DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Manuscript for IEEE Transactions on Automatic Control 8 4. Overall control procedure There are two representative optimal control approaches in dynamic systems: Dynamic Programming (DP) and Model Predictive Control (MPC). Though DP gives optimal closed-loop policy it has inefficiencies in dealing with large-scale systems especially when systems are working in finite time horizon [20]-[22]. In MPC, for each current state, an optimal open-loop control policy is designed for finite-time horizon by solving a static mathematical programming model [23]-[26]. The design process is repeated for the next observed state feedback forming a closed-loop policy reactive to each current system state. Though MPC does not give absolutely optimal policy in stochastic environments, the periodic design process alleviates the impacts of stochasticity and it is easy to adapt to new contexts by explicitly handling objective function or constraints. Considering the characteristic of the current problem, we choose MPC framework. Our networks are large-scale working in finite time horizon and need to adapt to unpredictable stress environment. Therefore, under MPC framework, we develop an adaptive control mechanism as depicted in Fig. 2. First, to address adaptivity we model stress environment by quantifying resource availability through sensors. Second, we build a mathematical programming model with the resource availability incorporated, which predicts QoS as a function of alternative algorithms. Third, we provide an auction market as a decentralized coordination mechanism for solving the programming model. By periodically opening the auction market, the system can achieve desirable performance adaptive to changing stress environment while assuring scalability property. We define sensors and build a mathematical programming model in Section 5, and refine it based on stability analysis in Section 6. The refined programming model is decentralized in Section 7.
Manuscript for IEEE Transactions on Automatic Control 9 Stress Environment Modeling Sensor Design Sensor Component Sensor Component Sensor Component Mathematical Programming Periodic Auctioning Auction Decentralized Coordination Fig. 2. Overall control procedure 5. Mathematical programming model In this section we define sensors and build a mathematical programming model under MPC framework. 5.1 Sensors Each component monitors its operating environment through a sensor. The sensor measures resource availability MRA i (t), which is defined as available fraction of a resource when a component i requests that resource in the last control period at control point t. There are two quantities to extract this measurement, which are request time and execution time. Request time is the duration for which the component requests resource or equivalently queue length (including a task in service) is more than zero. Execution time is the duration for which the component utilizes the resource. If control period is SW, resource sensor calculates MRA i (t) as:
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Ultra*Log PSU/IAI Final Report for
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Contents Contents .................
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Executive Summary Ultra*Log is a De
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2.3 Gnanasambandam, N., Lee, S., Ku
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6 Characterization and analysis of
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timizing simultaneously the link de
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where ∆ 1 (j) ≥ 0 and ∆ 2 (i,
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Table 2. GA Results Agent N A D max
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connected component, in which a pat
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Random Small-world Scale-free with
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Growth mechanisms Start with a smal
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Table 2. The proposed network’s c
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DMAS Controller. The functional uni
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1000 500 0 -500 -1000 0.2 0.4 0.6 0
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Page 44 and 45: 1 SITUATION IDENTIFICATION USING DY
Page 46 and 47: 3 3.2 Behavior In SSC society an ag
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Page 50 and 51: Estimating Global Stress Environmen
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Page 60 and 61: Extensive testing and validation of
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Page 72 and 73: Table 1: Notation Symbol Descriptio
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Page 118 and 119: component is a member of one of the
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Manuscript for IEEE TRANSACTIONS ON
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Coordinating Control Decisions of S
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directly. It has coarser scale. It
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Understanding Agent Societies Using
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• For tasks, the UID of the direc
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for tracking the dependencies betwe
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within the monitored enclave. With
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Figure 1. Agent Hierarchy in CPE So
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Figure 3. The World Model CPY Agent
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Table 2. TechSpecs: Infrastructure
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terms of the average waiting times
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Acknowledgements The work described
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key realization to tackle this prob
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are or ignored perturbations. The e
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sustained oscillations. If the inst
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solved by biological systems for li
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non-linear and non-stationarity. Ne
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D k : Outflow from high priority qu
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the system starts to perform self-s
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sustained in a supply chain. Resour
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which are necessary to make good mo
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focusing on the computational compl
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line of research that deals with ne
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ealizable. But the inherent complex
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Min, H. and Zhou, G., 2002, Supply
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In the rest of this paper we report
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shows relatively irregular behavior
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