GridSim

The GridSim toolkit allows modeling and simulation of entities in parallel and distributed computing (PDC) systems-users, applications, resources, and resource brokers (schedulers) for design and evaluation of scheduling algorithms. It provides a comprehensive facility for creating different classes of heterogeneous resources that can be aggregated using resource brokers. for solving compute and data intensive applications. A resource can be a single processor or multi-processor with shared or distributed memory and managed by time or space shared schedulers. The processing nodes within a resource can be heterogeneous in terms of processing capability, configuration, and availability. The resource brokers use scheduling algorithms or policies for mapping jobs to resources to optimize system or user objectives depending on their goals.


References in zbMATH (referenced in 44 articles , 1 standard article )

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  1. Johnsen, Einar Broch; Schlatte, Rudolf; Tapia Tarifa, S.Lizeth: Integrating deployment architectures and resource consumption in timed object-oriented models (2015)
  2. Mollamotalebi, Mahdi; Maghami, Raheleh; Ismail, Abdul Samad: THRD: threshold-based hierarchical resource discovery for Grid environments (2015)
  3. Hasanzadeh, Mohammad; Meybodi, Mohammad Reza: Grid resource discovery based on distributed learning automata (2014)
  4. Kingsy Grace, R.; Manimegalai, R.: Dynamic replica placement and selection strategies in data grids -- a comprehensive survey (2014)
  5. Mansouri, Najme: Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments (2014)
  6. Rodriguez, Juan Manuel; Mateos, Cristian; Zunino, Alejandro: Energy-efficient job stealing for CPU-intensive processing in mobile devices (2014)
  7. Malik, Sana; Nazir, Babar; Qureshi, Kalim; Khan, Imran Ali: A reliable checkpoint storage strategy for grid (2013)
  8. 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)
  9. Kołodziej, Joanna; Xhafa, Fatos: Integration of task abortion and security requirements in GA-based meta-heuristics for independent batch grid scheduling (2012)
  10. Castellà, Damia; Blanco, Hector; Giné, Francesc; Solsona, Francesc: A computing resource discovery mechanism over a P2P tree topology (2011)
  11. Li, Kenli; Tong, Zhao; Liu, Dan; Tesfazghi, Teklay; Liao, Xiangke: A PTS-PGATS based approach for data-intensive scheduling in data grids (2011)
  12. Wu, Qishi; Gu, Yi: Modeling and simulation of distributed computing workflows in heterogeneous network environments (2011)
  13. Altameem, T.; Amoon, M.: An agent-based approach for dynamic adjustment of scheduled jobs in computational grids (2010)
  14. Cao, Haijun; Jin, Hai; Wu, Xiaoxin; Wu, Song; Shi, Xuanhua: DAGMap: efficient and dependable scheduling of DAG workflow job in grid (2010)
  15. de Assunção, Marcos Dias; di Costanzo, Alexandre; Buyya, Rajkumar: A cost-benefit analysis of using cloud computing to extend the capacity of clusters (2010)
  16. Khan, Fiaz Gul; Qureshi, Kalim; Nazir, Babar: Performance evaluation of fault tolerance techniques in grid computing system (2010)
  17. Villela, Daniel: Minimizing the average completion time for concurrent grid applications (2010)
  18. Farha, Ramy; Leon-Garcia, Alberto: Operations research methods for autonomic resource management (2009)
  19. Jiang, Weijin; Zhang, Lianmei; Wang, Pu: Dynamic scheduling model of computing resource based on MAS cooperation mechanism (2009)
  20. Kertész, Attila; Dombi, József Dániel; Dombi, József: Adaptive scheduling solution for grid meta-brokering (2009)

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