DOCTORAL Symposium- MATES 2006 1 Personal Data Title Scalable Multi Agent Based Simulation

DOCTORAL Symposium - MATES 2006Title: Scalable Multi Agent Based SimulationDirk PawlaszczykTechnische Universität Ilmenau1 Personal DataName: Dipl.-Wirtsch.-Inf. Dirk Pawlaszczyk (dirk.pawlaszczyk@tu-ilmenau.de)PhD Thesis Advisor: Professor Dr. Peter GmilkowskyAffiliation: Fachgebiet Wirtschaftsinformatik für Industriebetriebe an der TechnischenUniversität Ilmenau2 IntroductionModelling and simulating entities that perceive the environment, reason upon their perceptions and certain background of knowledge, and act upon that environment is the burden of Multi Agent based Simulation (MABS). Agent based simulation can help to study the dynamic behavior of complex systems. It plays an important role in agent research as well as development of agent applications, and can be adapted to a multitude of problems [Log05], [Dav00].Even if agent based simulation is a powerful tool, some problems remain. To simulate a large number of entities normally far exceeds the capabilities of a single computer. Accordingly, we need a system that scales with growing problem size. The objective of this PhD-Project is to develop and support methods for scalable simulation of agent based models, which consider the specifics of agent-technology.3 The Scalability ProblemIn general, scalability could be described as the property of a system compared to some other property of the same system. It is worth mentioning that scalability is always a relative value dedicated to a certain system. In the context of simulation domain, scalability could be defined as “…how the performance of a certain application behaves as the application problem size increases and the parallel architecture executing it increases.” [Nic98]. Hence, if we wish to decide whether a simulation model will scale or not, we have to consider architectural as well as algorithmic side of the simulation system. Furthermore, scalability is a property particular to distributed simulation [Law98]. Therefore, the simulation model is partitioned among the available processors and simulated with a parallel algorithm that synchronizes the different partitions. To find a partition that balances workload efficiently - even if the simulation model grows - requires a suitable partitioning strategy as well as adaptive load balancing schemes that yield scalable behavior. Hence, effectiveness of a distributed simulation, and its capability to scale, generally depends on three elements: partitioning, load balancing and synchronization.4 Related WorkA literature survey shows that only small research efforts have been made in the field of scalable agent-based simulation so far. This section introduces some relevant sources.MACE3J is one of the first implementations for Java-based MAS simulation [Gas04]. It is reported, that MACE3J has been run with up to 5000 agents on a shared memory system too prove scalability; admittedly, agents have not changed any messages in this test. For the SESAM agent testbed, test with 10.000 agents on a single computer attests acceptable performance [Oec04]. Nevertheless, SESAM doesn’t support distributed simulation. Accordingly, its capability to scale is limited by default.Theodoropoulos and Logan [The99] propose a framework to speed up simulation of multi agent systems by adopting techniques from parallel simulation community such as optimistic synchronization. They argue, for discrete event-driven simulation approach, since it is most suitable to simulate agent based models. Most of their work focuses on questions about spatial representation and management of shared space. However, scalability is not main objective of their work.Another simulation toolkit that is designed for large scale testing of distributed multi-agent systems is the JAMES agent testbed [Uhr00]. It uses centralized server architecture for tracking and tracing of simulated agents using a centralized environment model. Again, there are no concrete statements concerning scalability of this system. Popov et al [Pop03] describe a parallel sequential simulation approach to simulation of 106 agents to capture the behaviour of web users. This vast number of simulated agents is reached by keeping the agent implementation static. Furthermore they are using a relative weak notion of agency, and do not consider deliberative agent structures. Another approach is discussed as part of the SPADES framework, a middleware for building (distributed) agent based simulation environments [Ril03]. They reported good speed ups for small agent sets with heavy workload. Furthermore, there are some contributions in place concerning the distributed simulation through federation of agent-based simulation environments using the High Level Architecture (HLA), a generic, language-independent specification, which allows the integration of different sequential simulators, and was originally initiated by the Department of Defence and meanwhile committed as an ISO standard [Wan05]. With HLA, simulators, referred to as federates, can be integrated into a global simulation context known as federation. A strict hierarchical tree-oriented model is used to structure respective federations. Communication between certain simulators is enabled by predefined gateways. Thus, HLA can be considered as a centralized coordination approach to distributed simulation resources. HLA_AGENT for example introduces support for distributed simulation to the SIMAGENT toolkit [Lee05]. HLA clearly focuses on the interoperability between different simulation toolkits. To summarize, although MABS has received a lot of attention in recent years there are only a few contributions that deal with the problem of simulating large scale agent-based systems. Moreover, only a small set of agent-based simulation toolkits does support distributed simulation over multiple hosts - a prerequisite for scalability.5 Research ApproachThe primary research issue reads as follows: How to design a simulation service in order to support scaleable simulation of large agent-based systems? Accordingly, the PhD project focuses on the conception and design of a generic simulation service that offers support to distributed simulation of large scale agent based models. As pointed out in section 3 we have to regard threeelements: partitioning, load balancing and synchronization. Since synchronization is the linchpin to scaleable simulation, the project concentrates on selecting an adequate synchronization scheme. Therefore, the adoption of ideas from the traditional simulation community, especially parallel and discrete event simulation (PDES) techniques is planned. Searching for a general purpose synchronization technique, optimistic synchronization seems to be most promising approach, because it (at least) theoretically provides enough performance for a wide range of models [Fuj00]. Hence, to enable scalable agent based simulation we claim an optimistic synchronization scheme. Appling conventional PDES models and techniques to MABS is more complicated than is looks, since we have to regard specifics of agent technology like particular high communication demand, dynamic topologies, and finally the possibility to simulate an environmental model, without fully loss of performance. As prove of concept, a prototype implementation is intended. The validation of the prototype and its properties is done by using exemplary application scenarios on different scales.6 Current Findings, State of the workA survey of existing simulation techniques has been performed to identify useful approaches and techniques, which could be employed. A comprehensive literature research has been done. Meanwhile, this survey is completed; the most appropriate methods were identified for inclusion in the prototype. At present, work on the project is concentrating on the primary implementation of the synchronization service. To assist with implementation, a series of test applications will be used to assess the tool practicality. The results of these studies will allow us to incrementally improve and enhance the tool during the development process.A first result of these efforts, a time management service for the JADE agent toolkit called SIMJADE has already been published [Paw06b]. By joining optimistic synchronization techniques together with domain-specific knowledge based on agent communication protocols a new synchronization scheme has been proposed [Paw06a]. First experimental results are really encouraging. However, the current work needs to be continued referring the following aspects: On going evaluation of the proposed algorithm;Implementation of different agent models (transportation networks, electronic markets);Additional performance improvements, as well as the introduction of advanced load balancing schemes.7 References[Dav00] P. Davidsson: Multi Agent Based Simulation: Beyond Social Simulation. In: Moss, S., Davidsson, P. (eds.): 3.Workshop on Multi Agent Based Simulation (MABS) 2000, LNAI 1997, pp 97-107, 2000.[Fuj00] R. M. Fujimoto: Parallel and Distributed Simulation Systems. John Wiley & Sons, Inc. 2000.[Gas04] L. Gasser, K. Kakugawa et al: Smooth Scaling Ahead: Progressive MAS Simulation from Single PCs to Grids. In: Proceedings of the Joint Workshop on Multi-Agent & Multi-Agent-Based Simulation, Autonomous Agents & Multiagent Systems (AAMAS) New York, USA, pp 1-10, 2004.[Law98] D.R. Law: Scalable means more than more: a unifying definition of simulation scalability. In: Proceedings of the 30th conference on winter simulation table of contents Washington, D.C., United States, pp 781 – 788, 1998.[Lee04] M. Lees, B. Logan, G. Theodoropoulos. Adaptive optimistic synchronisation for multiagent distributed simulation. In: Proceedings of the 5th EUROSIM Congress on Modelling and Simulation (EuroSim’04) 2004.[Log05] B. Logan: The Simulation of Agent Systems. In: AgentLink News (17), April, pp. 4-5, 2005.[Nic98] D. M. Nicol: Scalability, Locality, Partitioning and Synchronization in PDES,1998.[Oec04] C. Oechslein: Vorgehensmodell mit integrierter Spezifikations- und Implementierungssprache für Multiagentensys-teme. PhD-Thesis, Würzburg, Shaker-Verlag 2004.[Paw06a] D. Pawlaszczyk, I. J. Timm: A Hybrid Time Management Approach to Agent-Based Simulation.29th Annual German Conference On Artificial Intelligence KI2006, 2006.[Paw06b] D. Pawlaszczyk: Scalable Multi Agent Based Simulation - Considering Efficient Simulation of Transport Logistic Networks. 12th ASIM Conference - Simulation in Production and Logistics (ASIM 2006), Kassel, 2006.[Pop03] K. Popov, et al: Parallel Agent-Based Simulation on a Cluster of Workstation. In: Kosch, H. et al (eds.): Euro-Par 2003, pp 470-480, 2003 Springer.[Ril03] P. F. Riley: SPADES – A Distributed Agent Simulation Environment with Software-In-The-Loop Execution.In: S.Chick et al: Proceedings of the 2003 Winter Simulation Conference, pp 817-825, 2003.[The99] G. Theodoropoulos, B. Logan: A framework for the distributed simulation of agent-based systems. In: Proceedings of the 13 th SCS European Simulation Multiconference, pp 58-65, 1999.[Wan05] F. Wang, S.J. Turner, L. Wang: Agent Communication in DistributedSimulations. In: Davidsson, P. et al (eds.): Workshop on Multiagent Based Simulation (MABS) 2004, LNAI Vol.3415, pp 11-24, 2005, Springer.。