Abstract
There have been many systems available for parallel and distributed computing (PDC) applications such as grids, clusters, super-computers, clouds, peer-to-peer and volunteer computing systems. High-performance computing (HPC) has been an obvious candidate domain to take advantage of PDC systems. Most of the research on HPC has been conducted with simulations and has been generally focused on a specific type of PDC system. This paper, however, introduces a general purpose simulation model that can be easily enlarged for constructing simulations of many of the most well-known PDC system types. Although it might create a new vision for research activities in the simulation community, current simulation tools do not provide proper support for cooperation between software working in real-time and simulation time. In this paper, thus, we also present a promising approach for constructing hybrid simulations that offers great potential for many research areas. As a proof of concept, we implemented a prototype for our simulation model. Then, we are able to rely on this prototype to build simulations of various PDC systems. Thanks to hybrid simulation support of our model, we are able to combine and manage the simulated PDC systems with our previously developed policy-based management framework in simulation runs.
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References
Flynn, M.J.: Some computer organizations and their effectiveness. IEEE Trans. Comput. C–21(9), 948–960 (1972)
Berkeley Open Infrastructure for Network Computing. http://boinc.berkeley.edu/ (2014)
Weiss, A.: Computing in the clouds. NetWorker 11(4), 16–25 (2007)
Page, E.H.; Smith, R.: Introduction to military training simulation: a guide for discrete event simulationists. In: Winter Simulation Conference (1998)
Sulistio, A.; Yeo, C.S.; Rajkumar, B.: A taxonomy of computer-based simulations and its mapping to parallel and distributed systems simulation tools. Softw. Pract. Exp. 34, 653–673 (2004). doi:10.1002/spe.585
Banks, J.; Carson, J.S.; Nelson, B.L.; Nicol, D.M.: Discrete-Event System Simulation. Prentice Hall, Englewood Cliffs (2001)
Banerjee, S.; Adhikari, M.; Kar, S.; et al.: Development and analysis of a new cloudlet allocation strategy for QoS improvement in cloud. Arab. J. Sci. Eng. 40, 1409 (2015). doi:10.1007/s13369-015-1626-9
Policy Based Management, IETF, Internet Engineering Task Force, Policy Working Group. http://www.ietf.org/html.charters/policy-charter.html (2009)
Dursun, T.; Örencik, B.: POLICE: A Novel Policy Framework. Lecture Notes in Computer Science, LNCS 2869, pp. 819–827 (2003)
Dursun, T.: A Generic Policy Conflict Handling Model. Lecture Notes in Computer Science, LNCS, 3733, pp. 193–204 (2005)
Casanova, H.; Giersch, A.; Suter, F.: Versatile, scalable, and accurate simulation of distributed applications and platforms. J. Parallel Distrib. Comput. 74(10), 2899–2917 (2014)
Krauter, K.; Buyya, R.; Maheswaran, M.: A taxonomy and survey of grid resource management systems for distributed computing. Softw. Pract. Exp. 32, 135–164 (2002)
Sinha, P.K.: Distributed Operating Systems: Concepts and Design. IEEE Press, New York (1997)
Buyya, R.; Murshed, M.: GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurr. Comput. Pract. Exp. 14, 1175–1220 (2002)
JFreeChart graphic library web portal. http://www.jfree.org/jfreechart (2009)
Dursun, T.; Dağ, H.: HeteroSim: heterogeneous simulation framework. In: Communications and Networking Simulation Symposium, CNS (2009)
Java Message Service Concept, Oracle (2016). http://docs.oracle.com/javaee/6/tutorial/doc/bnceh.html (2016)
Depoorter, W.; De Moor, N.; Vanmechelen, K.; Broeckhove, J.: Scalability of grid simulators: an evaluation. In: 14th EuroPar Conference, Vol. 5168 of LNCS, pp 544–553. Springer (2008)
Dursun, T.; Dağ, H.: A generic simulation model for high performance computing systems. In: International Conference on Modeling, Simulation and Visualization Methods (MSV14), Las Vegas (2014)
Little, M.C.: JavaSim User Guide, pub.rel. 0.3 (2004). http://javasim.ncl.ac.uk/ (2009)
Miller, J.A.; Nair, R.S.; Zhang, Z.: JSIM: A JAVA-based simulation and animation environment. In: 30th Annual Simulation Symposium (ANSS97), Atlanta (1997)
Tyan, H.Y.; Hou, C.J.: J-Sim JavaSim: a component based compositional network simulation environment. In: Western Simulation Multiconference (2001)
Healy, K.J.; Kilgore, R.A.: Silk: a Java-based process simulation language. In: Winter Simulation Conference, pp. 475–482 (1997)
Howell, F.; McNab, R.: SimJava: a discrete event simulation package for Java with applications in computer systems modelling. In: First International Conference on Web-Based Modelling and Simulation, San Diego (1998)
Ranganathan, K.; Foster, I.: Decoupling computation and data scheduling in distributed data-intensive applications. In: 11th IEEE International Symposium on High Performance Distributed Computing, HPDC-11, 23–26 July, pp. 352–358 (2002)
Dumitrescu, C.L.; Foster, I.: GangSim: a simulator for grid scheduling studies. In: IEEE International Symposium on Cluster Computing and the Grid, CCGrid, vol. 2, 9–12 May, pp. 1151–1158 (2005)
Dumitrescu, C.L.; Foster, I.: Usage policy-based CPU sharing in virtual organizations. In: Fifth IEEE/ACM International Workshop on Grid Computing, pp. 53–60 (2004)
Legrand, A.; Marchal, L.; Casanova, H.: Scheduling distributed applications: the SimGrid simulation framework. In: 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid2003), Tokyo, 12–15 May (2003)
Syed Abudhagir, U.; Shanmugavel, S.: A novel dynamic reliability optimized resource scheduling algorithm for grid computing system. Arab. J. Sci. Eng. 39, 7087 (2014). doi:10.1007/s13369-014-1305-2
Calheiros, R.N.; Ranjan, R.; Beloglazov, A.; De Rose, C.A.F.; Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)
Bux, M.; Leser, U.: DynamicCloudSim: simulating heterogeneity in computational clouds, DynamicCloudSim. In: SWEET ’13, 2nd ACM SIGMOD Workshop on Scalable Workflow Execution Engines and Technologies (2013)
Ostermann, S.; Prodan, R.; Fahringer, T.: Dynamic cloud provisioning for scientific grid workflows. In: 11th ACM/IEEE International Conference on Grid Computing, pp. 97–104 (2010)
Nunez, A.; Poletti, J.V.; Caminero, A.: Design of a new cloud computing simulation platform. In: 11th International Conference on Computational Science and Applications, pp. 582–593 (2011)
Taufer, M.; Kerstens, A.; Estrada, T.; Flores, D.; Teller, P.J.: SimBA: a DES for performance prediction of volunteer computing projects. In: 21st International Workshop on Principles of Advanced and Distributed Simulation, pp. 189–197 (2007)
Estrada, T.; Taufer, M.; Reed, K.; Anderson, D.P.: EmBOINC: an emulator for performance analysis of BOINC projects. In: Workshop on Large-Scale and Volatile Desktop Grids (PCGrid) (2009)
Kondo, D.: SimBOINC: A Simulator for Desktop Grids and Volunteer Computing Systems. http://simboinc.gforge.inria.fr/ (2007)
Basu, A.; Fleming, S.; Stanier, J.; Naicken, S.; Wakeman, I.; Gurbani, V.K.: The state of peer-to-peer network simulators. ACM Comput. Surv. 45(4), 46:1–46:25 (2013)
Montresor, A.; Jelasity, M.: PeerSim: a scalable P2P simulator. In: 9th International Conference on Peer-to-Peer, pp. 99–100 (2009)
Baumgart, I.; Heep, B.; Krause, S.: OverSim: a flexible overlay network simulation framework. In: 10th IEEE Global Internet Symposium, pp. 79–84 (2007)
Gil, T.M.; Kaashoek, M.F.; Li, J.; Stribling, J.: P2PSim, A Simulator for Peer-to-Peer Protocols. http://pdos.csail.mit.edu/p2psim/ (2005)
Garcia, P.; Pairot, C.; Mondejar, R.; Rallo, R.: PlanetSim: a new overlay network simulation framework. In: 4th International Workshop on Software Engineering and Middleware (SEM), Vol. 3437 of LNCS, pp .123–136. Springer. (2004)
Kilgore, R.A.; Healy, K.J.; Kleindorfer, G.B.: The future of Java-based simulation. In: Winter Simulation Conference, pp. 1707–1712 (1998)
Page, E.H.; Moose, R.L.; Griffin, S.P.: Web-Based simulation in SimJava using remote method invocation. In: Winter Simulation Conference, Atlanta, 7–10 Dec (1997)
Jacobs, P.H.M.; Lang, N.A.; Verbraeck, A.: D-SOL; a distributed Java based discrete event simulation architecture. In: Winter Simulation Conference, pp. 793–800 (2002)
Herrmann, J.W.; Conaghan, B.F.; Lecordier, L.H.: Understanding the impact of equipment and process changes with a heterogeneous semiconductor manufacturing simulation environment. In: Winter Simulation Conference (2000)
Kim, Y.J.; Kim, T.G.: A heterogeneous simulation framework based on the DEVS BUS and the high level architecture. In: Winter Simulation Conference, 13–16 Dec (1998)
Barr, R.; Haas, Z.J.; Van Renesse, R.: JiST: An efficient approach to simulation using virtual machines. Softw. Pract. Exp. 35(6), 539–576 (2005)
Kaner, C.: Cem Kaner on scenario testing: the power of “what if” and nine ways to fuel your imagination. In: Software Testing & Quality Engineering (STQE) (2003)
Java Management Extensions (JMX) Technology, Oracle.http://www.oracle.com/technetwork/articles/java/javamanagement-140525.html (2016)
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Dursun, T., Dağ, H. A Generic Framework for Building Heterogeneous Simulations of Parallel and Distributed Computing Systems. Arab J Sci Eng 42, 3357–3373 (2017). https://doi.org/10.1007/s13369-017-2497-z
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DOI: https://doi.org/10.1007/s13369-017-2497-z