DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

More documents

Recommendations

Info

Table 2. GA Results Agent N A D max , GA Ratio Society No. l max C C N A 1 4 2, 2 15 3.75 2 7 ” 27 3.86 3 10 5, 4 30 3.00 4 12 ” 33 2.75 5 15 ” 50 3.33 6 19 ” 61 3.21 7 24 ” 87 3.63 8 33 ” 108 3.27 9 40 ” 164 4.1 10 50 ” 188 3.76 The Table 2, shows that the optimal cost per agent ratio ( C N A ) is fairly constant, with a small variation. This may be a result of the cost structure assumed and the particular instances of the agent societies considered. This observation, however, can have following implication: given a very large agent society like with N A = 5, 000 agents, we can decompose it into smaller agent societies, solve the optimization problem for each of the sub-societies and then combine them to solve the overall problem. Due to the constancy of the ratio C N A , this heuristic should lead to solutions which are fairly close to optimal. As a final example we consider one of the realistic agent societies which has been developed in the Ultra*Log Program [14], [6]. The society is shown below in Figure 3 and represents a typical military supply chain (MSC). The structure of society is an exact replica of the true society, however the processing and band width requirements for the agents have been assigned randomly. The result obtained by GAs has been shown in the Figure 4. 6 Conclusion and Future Work In this paper we have systematically studied the issue of survivability of DIS. Based on these we formulated a robust design problem for DIS. We showed that this problem is NP hard in strong sense and used GAs to obtain solutions for a number of example agent societies. We also considered a realistic agent society representing a military supply chain. We showed that our robust design problem formulation results in a fairly survivable DIS. Survivability of DIS is an emerging area and future research is possible in a number of varied directions. Refining the robust design problem we have posed and developing heuristic solution methodologies for it. Further research is required to build mechanisms for survivability against other types of attacks, such as security and DOS attacks. Most of the above stated problems are nothing new for biological systems which have routinely solved them for literally millions of years. Can we draw inspiration from the structures discovered in biology to solve problems of distributed systems? We believe that the quest for “open-ended” survivability for DIS can be achieved only by exporting biological mechanisms into software systems. Acknowledgements The authors acknowledge DARPA (Grant#: MDA972-1-1-0038 under Ultra*Log Program) and NSF (Grant#:ANI-0219747 under ITR program) for their generous support for this research. Special thanks to the anonymous reviewers for their comments and suggestions. References [1] W. Aiello, B. Awerbuch, B. M. Maggs, and S. Rao. Approximate load balancing on dynamic and asynchronous networks. In ACM Symposium on Theory of Computing, pages 632–641, 1993. [2] J. Andreoli, U. Borghoff, R. Pareschi, S. Bistarelli, U. Montatnari, and F. Rossi. Constraints and agents for a decentralized network infrastructure. In AAAI Workshop, Menlo Park, California, USA: AAAI Press., pages 39–44, 1997. [3] A. Balakrishnan and K. Altinkemer. Using hop-constrined model to generate alternative= communication network design. ORSA Journal on Computing, 4(2), 1992. [4] A. Balakrishnan, T. l. Magnanti, and P. Mirchandani. A dual-based algorithm for multi-level network design. Management Science, 40(5):567–581, 1994. [5] A. Balakrishnan, T. l. Magnanti, and P. Mirchandani. Connectivity-splitting models for survivable network design. Submitted, 2003. [6] M. Brinn and M. Greaves. Leveraging agent properties to assure survivability of distributed multi-agent systems. In https://docs.ultralog.net/dscgi/ds.py/Get/File- 4088/AA03-SurvivabilityOfDMAS.pdf, 2002. [7] R. F. Cancho and R. V. Sole. Optimization in complex networks. In http://arxiv.org/PS cache/condmat/pdf/0111/0111222.pdf, 2001. [8] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multi-objective genetic algorithm-nsga-ii. In http://www.iitk.ac.in/kangal/pub.htm, 2000. [9] M. O. Hofmann, A. McGovern, and K. R. Whitebread. Mobile agents on the digital battlefield. In Proceedings of the 2nd International Conference on Autonomous Agents (Agents’98), pages 219–225, 1998. [10] KanGAL. Kanpur genetic algorithm laboratory. In http://www.iitk.ac.in/kangal/pub.htm. [11] J. O. Kephart, T. Hogg, and B. A. Huberman. Collective behavior of predictive agents. Physics D, 42:48–65, 1990. [12] A. Martello and P. Toth. Knapsack Problems Algorithms and Computer Implementations. John Wiley and Sons, 1990. [13] C. Tomlin, G. Pappas, and S. Sastry. Conflict resolution for air traffic management: A case study in multi-agent hybrid systems. IEEE Trans. on Automat. Ctrl, 43(4), 1998. [14] ULTRALOG. A darpa program on logistics infromation system= survivability. In http://www.ultralog.net/.
D e p e n d a b l e A g e n t S y s t e m s Survivability of Multiagent-Based Supply Networks: A Topological Perspective Hari Prasad Thadakamalla, Usha Nandini Raghavan, Soundar Kumara, and Réka Albert, Pennsylvania State University Supply chains involve complex webs of interactions among suppliers, manufacturers, distributors, third-party logistics providers, retailers, and customers. You can improve a multiagent-based supply network’s survivability by concentrating on the topology and its interplay with functionalities. Although fairly simple business processes govern these individual entities, real-time capabilities and global Internet connectivity make today’s supply chains complex. Fluctuating demand patterns, increasing customer expectations, and competitive markets also add to their complexity. Supply networks are usually modeled as multiagent systems (MASs). 1 Because supply chain management must effectively coordinate among many different entities, a multiagent modeling framework based on explicit communication between these entities is a natural choice. 1 Furthermore, we can represent these multiagent systems as a complex network with entities as nodes and the interactions between them as edges. Here we explore the survivability (and hence dependability) of these MASs from the view of these complex supply networks. Today’s supply networks aren’t dependable—or survivable—in chaotic environments. For example, Figure 1 shows how mediocre a typical supply network’s reaction to a node or edge failure is compared to a network with built-in redundancy. Survivability is a critical factor in supply network design. Specifically, supply networks in dynamic environments, such as military supply chains during wartime, must be designed more for survivability than for cost effectiveness. The more survivable a network is, the more dependable it will be. We present a methodology for building survivable large-scale supply network topologies that can extend to other large-scale MASs. Building survivable topologies alone doesn’t, however, make an MAS dependable. To create survivable—and hence dependable—multiagent systems, we must also consider the interplay between network topology and node functionalities. A topological perspective To date, the survivability literature has emphasized network functionalities rather than topology. To be survivable, a supply network must adapt to a dynamic environment, withstand failures, and be flexible and highly responsive. These characteristics depend on not only node functionality but also the topology in which nodes operate. The components of survivability From a topological perspective, the following properties encompass survivability, and we denote them as survivability components. The first is robustness. A robust network can sustain the loss of some of its structure or functionalities and maintain connectedness under node failures, whether the failure is random or is a targeted attack. We measure robustness as the size of the network’s largest 24 1541-1672/04/$20.00 © 2004 IEEE IEEE INTELLIGENT SYSTEMS Published by the IEEE Computer Society
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 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 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 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Page 152 and 153:
Page 154 and 155:
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 172 and 173:
Table 2. TechSpecs: Infrastructure
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 ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?