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. , Jobs are submitted anytime by users and scheduled by the RJMS, then executed

, Illustration of the scheduling process (time steps 1 to 4) using a simple 'first-come, first serve' policy. This policy waits for space to be available for the oldest job in the waiting queue, and starts it. The characteristics of the three job submissions are given in the table above, p.15

. .. , Example Loss function L, plotted with respect to the difference of it's second and third parameters f (x j ) ? p j (the prediction error), p.22

. , The decision to launch job 1 is taken at t 0. The second ready job (2) can not be executed since there are not enough CPUs/Nodes left. Thus, job 3 can also be launched at t 0. The decision to launch job 2 is finally taken when job 1 and 3 complete

. , Scatter plot of heuristic's relative performance between the MetaCentrum and SDSC-BLUE logs

. , Experimental cumulative distribution functions of prediction errors obtained using the Curie log

. , Experimental cumulative distribution functions of predicted values obtained using the Curie log

. .. , AvgWait obtained for the 7 main queue policies with FCFS backfilling for 150 generated weeks on the KTH-SP2 trace. First, in absolute value, and then normalized with respect to EASY-FCFS-FCFS, p.40

. .. , Comparison of the various predictive approaches

, AVEbsld performances of EASY (using requested times) and EASYCLAIRVOYANT (using actual running times). Values between parentheses show the corresponding decrease in AVEbsld, p.17

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. , Weighting factors considered for training the model. The constants are chosen to ensure positivity of the weights with typical running times and resource requests in the HPC domain. Logarithms are used to alleviate the high range produced by ratios

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. , For predictive techniques, only the best and the worst AVEbsld are given. The best non-clairvoyant heuristic triples are outlined in bold

. , 34 4.8 MAE and E-Loss for different prediction techniques. All values are in seconds, AVEbsld performance of the heuristic triples resulting from cross validation. Values in parenthesis show the AVEbsld reduction obtained respective to EASY

. , AvgWait performance of EASY-EXP-EXP and EASY-SQF-SQF on the original CTC-SP2 and SDSC-SP2 traces, in seconds

. , AvgWait and MaxWait performance of EASY-SPF-SPF and EASY-FCFSFCFS on the original CTC-SP2 trace, in seconds

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. , Workload logs used in the simulations, p.61

. .. , Hyper-parameter leave-one-out selection for, p.64

. , The precise definition of the value reported is provided in Eq (6.11), Average total waiting time diminution of fixed policies with respect to EASY(FCFS)

. , Average Cumulative waiting time improvement of the policies used with respect to EASY(FCFS)

, Additionally, the work

?. E. Gaussier, J. Lelong, V. Reis, and D. Trystram, Online Tuning of EASYBackfilling using Queue Reordering Policies, IEEE Transactions on Parallel and Distributed Systems, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01963216

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J. Lelong, V. Reis, and D. Trystram, Tuning EASY-Backfilling Queues, 21st Workshop on Job Scheduling Strategies for Parallel Processing. 31st IEEE International Parallel & Distributed Processing Symposium, 2017.
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?. Ngoko, D. Trystram, V. Reis, and C. Cerin, An Automatic Tuning System for Solving NP-Hard Problems in Clouds, 2016 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPS Workshops, pp.1443-1452, 2016.
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