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86 M. Bonn and H. Schmeck tells the user the price for these virtual machines. If he agrees, the virtual machines are cloned from templates; configured according the user’s needs, and started to execute the agents. So JoSchKa would not only react on the dynamic behavior of fixed machines, but also would extend/reduce them dynamically (and thus acting as a real intervening organic controller), if needed. References [1] Anderson, D.: BOINC: A System for Public Resource Computing and Storage. In: 5th IEEE/ACM International Workshop on Grid Computing, Pittsburg, PA, pp. 365–372 (2004) [2] Wedeniwski, S.: ZetaGrid – Computations connected with the Verification of the Riemann Hypothesis. In: Foundations of Computational Mathematics Conference, Minnesota, USA, August 2002 (2008) [3] Litzkow, M., Livny, M., Mutka, M.: Condor – a Hunter of idle Workstations. In: 8th International Conference on Distributed Computing Systems, pp. 104–111 (1988) [4] Luther, A., Buyya, R., Ranjan, R., Venugopal, S.: Peer-to-Peer Grid Computing and a.NET-based Alchemi Framework. In: Guo, L.Y. (ed.) High Performance Computing: Paradigm and Infrastructure. Wiley Press, New Jersey (2005) [5] Bonn, M.: JoSchKa: Jobverteilung in heterogenen und unzuverlässigen Umgebungen. Dissertation an der Universität Karlsruhe (TH), Universitätsverlag Karlsruhe (2008) [6] Gelernter, D.: Generative Communication in Linda. ACM Transactions on Programming Languages and Systems 7(1), 80–112 (1985) [7] Dostal, W., Jeckle, M., Melzer, I., Zengler, B.: Service-orientierte Architekturen mit Web Services. Spektrum Akademischer Verlag (2005) [8] Tsafrir, D., Etison, Y., Feitelson, D.: Backfilling using System-generated Predictions rather than User Runtime Estimates. IEEE Transactions on Parallel and Distributed Systems 18(6), 789–803 (2007) [9] He, Y., Hsu, W., Leiserson, C.E.: Provably efficient two-level adaptive Scheduling. In: Frachtenberg, E., Schwiegelshohn, U. (Hrsg.) JSSPP 2006. LNCS, vol. 4376, pp. 1–32. Springer, Heidelberg (2007) [10] DFG Priority Program 1183 Organic Computing (2005), http://www.organic-computing.de/SPP (visited September 2009) [11] Richter, U., Mnif, M., Branke, J., Müller-Schloer, C., Schmeck, H.: Towards a generic observer/controller architecture for Organic Computing. In: Christian Hochberger and Rüdiger Liskowsky, INFORMATIK 2006 – Informatik für Menschen! GI-Edition – Lecture Notes in Informatics (LNI), vol. P-93, pp. 112–119. Bonner Köllen Verlag (2006) [12] Richter, U., Mnif, M.: Learning to control the emergent Behaviour of a multi-agent System. In: Proceedings of the 2008 Workshop on Adaptive Learning Agents and Multi- Agent Systems at AAMAS 2008 (2008) [13] Baun, C., Kunze, M., Nimis, J., Tai, S.: Cloud Computing: Web-basierte dynamische IT-Services. Auflage, vol. 1. Springer, Berlin (2009)
On Deadlocks and Fairness in Self-organizing Resource-Flow Systems ⋆ Jan-Philipp Steghöfer 1 , Pratik Mandrekar 2 ,FlorianNafz 1 , Hella Seebach 1 , and Wolfgang Reif 1 1 Universität Augsburg, Institute for Software- & Systems-Engineering, Augsburg, Germany {steghoefer,nafz,seebach,reif}@informatik.uni-augsburg.de 2 Birla Institute of Technology and Science Pilani, Goa, India pratik.mandrekar@gmail.com Abstract. Systems in which individual units concurrently process indivisible resources are inherently prone to starvation and deadlocks. This paper describes a fair scheduling mechanism for self-organizing resourceflow systems that prevents starvation as well as a distributed deadlock avoidance algorithm. The algorithm leverages implicit local knowledge about the system’s structure and uses a simple coordination mechanism to detect loops in the resource-flow. The knowledge about the loops that have been detected is then incorporated into the scheduling mechanism. Limitations of the approach are presented along with extension to the basic mechanism to deal with them. 1 Introduction In a resource-flow system agents handle resources by receiving them from another agent, processing them and handing them over to another agent. An instance of this are flexible manufacturing systems. If agents can only process one resource at a time and there are no buffers available, such systems are prone to deadlocks. If the agents are arranged in a way that an agent can receive a resource it had already processed before, the resource has been processed by agents arranged in a loop. This loop can fill up with resources, in a way that the agent can not give its currently held resource to another agent and can thus not accept a new resource. Such a situation is depicted in Fig. 1. This paper introduces a deadlock avoidance mechanism that leverages the implicit knowledge available to the agents in self-organizing resource-flow systems modelled with the Organic Design Pattern (ODP) [16] and incorporates the mechanism into a fair scheduler for this system class. The local knowledge of the agents along with the structure that is given by the definition of the resource flow allows the algorithm to be efficient both in terms of messages sent and in computational time required while effectively avoiding deadlocks and preventing ⋆ This research is partly sponsored by the priority program “Organic Computing” (SPP 1183) of the German research foundation (DFG) in the project SAVE ORCA. C. Müller-Schloer, W. Karl, and S. Yehia (Eds.): ARCS 2010, LNCS 5974, pp. 87–100, 2010. c○ Springer-Verlag Berlin Heidelberg 2010
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Table of Contents XIII JetBench:AnO
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Abdullah, Tariq 174 Alima, Luc Onan
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