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4 Conclusions and Future Work 4.1 Conclusions In this paper, we were able to successfully control a real-time MAS to achieve overall better utility in the long run using application-level trade-offs between quality of service and performance. We utilized a queueing network based framework for performance analysis and subsequently used a learned utility model for computing the overall benefit to the MAS (i.e. community). While Tesauro et al. [5] have found a similar construction to improve utility in multiple applications, we concentrated on optimizing the utility of a single distributed application using queueing theory. We think that the approaches are complementary, with this study providing empirical evidence to support the observation in [1] that agents can be used to optimize distributed application environments, including themselves, through flexible high-level (i.e. application-level) interactions. 4.2 Discussion and Future Work We believe that keeping the building-blocks small and the number of interactions (between performance and utility models) minimal may assist in making the framework more flexible and scalable. For example, if system size increases, we can consider a superior agent or human user to be at the next higher level controlling the weights in the utility function without affecting the performance model. The larger system with supervisory control would then be analyzed using another higher-level QNM or a network of networks. TechSpecs has assisted this effort to a large extent, re-emphasizing the well-founded separation principle (separating knowledge/policy and mechanism) in the computing field. While we think that the aforementioned architectural principles have been well-utilized, we hope to broaden the layered control approach to encompass the infrastructural-level control into the framework. Another avenue for improvement is to design self-protecting mechanisms within our framework so that the security aspect of the framework is reinforced. Acknowledgments The work described here was performed under the DARPA UltraLog Grant#: MDA972-1-1-0038. The authors wish to acknowledge DARPA for their generous support. References [1] Jennings, N. R. and Wooldridge, M., 2000, “Agent-Oriented Software Engineering”, Handbook of Agent Technology, AAAI/MIT Press. [2] UltraLog Program Site. www.ultralog.net. DARPA. [3] Jung, H., and Tambe, M., 2003, “Performance Models for Large Scale Multi-Agent Systems”, Proceedings of the Seocnd Joint Conference on Autonomous Agents and Multi-Agnet Systems. [4] Rana, O. F., and Stout, O., “What is Scalability in Mult-Agent Systems”, 2000, Proceedings of the Fourth International Conference on Autonomous Agents. [5] Tesauro, G., Chess, D. M., Walsh, W. E., Das, R., Whalley, I., Kephart, J. O., and White, S. R., 2004, “A Multi-Agent Systems Approach to Autonomic Computing”, Autonomous Agents and Multi-Agent Systems. [6] Bolch, G., de Meer, H., Greiner, S., and Trivedi, K. S., 1998, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications. John Wiley and Sons. [7] Thadakamalla, H. P., Raghavan, U. N., Kumara, S. R. T., and Albert, R., 2004, “Survivability of Multi-Agent Systems - A Topological Perspective”, IEEE Intelligent Systems: Dependable Agent Systems, vol. 19, no. 5, pp. 24-31, Sep/Oct 2004. [8] Hong, Y., and Kumara, S. R. T., 2004, “Coordinating Control Decisions of Software Agents for Adaptation to Dynamic Environments”, Working Paper, Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State Univerity, University Park, PA. [9] Brinn, M., and Greaves, M., 2003, “Leveraging Agent Properties to Assure Survivability of Distributed Multi-Agent Systems”, in the Proceedings of the Second Joint Conference on Autonomous Agents and Multi-Agent Systems. [10] Cougaar Open Source Site. www.cougaar.org. DARPA. [11] Whitt, W., 1983, “The Queueing Network Analyzer”, The Bell System Technical Journal, vol. 62, no. 9, pp. 2779-2815. [12] Gnanasambandam, N., Lee, S., Gautam, N., Kumara, S. R. T., Peng, W., Manikonda, V., Brinn, M., and Greaves, M., 2004, “Reliable MAS Performance Prediction Using Queueing Models”, IEEE Multi-Agent Security and Survivability Symposium.
An Autonomous Performance Control Framework for Distributed Multi-Agent Systems: A Queueing Theory Based Approach Nathan Gnanasambandam gsnathan@psu.edu Seokcheon Lee stonesky@psu.edu Pennsylvania State University 310 Leonhard Building University Park, PA 16802 Soundar R.T. Kumara skumara@psu.edu ABSTRACT Distributed Multi-Agent Systems (DMAS) such as supply chains functioning in highly dynamic environments need to achieve maximum overall utility during operation. The utility from maintaining performance is an important component of their survivability. This utility is often met by identifying trade-offs between quality of service and performance. To adaptively choose the operational settings for better utility, we propose an autonomous and scalable queueing theory based methodology to control the performance of a hierarchical network of distributed agents. Categories and Subject Descriptors C.4 [Performance of Systems]: Design studies, modeling techniques, performance attributes General Terms Performance Keywords Multi-Agent Systems, Survivability, Queueing Models 1. INTRODUCTION With the emerging popularity of distributed multi-agent systems as application platforms, it is necessary that they survive dynamic and stressful environmental conditions, even partial permanent damage. While the survival notion necessitates adaptivity to diverse conditions along the dimensions of performance, security and robustness, delivering the correct proportion of these quantities can be quite a challenge. From a performance standpoint, a survivable system can deliver excellent Quality of Service (QoS) even when stressed. A DMAS could be considered survivable if it can maintain at least x% of system capabilities and y% of system perfor- Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. AAMAS’05, July 25-29, 2005, Utrecht, Netherlands. Copyright 2005 ACM 1-59593-094-9/05/0007 ...$5.00. mance in the face of z% of infrastructure loss and wartime loads (x, y, z are user-defined) [1]. We address a piece of the survivability problem by building an autonomous performance control framework for the DMAS drawing on the idea of composing the bigger society of smaller building blocks (i.e. agent communities) [3]. Identifying data-flowsinthe agent network (similar to [4]) and utilizing the network’s servicelevel attributes such as delays, utilization and response times as a basis for its utility (like in [5]) we build a self-optimizing framework for DMAS. We believe that by using queueing theory we can analyze data-flows within the agent community as a network of queues with greater granularity in terms of processing delays and network latencies and also capitalize on using a building block approach by restricting the model to the community. We contribute by engineering a queueing theory based adaptation (control) framework to enhance the performance of the application layer, which inherently can be visualized as residing over the infrastructure (logical layer or middle-ware) and the physical layer (resources such as CPU, bandwidth). 2. FRAMEWORK ARCHITECTURE Building on the ideas of high-level system specifications (or Tech- Specs) and utilizing queueing network models (QNMs) for performance estimation as in [2] we build a real-time framework for application-level survivability. This framework is represented in Figure 1 and consists of activities, modules, knowledge repositories and information flow through a distributed collection of agents. 2.1 Architecture Overview When the DMAS is stressed by an amount S by the underlying layers (due to under-allocation of resources) and the environment (due to increased workloads during wartime conditions), the DMAS Controller has to examine all its performance-related variables from set X and the current overall performance P in order to adapt. The variables that need to be maintained are specified in the TechSpecs and may include delays, time-stamps, utilization and their statistics. They are collected in a distributed fashion through the measurement points MP which are “soft” storage containers residing inside the agents and contain information on what, where and how frequently they should be measured. The DMAS Controller knows the set of flows F that traverse the network and the set of packet types T from the TechSpecs. With {F, T, X, C}, where C is a suggestion from the DMAS Controller, the Model Builder can select a suitable queueing model template Q. The Control Set
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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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Growth mechanisms Start with a smal
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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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