DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Manuscript for IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 4 solution) and (plan) completion time. These two metrics directly affect the performance of the operation. In this paper we design a control mechanism for such novel networks. We stress scalability with respect to computational complexity as well as communicational overhead, as an important consideration of the control mechanism for its practical use. The control mechanism should be able to supply appropriate control policy in a timely manner even though the size of the network is large. Such a property is important especially when completion time is an explicit consideration as in our control problem. However, the property is hard to achieve in general if one pursues exactly optimal policy. Therefore, we design a scalable control mechanism by sacrificing some amount of optimality in a systematic way as follows. First, we adopt Model Predictive Control (MPC) as our control framework. 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 [10]-[13]. 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. Note that technologies such as Dynamic Programming are not efficient in terms of computational complexity as they try to give optimal closed-loop control policy. Second, under MPC framework, we build a heuristic programming model due to computational complexity. The heuristic model is solvable in polynomial time and its solution converges to the solution of exact model in the limit of large number of tasks. Third, we provide a decentralized coordination mechanism for solving the programming model. Computations and communications are distributed to multiple entities through an auction market while giving a solution equivalent to the solution of the programming model.
Manuscript for IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 5 The organization of this paper is as follows. In Section 2 we formally define the problem in detail. After designing the control mechanism in Sections 3 and 4, we show empirical results in Section 5. Finally, we discuss implications and possible extensions of our work in Section 6. 2. Problem specification In this section we formally define the control problem by detailing network configuration and control actions. We focus on computational CPU resources assuming that the system is computation-bounded. 2.1 Network configuration A network is composed of a set of components A and a set of nodes (i.e., machines) N. K n denotes a set of components that reside in node n sharing the node’s CPU resource. Task flow structure of the network, which defines precedence relationship between components, is an arbitrary directed acyclic graph. A problem given to the network is decomposed in terms of root tasks for some components and those tasks are propagated through the task flow structure. Each component processes one of the tasks in its queue (which has root tasks as well as tasks from predecessor components) and then sends it to successor components. We denote the number of root tasks of component i as rt i . Fig. 1 shows an example network in which there are four components residing in three nodes. Components A 1 and A 2 reside in N 1 and each of them has 100 root tasks. A 3 in N 2 and A 4 in N 3 have no root tasks, but they have 200 and 100 tasks respectively from the corresponding predecessors.
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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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tems. Proceedings of the Second Joi
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Proceedings of the 1st Open Cougaar
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1 SITUATION IDENTIFICATION USING DY
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3 3.2 Behavior In SSC society an ag
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5 All the behavior parameters may n
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Estimating Global Stress Environmen
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chaotic deterministic time series.
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100% 1 95% 2 14 15 64% TAO 4 64% 62
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interactions as a dynamical system
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ehavior states under varying system
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Extensive testing and validation of
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5 Conclusions and Future Research T
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2 to function critically even under
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4 points into a corresponding inner
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6 stresses well. The Warehouse 1 ag
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Table 1: Notation Symbol Descriptio
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4 Conclusions and Future Work 4.1 C
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O, U Stresses Physical/Infrastructu
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Manuscript for IEEE Transactions on
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Page 106 and 107: 2 discuss problem domain and in Sec
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Page 172 and 173: Table 2. TechSpecs: Infrastructure
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Page 176 and 177: Acknowledgements The work described
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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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