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Intensional High Performance Computing.

, , , and . DCW, volume 1830 of Lecture Notes in Computer Science, page 161-170. Springer, (2000)

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ParoC++: Extending C++ to the Grid., and . GCA, page 177-183. CSREA Press, (2005)Multi Objective Resource Scheduling in LTE Networks Using Reinforcement Learning., , , , and . Int. J. Distributed Syst. Technol., 3 (2): 39-57 (2012)CASP: a community-aware scheduling protocol., , , and . Int. J. Grid Util. Comput., 2 (1): 11-24 (2011)Introduction., , , and . Ultrascale Computing Systems, IET, (2019)Benefits of data aggregation on energy consumption in wireless sensor networks., , , and . IET Commun., 11 (8): 1216-1223 (2017)Meta-Alignment: Combining Sequence Aligners for Better Results., , and . IWBBIO (1), volume 10813 of Lecture Notes in Computer Science, page 395-404. Springer, (2018)MaGate Simulator: A Simulation Environment for a Decentralized Grid Scheduler., , , , and . APPT, volume 5737 of Lecture Notes in Computer Science, page 273-287. Springer, (2009)Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management., , , , , , and . IEEE Trans. Netw. Serv. Manag., 15 (4): 1661-1675 (2018)Adaptive data aggregation with probabilistic routing in wireless sensor networks., , , and . Wirel. Networks, 22 (8): 2485-2499 (2016)Integrating Wireless Sensor Networks and the Grid through POP-C++., , , and . IESS, volume 231 of IFIP Advances in Information and Communication Technology, page 411-420. Springer, (2007)