Download Computer Performance Engineering: 13th European Workshop, by Dieter Fiems, Marco Paolieri, Agapios N. Platis PDF

By Dieter Fiems, Marco Paolieri, Agapios N. Platis

This publication constitutes the refereed complaints of the thirteenth ecu Workshop on computing device functionality Engineering, EPEW 2016, held in Chios, Greece, in October 2016. The 14 papers awarded including 2 invited talks during this quantity have been conscientiously reviewed and chosen from 25 submissions. The papers provided on the workshop replicate the variety of contemporary functionality engineering, with subject matters starting from the research of queueing networks and stochastic approaches, to functionality research of desktops and networks, or even modeling of human behavior.

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Additional info for Computer Performance Engineering: 13th European Workshop, EPEW 2016, Chios, Greece, October 5-7, 2016, Proceedings

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N−1)/2 . We found the minimum implementing an evolution strategy. Starting with one feasible solution, the parameter values were changed by adding a normally distributed random value for each parameter (mutating the solution). The deviation was changed according to Rechenberg’s 1/5 rule; if the success ratio (that is, the cv has decreased for the new parameter values) after 20 steps is over 1/5, then the deviation is sligthly increased, in order to ensure the exploration of the search space.

In: Symposium Proceedings on Communications Architectures & Protocols, SIGCOMM 1989, pp. 1–12. ACM, New York (1989) 6. : Borrowed-virtual-time (BVT) scheduling: supporting latency-sensitive threads in a general-purpose scheduler. SIGOPS Oper. Syst. Rev. 33(5), 261–276 (1999) 7. : A case for grid computing on virtual machines. In: Proceedings of the 23rd International Conference on Distributed Computing Systems, pp. 550–559, May 2003 8. : Enhanced modeling and solution of layered queueing networks.

In the simulation, the time unit of temporal parameters is 180 s. Finally, different settings of the predictor component, obtained with the different fitting strategies Exp, Erlang, and Whitt described in Sect. 3 are experimented, so as to evaluate how different fitting distributions impact on prediction and scheduling performance. Note that, in so doing, the trained model used for prediction is different than that used in the generation of datasets: the Whitt strategy will fit expected value and coefficient of variation, while Exp and Erlang strategies will fit only the expected value; incidentally, for the 2-phase Erlang dataset, the Erlang strategy fits both the coefficient of variation and the overall shape, and so this benevolent case was not reported in the experimentation.

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