DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Table 1: Notation Symbol Description N Total # of nodes in the community λ ij Average arrival rate of class j at node i 1/µ ijk Average processing time of class j at node i at quality k M Total number of classes T i Routing probability matrix for class i W ijk Steady state waiting time for class j at node i at quality k Set of qualities at which a class j task can be processed at node i Q ij The primary performance prediction tool that we use are called Queueing Network Models (QNM) [6]. The QNM is the representation of the agent community in the queueing domain. As the first step of performance estimation, the agent community needs to be translated into a queueing network model. Table 1 provides the notations used is this section. Inputs and outputs at a node are regarded as tasks. The rate at which tasks of class j are received at node i is captured by the arrival rate (λ ij ). Actions by agents consume time, so they get abstracted as processing rates (µ ij ). Further, each task can be processed at a quality k ∈ Q ij , that causes the processing rates to be represented as µ ijk . Statistics of processing times are maintained at each agent in the Performance Database (PDB) to arrive at a linear regression model between quality k and µ ijk . Flows get associated with classes of traffic denoted by the index j. If a connection exists between two nodes, this is converted to a transition probability p ij , where i is the source and j is the target node. Typically, we consider flows originating from the environment, getting processed and exiting the network making the agent network an open queueing network [6]. Since we may typically have multiple flows through a single node, we consider multi-class queueing networks where the flows are associated with a class. Performance metrics such as delays for the “sense-plan-respond” cycle is captured in terms of average waiting times, W ijk . As mentioned earlier, TechSpecs is a convenient place where information such as flows and Q ij can be embedded. The choice of QNM depends on the number of classes, arrival distribution and processing discipline as well as a suggestion C by the DMAS controller that makes this choice based upon history of prior effectiveness. Some analytical approaches to estimate performance can be found in [6, 11]. In the context of agent networks, Jackson and BCMP queueing networks have been applied to estimate the performance in [12]. By extending this work we provide several templates of queueing models (such as BCMP, Whitt’s QNA [11], Jackson, M/G/1, a simulation) that can be utilized for performance prediction. 2.5 Self-Controlling Capability In contrast to [5], we deal with optimization of the domain utility of a MAS that is distributed, rather than allocating resources in an optimal fashion to multiple applications that have a good idea of their utility function (through policies). As mentioned before opmodes allow for trading-off quality of service (task quality and response time) and performance. We are assuming there is a maximum ceiling R on the amount of resources, and the available resources fluctuate depending on stresses S = S e +S a , where S e are the stresses from the environment (i.e. multiple contending applications, changes in the infrastructural or physical layers) and S a are the application stresses (i.e. increased tasks). The DMAS controller receives from MP (measurement points) a measurement of the actual performance P and a vector of other statistics (X) about task processing times. Also at the top-level the overall utility (U) is U(P, S) = ∑ w n x n is known where x n is the actual utility component and w n is the associated weight. We cannot change S, but we can adjust P to get better utility. Since P depends on O, which is a vector of opmodes collected from the community, we can use the QNM to find O ∗ and hence P ∗ that maximizes U(P, S) for a given S. In words, we find the vector of opmodes (O ∗ ) that maximizes domain utility at current S and update O. This computation is performed in the Utility Calculator module using a utility model that is learned and stored in the Utility Database (UDB). This formulation although independently found matches the self-optimization notion in [5]. But some differences exist as follows. Tesauro et al. [5] assume that the system’s knowledge includes a performance model, which we do not assume. We use a queueing network model to estimate the performance in real-time for any set of opmodes O ′ by taking the current set of opmodes O and scaling them appropriately based on observed histories (X) to X ′ in the Control Set Evaluator. Also, we deal with a single MAS with an overall utility function for the entire distributed functionality (within the community). Because of the interactions involved and complexity of performance modeling[3, 4], it may be time-consuming to utilize inferencing and learning mechanisms in real-time. This is why we use an analytical queueing network to get the performance estimate quickly. Another difference is that in [5], they assume operating system support which may not be true in many MAS-based situations because of mobility, security and real-time constraints. Furthermore, in addition to the estimation of performance, the queueing model may have the capability to eliminate instabilities from a queueing sense, which is not apparent in the other approach. But inspite of these differences, it is interesting to see that self-controlling capability can be achieved, with or without explicit layering, in a couple of real-world applications.
1000 500 0 0.2 0.4 0.6 0.8 -500 -1000 Default Policy Controlled Stress (S) Figure 2: Results overview 20 15 10 5 0 0 200 400 600 800 1000 1200 -5 -10 time (sec.) Controlled Default A Default B 1400 1200 1000 800 600 400 200 0 0 200 400 600 800 1000 1200 time (sec.) Controlled Default A Default B (a) Instantaneous Utility (stress 0.25) (b) Cumulative Utility (stress 0.25) 20 15 10 5 0 0 200 400 600 800 1000 1200 -5 -10 time (sec.) Controlled Default A Default B 1200 1000 800 600 400 200 0 0 200 400 600 800 1000 1200 time (sec.) Controlled Default A Default B (c) Instantaneous Utility (stress 0.75) (d) Cumulative Utility (stress 0.75) Figure 3: Sample results 3 Empirical Evaluation on CPE Test-bed We utilized the prototype CPE framework to run 36 experiments at two stress levels (S = 0.25 and 0.75). The scenario consisted of 14 agents, besides a world agent that created random scenarios in military logistics for the agents to react to. There were three layers of hierarchy with a three-way branching at each level and one supply node. The community’s utility function was based on the achievement of real goals in military engagements such as terminating or damaging the enemy and reducing the penalty involved in consuming resources such as fuel or sustaining damage. We also assumed for the model selection process that the external arrival was Poisson and the service times were exponentially distributed. In order to cater to general arrival rates, our framework contains a QNA- and simulation-based model. Using this assumption a BCMP or M/G/1 queueing model could be utilized. We used the Cougaar based default control without additional support from our framework as the baseline (denoted as Default A and Default B) and found that controlling the agent community using our framework (denoted as controlled) was beneficial in the long run. The overview of the results is provided in Figure 2. At both stress levels, the controlled scenario performed better that the default as shown in Figure 3. We did observe oscillations in the instantaneous utility and we attribute this to the impreciseness of the prediction of stresses. Stresses vary relatively fast in the order of seconds while the control granularity was of the order of minutes. Since this is a military engagement situation following no pre-determined stress patterns, it is hard to cope with in the higher stress case. We think that this could be the reason why our utility falls in the latter case.
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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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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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