DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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6 Characterization and analysis of supply chains from complex systems perspective With the advent of information technology, supply chains have acquired complexity that is almost equivalent to biological systems. In the following papers, we argue why supply chains should be treated as complex systems and propose how various concepts, tools and techniques used in complex adaptive systems literature can be exploited to characterize and analyze supply chain networks. 6.1 Surana, A., Kumara, S. R. T., Greaves, M. and Raghavan, U. N. “Supply Chain Network: A Complex Adaptive Systems Perspective”, International Journal of Production Research (to be published), 2005. 6.2 Kumara, S. R. T., Ranjan, P., Surana, A. and Narayanan, V., “Decision Making in Logistics: A Chaos Theory Based Approach”, CIRP Annals, p.381, 2003.
DESIGNING A NETWORK INFRASTRUCTURE FOR SURVIVABILITY OF MULTI-AGENT SYSTEMS A. Surana MIT, Cambridge, MA 02139 email: surana@mit.edu N. Gautam, S.R.T. Kumara Penn. State University, University Park, PA 16802 email: {ngautam, skumara}@psu.edu M. Greaves DARPA, 3701 Fairfax Drive Arlington, VA 22203-1714 email: mgreaves@darpa.mil ABSTRACT In this paper we consider a society of agents whose interactions are known. Our objective is to solve a strategic network infrastructure design problem to determine: (i) number of nodes (usually computers or servers) and their processing speeds, (ii) set of links between nodes and their bandwidths, and (iii) assignment of agents to nodes. From a performance standpoint, on one hand all the agents can reside in a single node thereby stressing the processor, on the other hand the agents can be distributed so that there is a maximum of one agent per node thereby increasing communication cost. From a robustness standpoint since links and arcs can fail (possibly due to attacks) we would like to build a network that is least disruptive to the multiagent system functionality. Although we do not explicitly consider tactical issues such as moving agents to different nodes upon failure, we would like to design an infrastructure that facilitates such agent migrations. We formulate and solve a mathematical program for the network infrastructure design problem by minimizing a cost function subject to satisfying quality of service (QoS) as well as robustness requirements. We test our methodology on Cougaar multi-agent societies. KEY WORDS network design, QoS, robustness, optimization. 1 Introduction As the number of applications requiring distributed multiagent systems (MAS) is continuously growing, it becomes extremely important to build a network infrastructure that can guarantee a survivable MAS architecture. By survivable we mean a system that is robust, secure as well as able to provide excellent quality of service (QoS) even when stressed. For example Brinn and Greaves [6] state that the Cougaar MAS in Ultra*Log [14], would be considered survivable if it would maintain at least x% of system capabilities and y% of system performance in the face of z% infrastructure loss and wartime loads (where x, y and z are provided by the system users). In order to build such a survivable system, there are several decisions that need to be made at different time granularities. These can be broken down as strategic (once a year or just one time), tactical (once a week to once a day depending on how often the configuration changes), and operational (typically milliseconds to seconds, depending on the granularity of information exchange) decisions. The strategic decisions typically involve designing the network infrastructure (in terms of both number and capacity) for the MAS such as computers, servers, cables, etc. The tactical decisions include where to migrate the agents if a node fails or is cut off from the other nodes. Operational decisions include adaptive control methods for deciding which agent should process a task, the fidelity with which to process a task, etc. There has been a lot of research related to (a) software technology, such as, agent architecture, communication, migration, adaptation, learning, etc., and (b) networking, such as, QoS provisioning, fault-tolerance, dependability, robustness, etc. However there is very little research that combines the two and studies them from a systems engineering viewpoint. In this research we address that shortcoming. We focus on the strategic problem stated in the previous paragraph of designing a network infrastructure in terms of hardware to support a given society of agents and their interactions. This is with the understanding that in order to solve the tactical and operational problems effectively, the strategic problem must favor a network design that would ease tactical and operational decisions. We now present some of the related research with the understanding that due to space restrictions it is difficult to cite all relevant articles in the literature. Andreoli et al [2] consider a distributed software network infrastructure for agents performing search tasks (such as search engines). Optimization issues for MAS at software level such as load balancing using non-linear programming techniques is studied in Aiello et al [1] for a given hardware topology. In Hofmann et al [9], a mobile intelligent agent system is built under conditions of low bandwidth to show that it could improve the efficiency of military tactical operations and the mobile agents would outperform static agents. Multiagent hybrid systems that combine computational hardware and a large scale software residing on it, with an application to air-traffic management is studied in Tomlin et al [13]. In Kephart et al [11], one of the emerging research areas, namely considering a distributed information system as analogous to biological ecosystem and social systems, is presented in order to study their survivability. Cancho and Sole [7] consider a complex network and show that by op-
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Page 3 and 4: Contents Contents .................
Page 5: Executive Summary Ultra*Log is a De
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Extensive testing and validation of
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5 Conclusions and Future Research T
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6 stresses well. The Warehouse 1 ag
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Table 1: Notation Symbol Descriptio
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4 Conclusions and Future Work 4.1 C
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O, U Stresses Physical/Infrastructu
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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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