research Adapter

9. Refining Problem Understanding Because the challenge problem datasets are very large and the challenges complex, collaboration with problem contributors is critical. Examples from Pub/Sub Opt: 1.Are priorities present? Are they predefined and static? Dynamic? 2.Can multiple tasks run at the same time? 3.What type of scheduling is appropriate? 4.How is task duration calculated? 5.What are the scalability requirements?

10. Solution Approach Our existing techniques weren’t designed to handle network bandwidth minimization or real- time scheduling constraints. Solution Approach: 1.We modified the bin-packing algorithms from the GUTS work to support scheduling constraints using Liu & Layland bound 2.Combined randomized bin- packing with particle swarm and genetic optimization algorithms to search for bandwidth minimizing deployment topologies Particle Swarm Optimization (PSO) Genetic Optimization

11. Particle Swarm Deployment Optimization 1.The deployment search is performed by a series of “particles” that represent potential solutions 2.We combine a first-fit heuristic bin-packing algorithm with randomization to generate the initial particle positions. 3.Each particle has a velocity and flies through the solution space 4.Particles gravitate towards better local and global solutions Particle Seeding Randomized Bin-Packing Particle Swarm A Particle Represents A Deployment Topology

12. Genetic Deployment Optimization Initial Population Randomized Bin-Packing A Population Member Represents A Deployment Topology 1.The deployment search is performed by evolving a population of solutions through mating and mutation. 2.We combine a first-fit heuristic bin-packing algorithm with randomization to generate the initial solution population. 3.Only the fittest solutions mate and propagate from generation to generation

13. SPRUCE Impact on GUTS Research Return GUTS Research Effort SPRUCE gives researchers and solution consumers the ability to have a “bake off” to empirically compare solution capabilities Research Adapter Solution Approach: PSO Solution Approach: Genetic Opt.

14. SPRUCE Impact on GUTS Research Return Research Adapter SPRUCE Experiment Instances Experiment instances show solution consumers how to test research solutions on other datasets (e.g. proprietary/restricted design info.)

15. Automatic Result Reporting IsisLab Experiments may require complex setups that are difficult to manually replicate. 1.Experimentation process is stored in IsisLab. Experiments can be re-run on demand. 2.Results of experiments can be automatically reported to Spruce without user intervention Results Spruce

16. Deployment Ongoing & Future Work 5 yearsNow To make chips more power efficient, multicore designs are being used If we are writing applications for 80 core processors in 5 years, we are going to be facing similar deployment problems to what we have in CPS How do we use cache architecture & usage characteristics to better predict processor utilization How do we use cache prediction models to improve deployment decisions? 80 Core Processors The hard deployment problems now, may become the standard in a few years 80 Embedded Processors

17. Experimentation Infrastructure Ongoing & Future Work IsisLab Launching an experiment still requires leaving Spruce and logging into IsisLab. 1.Extend Spruce & IsisLab to support launching experiments from Spruce. 2.Provide facilities for parameterizing experiments with inputs from forms on the Spruce website. Results Spruce

18. Lessons Learned & Concluding Remarks 1.Email is a problem. Discussions conducted via email are lost to the broader SPRUCE community. Possible need for discussion areas that are initially private. 2.Giving researchers access to real datasets is a key capability of SPRUCE. Real data is hard for researchers to obtain. Real data makes solutions more applicable. 3.Getting started with SPRUCE can be intimidating. New users may be unsure of correct usage. 4.The SPRUCE experimentation platform is critical to empirical validation of solutions. SPRUCE