DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Proceedings of the 1st Open Cougaar Conference 6 The auction mechanism described as a decentralized coordination incorporates a centralized seller. As a centralized auction can still exhibit problems in terms of scalability and robustness we introduce a hierarchical auction mechanism. Suppose that T a * is optimal of agent group a and T b * is optimal of agent group b. If a ⊂ b, then we can say that: * * a T b T ≤ . Through this property we can convert the auction mechanism into a hierarchical one, in which there are multiple auction markets that are structured hierarchically. Each auction solves its decision problem based on the bids from the agents or subordinate auctions and makes a bid to its superior auction with T larger than and equal to its optimal completion time. This hierarchical structure makes improvements with respect to scalability and robustness compared to central auction mechanism. Scalability improves because bids and decisions are distributed to multiple auctions in the hierarchical framework. And, robustness improves because there is no single point of failure. 6. Empirical result We ran several experiments to validate the proposed control approach through discrete-event simulation. 6.1. Experimental design The network is composed of fifteen agents with a convergent structure as in figure 1, in which each link is assigned 1 (l ij ). In this network each agent in the lowest position has 200 root tasks. Each of the agents has the same linear value function and the cost of completion time is linear as described in the figure. A 2 A 4 A 5 A 1 CCT(T) = 4T Figure 1. Experimental network configuration To observe adaptive behavior we assign weight w i to an agent and w′ i to a stressor residing in a same machine with the agent for proportional resource share between them. A stressor, which has infinite work (continuously A 3 A 8 A 9 A 10 A 11 A 6 A 7 A 12 A 13 A 14 A 15 200 200 200 200 200 200 200 200 requiring resources), can impose different levels of stress on the agent directly by changing w′ i . When it is zero there is no stress, and as it increases the stress level increases. We implement our stress environment simply by using Weighted Round-Robin scheduling, in which each thread gets a number of quanta in proportion to its weight. We set up four different experimental conditions as in table 1. In stressed conditions we stress agent A 4 in the middle of run. And, the distribution of CPU time in value function can be deterministic or exponential. While using stochastic value function we ran 5 experiments. Table 1. Experimental conditions Condition Stress Value function Con1 W/o Stress Deterministic Con2 W/o Stress Exponential Con3 W/Stress Deterministic Con4 W/Stress Exponential * Control period: 100 * w i : 0.1, w′ 4 : 1 in (500, 1000) We use three different control modes for each experimental condition. Table 2 shows the control modes we used for experimentation. AC represents the adaptive control mechanism we have developed. Table 2. Control modes for experimentation Control mode Description FL Fixed lowest value mode FH Fixed highest value mode AC Adaptive control 6.2. Results Experimental results are summarized in table 3. The proposed adaptive control showed significant advantages compared to non-adaptive cases in all different conditions. Table 3. Experimental results FL FH AC T V QoS T V QoS T V QoS Con1 1656 13558 6934 6313 30643 5391 1663 22898 16245 Con2 1652 13547 6942 6302 30643 5435 1723 22982 16089 Con3 1656 13558 6934 6313 30643 5391 1966 23401 15539 Con4 1652 13547 6942 6371 30643 5159 2024 23495 15401 * T: Completion time * V: Value of solution The adaptive behaviors under proposed control mechanism are shown in figures 2 and 3 for deterministic case, and figures 4 and 5 for stochastic case. These represent time series of decision variables at each control point. Under stress the system changes its behavior
Proceedings of the 1st Open Cougaar Conference 7 adaptively to the new environment. And, once the stress is removed the system adapts again. 4000 3500 mode 6.0 5.0 4.0 3.0 A 8 A 2 A 1 A 4 optimal T 3000 2500 2000 1500 1000 500 2.0 0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 time mode optimal T 1.0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 time Figure 2. Adaptive value mode under Con3 4000 3500 3000 2500 2000 1500 1000 500 0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 time Figure 3. Adaptive optimal T under Con3 A 8 6.0 A 2 5.0 4.0 A 1 3.0 A 4 2.0 1.0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 time Figure 4. Adaptive value mode under Con4 Figure 5. Adaptive optimal T under Con4 7. Summary and conclusions A typical information network emerges as a result of automation or organizational integration. These networks are large-scale with distributed and component-based architecture. As such networks can be easily exposed to various adverse events such as accidental failures and malicious attacks, there is a need to study survivability of the networks. In this paper we studied the emerging networks to support survivability by utilizing implementation alternatives. By adopting MPC-style approach considering its benefits with respect to complexity, optimality, and agility, we developed an adaptive control mechanism with scalability and predictability. To address adaptivity we modeled the stress environment indirectly by quantifying resource availability of the system. We built a mathematical programming model with the resource availability incorporated, which predicts QoS as a function of control actions. By periodically solving the programming model and taking optimal control actions with recent resource availability, the system could be adaptive to the changing stress environment predictably. But, as the programming model can be large-scale and complex, we agentified the components of the network from control point of view so that the system can solve the large-scale programming model in a decentralized mode. We provided a hierarchical auction mechanism as a coordination mechanism. We showed the effectiveness of our approach regarding to QoS and adaptivity in different experimental conditions. Our approach can be extended for the network configurations where there are multiple agents in a machine sharing resources together. In this case we have a good opportunity to improve the system performance by appropriately allocating resources to the agents. To implement the proposed control mechanism in information networks such as UltraLog network, we need to devise several things which are discussed in
Page 1 and 2: Ultra*Log PSU/IAI Final Report for
Page 3 and 4: Contents Contents .................
Page 5: Executive Summary Ultra*Log is a De
Page 8 and 9: 2.3 Gnanasambandam, N., Lee, S., Ku
Page 10 and 11: 6 Characterization and analysis of
Page 12 and 13: timizing simultaneously the link de
Page 14 and 15: where ∆ 1 (j) ≥ 0 and ∆ 2 (i,
Page 16 and 17: Table 2. GA Results Agent N A D max
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Page 20 and 21: Random Small-world Scale-free with
Page 22 and 23: Growth mechanisms Start with a smal
Page 24 and 25: Table 2. The proposed network’s c
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Page 30 and 31: DMAS Controller. The functional uni
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Page 36 and 37: Proceedings of the 1st Open Cougaar
Page 44 and 45: 1 SITUATION IDENTIFICATION USING DY
Page 46 and 47: 3 3.2 Behavior In SSC society an ag
Page 48 and 49: 5 All the behavior parameters may n
Page 50 and 51: Estimating Global Stress Environmen
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Page 54 and 55: 100% 1 95% 2 14 15 64% TAO 4 64% 62
Page 56 and 57: interactions as a dynamical system
Page 58 and 59: ehavior states under varying system
Page 60 and 61: Extensive testing and validation of
Page 62 and 63: 5 Conclusions and Future Research T
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Page 66 and 67: 4 points into a corresponding inner
Page 68 and 69: 6 stresses well. The Warehouse 1 ag
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Page 72 and 73: Table 1: Notation Symbol Descriptio
Page 74 and 75: 4 Conclusions and Future Work 4.1 C
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Manuscript for IEEE Transactions on
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2 discuss problem domain and in Sec
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4 t Si t ∫ + ( ) i ) t When RA i
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6 S UB i ∑ = P LI / LI . (16) i p
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8 policies while underutilizing in
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architecture based on both the Grid
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to machines (network topology) and
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component is a member of one of the
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denotes the immediate predecessors
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limit of large number of tasks. If
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Min-min heuristic algorithm Step 1:
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We set up eight different experimen
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0. 4 8057 8237 0.5 6439 6635 0.6 53
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pp. 191-200. [3] I. Foster, C. Kess
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Technology, Cambridge, MA, 1995. [2
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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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