DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

More documents

Recommendations

Info

Table 2. TechSpecs: Infrastructure Perspective an open system because tasks constantly enter and exit the system. • Does any parameter of the model require empirical data from the actual society? Although some aspects in this research are currently being resolved, the following observations can be made. 4. QoS Metrics such as freshness are in terms of average waiting times at several nodes ( ∑ W ij , i is the node, j is the class of traffic); 5. If tasks follow a particular route (or flow as described in Section 3.1.1), then that route gets associated to a class of traffic; 6. If a particular task goes into the node and gets converted to another task, we say class-switching has occured. For example, in our application update tasks go to BN and get converted to plan tasks; 7. If a connection exists between two nodes, this is converted to a transition probabilty p ij , where i is the source and j is the target node. Using the above rules as well as the aforementioned representations of TechSpecs we develop a mapping between the TechSpecs and a queueing model. Although the current procedure is manual, in thoery this procedure could be automated. Such an automatic capabilty of translating TechSpecs would prove very beneficial for predicting performance of the MAS in real-time. Table 3 captures the queueing model abstraction from TechSpecs for the CPY agents. Similarly, we can establish the mapping for other agents as well. Some useful guidelines that were followed in order to translate the TechSpecs into models are as follows: • Identify flows of traffic: Trace the route followed by each type of packet completely within the system boundary i.e. from the entry into the system until it exits the system. These would subsequently form classes of traffic in the queueing model. Care has to be taken to note any class switching. • Identify the network type: The network could be closed (fixed number of tasks) or open. The CPE is • Who does the TechSpecs translation? Where does the model run? In our case the translation is done manually at present. The model would run at a place visible to the controller (possibly as a seperate agent at the highest level). The controller we refer to here is the actual effector of control actions throughout the CPE society and is seperate from all we have discussed so far. The role of the controller is also to balance between other threads such as robustness and security. • The identification of control alternatives is currently centralized. However, we visualize a decentralized, hierarchical controller for effecting the changes. 4. Queueing Network Models (QNMs) A complex logistics system such as the CPE society has numerous interactions. Yet, if the functionalities are abstracted to capture some application level specifics in terms of queueing model elements (example as shown in Table 3), analytical predictions on the behavior of the MAS can be made. Analytical models are good candidates for enforcing adaptive control quickly and in real-time. Each agent behaves like a server that process jobs waiting in line. Hence, the mapping between an agent and a server with a queue is easily established. Because of the task flow structure and the superior-subordinate relationships in the TechSpecs, queues can be connected in tandem with jobs entering and exiting the system. This results in the formation of an open queuing network. We conducted initial experiments using an actual Cougaar based MAS, an analytical formulation and an Arena simulation. We used this experiment to bootstrap our modeling process in terms of parameter estimation and calibration. However, working with the MAS was timeconsuming as our goal was to identify modeling alternatives and control ramifications. Hence we continued our experimentation with a scaled up queueing model and simulation with the insight gained from working with the actual society. Thus the open queueing network’s parameters were carefully chosen and tasks sub-divided into mutiple-classes to denote a particular task within the MAS. The TechSpecs clearly delineate the input and output tasks facilitating the 60
Table 3. Queuing Model Abstraction from TechSpecs for CPY Agent Figure 4. Task Flow in the MAS mapping to arrivals and services in a queueing network. Application level QoS measures of the MAS are calcuated in terms of the waiting times (or other equivalent perfromance measures) at the individual nodes of the QNM. Figure 4 is a representation of the CPE society from a queueing perspective. We show two types of tasks flowing in the network namely the plan (denoting maneuver and sustainment) and the update tasks. These tasks can be divided further into three classes of traffic. The first class refers to update packets entering at the CPY nodes and proceeding further as updates to BDE through BN. Class 2 relates to those update packets that are converted to plan tasks. There is class-switching at nodes 2 and 3 and we introduce approximations to deal with this later in the paper. The third class relates to the maneuver plan tasks that reach SUPP nodes through CPY. Although we know multiple task types exist in the MAS, by making the simplifying assumption and treating all job classes alike we analyze the MAS using Jackson networks [5] in Section 4.1. We further analyze the system taking into accout multiple classes of traffic as discussed in Section 4.2. We compare the two analytical approaches with a simulation model. 4.1 Jackson Network Model We apply a single class Jackson network [5] formulation for open queuing networks to our example by choosing a weighted average service time for nodes with multiple classes. The nine agents of the MAS considered here can then be assumed to be M/M/1 systems. The arrival rates of the open network can be computed by solving the traffic equations. Assuming the load is balanced to start-with, the routing probabilities are also known. If each node of the system is ergodic, we can calculate the steady state probabilities and performance measures of the entire network by computing these measures for every agent exactly as in an M/M/1 system. We consider a simple example. For this queueing model, we assume all tasks are of a single type and do not distinguish between classes as shown in Figure 4. Let λ 0i and λ i0 be the rate of arrival and exit into and from the i th node respectively. Since the routing probabilties are known we can calculate the arrival rates λ i of each of the nodes of the open network by solving the following traffic equations: λ i = λ 0i + 9∑ λ j p ji , i =1, ..., 9 . j=1 The routing probabilties (p ji : probability from i (column index) to j (row index)) for the balanced case are as follows: 0 1/5 1/5 0 0 0 0 0 0 0 0 0 1/4 1/4 1/4 1/4 0 0 0 0 0 1/4 1/4 1/4 1/4 0 0 0 1/5 1/5 0 0 0 0 0 0 0 1/5 1/5 0 0 0 0 0 0 0 1/5 1/5 0 0 0 0 0 0 0 1/5 1/5 0 0 0 0 0 0 0 0 0 1/4 1/4 1/4 1/4 0 0 0 0 0 1/4 1/4 1/4 1/4 0 0 Note that the customer exits from a node i with probability 1 − ∑ j p ji. Once the arrival rates are known, we can calculate the average waiting times at the nodes by using the following formula: 1/µ i W i = , i =1, ..., 9 . 1 − (λ i /µ i ) The QoS metrics namely maneuver plan freshness (MPF) and sustainment plan freshness (SPF) are calculated in 61
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
Page 18 and 19:
connected component, in which a pat
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
Page 26 and 27:
¡ ¡ ¢ £ ¤ ¥ ¦ £ § ¨ © ¥
Page 28 and 29:
© © ¤ ¢ ¨ ¤ £ ¦ ¨ © §
Page 30 and 31:
DMAS Controller. The functional uni
Page 32 and 33:
1000 500 0 -500 -1000 0.2 0.4 0.6 0
Page 34 and 35:
tems. Proceedings of the Second Joi
Page 36 and 37:
Proceedings of the 1st Open Cougaar
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
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
Page 52 and 53:
chaotic deterministic time series.
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
Page 64 and 65:
2 to function critically even under
Page 66 and 67:
4 points into a corresponding inner
Page 68 and 69:
6 stresses well. The Warehouse 1 ag
Page 70 and 71:
© £ ¨ ¥ ¤ § ¢ ¥ ©
Page 72 and 73:
Table 1: Notation Symbol Descriptio
Page 74 and 75:
4 Conclusions and Future Work 4.1 C
Page 76 and 77:
O, U Stresses Physical/Infrastructu
Page 78 and 79:
Manuscript for IEEE Transactions on
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
2 discuss problem domain and in Sec
Page 108 and 109:
4 t Si t ∫ + ( ) i ) t When RA i
Page 110 and 111:
6 S UB i ∑ = P LI / LI . (16) i p
Page 112 and 113:
8 policies while underutilizing in
Page 114 and 115:
architecture based on both the Grid
Page 116 and 117:
to machines (network topology) and
Page 118 and 119:
component is a member of one of the
Page 120 and 121:
denotes the immediate predecessors
Page 122 and 123: limit of large number of tasks. If
Page 124 and 125: Min-min heuristic algorithm Step 1:
Page 126 and 127: We set up eight different experimen
Page 128 and 129: 0. 4 8057 8237 0.5 6439 6635 0.6 53
Page 130 and 131: pp. 191-200. [3] I. Foster, C. Kess
Page 132 and 133: Technology, Cambridge, MA, 1995. [2
Page 134 and 135: Manuscript for IEEE TRANSACTIONS ON
Page 156 and 157: Coordinating Control Decisions of S
Page 158 and 159: directly. It has coarser scale. It
Page 160 and 161: Understanding Agent Societies Using
Page 162 and 163: • For tasks, the UID of the direc
Page 164 and 165: for tracking the dependencies betwe
Page 166 and 167: within the monitored enclave. With
Page 168 and 169: Figure 1. Agent Hierarchy in CPE So
Page 170 and 171: Figure 3. The World Model CPY Agent
Page 174 and 175: terms of the average waiting times
Page 176 and 177: Acknowledgements The work described
Page 178 and 179: key realization to tackle this prob
Page 180 and 181: are or ignored perturbations. The e
Page 182 and 183: sustained oscillations. If the inst
Page 184 and 185: solved by biological systems for li
Page 186 and 187: non-linear and non-stationarity. Ne
Page 188 and 189: D k : Outflow from high priority qu
Page 190 and 191: the system starts to perform self-s
Page 192 and 193: sustained in a supply chain. Resour
Page 194 and 195: which are necessary to make good mo
Page 196 and 197: focusing on the computational compl
Page 198 and 199: line of research that deals with ne
Page 200 and 201: ealizable. But the inherent complex
Page 202 and 203: Min, H. and Zhou, G., 2002, Supply
Page 204 and 205: In the rest of this paper we report
Page 206: shows relatively irregular behavior
show all

DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

Create successful ePaper yourself

Delete template?

Save as template?