DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Manuscript for IEEE Transactions on Automatic Control 28 [25] M. Nikolaou, “Model predictive controllers: A critical synthesis of theory and industrial needs,” in Advances in Chemical Engineering Series, Academic Press, 2001. [26] S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive technology,” Control Engineering Practice, vol. 11, pp. 733-764, 2003. [27] Y. Lengwiler, “The multiple unit auction with variable supply,” Economic Theory, vol. 14, no. 2, pp. 373-392, 1999.
1 Self-Organizing Resource Allocation for Minimizing Completion Time in Large-Scale Distributed Information Networks Seokcheon Lee, Soundar Kumara, and Natarajan Gautam Abstract—As information networks grow larger in size due to automation or organizational integration, it is important to provide simple decision-making mechanisms for each entity or groups of entities that will lead to desirable global performance. In this paper, we study a large-scale information network consisting of distributed software components linked together through a task flow structure and design a resource control mechanism for minimizing completion time. We define load index which represents component’s workload. When resources are allocated locally proportional to the load index, the network can maximize the utilization of distributed resources and achieve optimal performance in the limit of large number of tasks. Coordinated resource allocation throughout the network emerges as a result of using the load index as global information. To clarify the obscurity of “large number of tasks” we provide a quantitative criterion for the adequacy of the proportional resource allocation for a given network. By periodically allocating resources under the framework of model predictive control, a closed-loop policy reactive to each current system state is formed. The designed resource control mechanism has several emergent properties that can be found in many self-organizing systems such as social or biological systems. Though it is localized requiring almost no computation, it realizes desirable global performance adaptive to changing environments. Index Terms—Distributed information networks, emergence, resource allocation, scalability. C I. INTRODUCTION ritical infrastructures are increasingly becoming dependent on networked systems in many domains due to automation or organizational integration. The growth in complexity and size of software systems is leading to the increasing importance of distributed and component-based architectures. Distributed computing aims at using computing power of machines Manuscript received June 24, 2005. This work was supported in part by DARPA under Grant MDA 972-01-1-0038. S. Lee is with the Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802 USA (phone: 814-863-4799; fax: 814-863-4745; e-mail: stonesky@psu.edu). S. Kumara is with the Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802 USA (e-mail: skumara@psu.edu). N. Gautam is with the Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802 USA (e-mail: ngautam@psu.edu). connected by a network. When a task requires intensive computation, it becomes natural choice to achieve high performance. A component is a reusable program element. Component technology utilizes the components so that developers can build systems needed by simply defining their specific roles and wiring them together [1][2]. In networks with component-based architecture, each component is highly specialized for specific tasks. We study a large-scale information network (with respect to the number of components as well as machines) comprising of distributed software components linked together through a task flow structure. A problem given to the network is decomposed in terms of root tasks for some components and those tasks are propagated through a task flow structure to other components. As a problem can be decomposed with respect to space, time, or both, a component can have multiple root tasks that can be considered independent and identical in their nature. The service provided by the network is to produce a global solution to the given problem, which is an aggregation of the partial solutions of individual tasks. Quality of Service (QoS) of the network is determined by the time for generating the global solution, i.e. completion time. For a given topology, components are sharing resources and the network can control its behavior through resource allocation. In specific, we address allocating resources of each machine to the components residing on that machine. In this paper we develop a resource control mechanism of such networks for minimizing completion time. Many self-organizing systems such as social and biological systems exhibit emergent properties. Though entities act with a simple mechanism without central authority, these systems are adaptive and desirable global performance can often be realized. The control mechanism designed in this paper has such properties so that it can be applicable to large-scale networks working in a dynamic environment. Scalability, defined as “the ability of a solution to some problem to work when the size of the problem increases” (From Dictionary of Computing at http://wombat.doc.ic.ac.uk), becomes a critical issue when developing practical software systems as the size of networks grows [3]. We also provide a criterion by which one can evaluate if the emergent properties hold for a given network. The organization of this paper is as follows. In Section II we
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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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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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