Optimizing Dynamic Resource Allocation

We present a formulation, solution method, and program acceleration techniques for two dynamic control scenarios, both with the common goal of optimizing resource allocations. These approaches allocate resources in a non-myopic way, accounting for long-term impacts of current control decisions via nominal belief-state optimization (NBO). In both scenarios, the solution techniques are parallelized for reduced execution time. A novel aspect is included in the second scenario: dynamically allocating the computational resources in an online fashion which is made possible through constant aspect ratio tiling (CART).

[1]  Sanjay V. Rajopadhye,et al.  Alphabets: An Extended Polyhedral Equational Language , 2011, 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum.

[2]  Edwin K. P. Chong,et al.  A POMDP Framework for Coordinated Guidance of Autonomous UAVs for Multitarget Tracking , 2009, EURASIP J. Adv. Signal Process..

[3]  Alfred O. Hero,et al.  Partially Observable Markov Decision Process Approximations for Adaptive Sensing , 2009, Discret. Event Dyn. Syst..

[4]  Paul Feautrier,et al.  Some efficient solutions to the affine scheduling problem. I. One-dimensional time , 1992, International Journal of Parallel Programming.

[5]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[6]  FeautrierPaul.  Some efficient solutions to the affine scheduling problem , 1992 .

[7]  Paul Feautrier,et al.  Dataflow analysis of array and scalar references , 1991, International Journal of Parallel Programming.

[8]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[9]  Edwin K. P. Chong,et al.  Approximate stochastic dynamic programming for sensor scheduling to track multiple targets , 2009, Digit. Signal Process..

[10]  Edwin K. P. Chong,et al.  UAV Path Planning in a Dynamic Environment via Partially Observable Markov Decision Process , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Uday Bondhugula,et al.  PLuTo: A Practical and Fully Automatic Polyhedral Program Optimization System , 2015 .

[12]  Sanjay V. Rajopadhye,et al.  On Synthesizing Systolic Arrays from Recurrence Equations with Linear Dependencies , 1986, FSTTCS.

[13]  Tomofumi Yuki,et al.  AlphaZ: A System for Design Space Exploration in the Polyhedral Model , 2012, LCPC.

[14]  Alfred O. Hero,et al.  Pomdp Approximation Using Simulation and Heuristics , 2008 .