Validating and scaling the MicroGrid: A scientific instrument for grid dynamics. Large-scale Grids that aggregate and share resources over wide-area networks present major challenges in understanding dynamic application and resource behavior for performance, stability, and reliability. Accurate study of the dynamic behavior of applications, middleware, resources, and networks depends on coordinated and accurate modeling of all four of these elements simultaneously. We have designed and implemented a tool called the MicroGrid which enables accurate and comprehensive study of the dynamic interaction of applications, middleware, resource, and networks. The MicroGrid creates a virtual Grid environment – accurately modeling networks, resources, the information services (resource and network metadata) transparently. Thus, the MicroGrid enables users, Grid researchers, or Grid operators to study arbitrary collections of resources and networks. The MicroGrid includes the MaSSF online network simulator which provides packet-level accurate, but scalable network modeling. We present experimental results with applications which validate the implementation of the MicroGrid, showing that it not only runs real Grid applications and middleware, but that it accurately models both their and underlying resource and network behavior. We also study a range of techniques for scaling a critical part of the online network simulator to the simulation of large networks. These techniques employ a sophisticated graph partitioner, and a range of edge and node weighting schemes exploiting a range of static network and dynamic application information. The best of these, profile-driven placement, scales well to online simulation of large networks of 6,000 nodes using 24 simulation engine nodes.

References in zbMATH (referenced in 11 articles )

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  1. Mansouri, Najme: Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments (2014) ioport
  2. Al-Khateeb, Asef; Rashid, Nur’Aini Abdul; Abdullah, Rosni: An enhanced meta-scheduling system for grid computing that considers the job type and priority (2012) ioport
  3. Hooshmand, A.; Malki, H. A.; Mohammadpour, J.: Power flow management of microgrid networks using model predictive control (2012) ioport
  4. Li, Chunlin; Li, Layuan: A global optimization approach for three layers of computational grid stack (2009)
  5. Li, Zhi-Jie; Cheng, Chun-Tian; Huang, Fei-Xue: Utility-driven solution for optimal resource allocation in computational grid (2009)
  6. Quétier, Benjamin; Neri, Vincent; Cappello, Franck: Scalability comparison of four host virtualization tools (2007) ioport
  7. Berman, F.; Casanova, H.; Chien, A; Cooper, K.; Dail, H.; Dasgupta, A.; Deng, W.; Dongarra, J.; Johnsson, L.; Kennedy, K.; Koelbel, C.; Liu, B.; Liu, X.; Mandal, A.; Marin, G.; Mazina, M.; Mellor-Crummey, J.; Mendes, C.; Olugbile, A.; Patel, M.; Reed, D.; Shi, Z.; Sievert, O.; Xia, H.; YarKhan, A.: New grid scheduling and rescheduling methods in the grADS project (2005) ioport
  8. Liu, Xin; Xia, Huaxia; Chien, Andrew A.: Validating and scaling the MicroGrid: A scientific instrument for grid dynamics (2005)
  9. Bell, William H.; Cameron, David G.; Capozza, Luigi; Millar, A. Paul; Stockinger, Kurt; Zini, Floriano: Simulation of dynamic grid replication strategies in OptorSim (2002)
  10. Buyya, Rajkumar; Murshed, Manzur: GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing (2002)
  11. Iamnitchi, Adriana; Foster, Ian: On fully decentralized resource discovery in grid environments (2001)