Results 1 - 10 of 15,561

Table 4 Percentage performance comparison for larger-size problems with the normal distribution of job processing times Normal Exponential

in Job scheduling to minimize the weighted waiting time variance of jobs
by Xueping Li , Nong Ye , Tieming Liu , Yang Sun 2006
"... In PAGE 11: ....2. Performance results of larger-size problems We compare each pair of the four scheduling methods in their performance by computing the percentage of the 1000 problems for a given distribution for which one method in the pair produces better results (smaller WWTV) than another. For example, in Table4 , the last column of the RW scenario for the Pareto data shows 100%, 100%, and 82.3%, which means WVS produce smaller WWTV than FIFO and WSPT for each of the 1000 problems and for 82.... In PAGE 11: ...3% of the problems WVS produces smaller WWTV than WSS. From Table4 we can see that WVS outperforms the other scheduling methods in the PW and RW scenarios regardless of the distribution of the job processing times. In the NW scenario, WSS outperforms other sched- uling methods regardless of the distribution of the job processing times.... ..."

Table 5. Normalized criteria and dominance percentages for job shop problem

in L.: Dynamic scheduling with genetic programming
by Domagoj Jakobović, Leo Budin
Cited by 1

TABLE V NORMALIZED CRITERIA AND DOMINANCE PERCENTAGES FOR JOB SHOP PROBLEM

in Priority Scheduling with Genetic Programming
by unknown authors

Table II. Experimental Results for Synthesized Jobs (Class 1) Normalized Energy

in On Energy-Optimal Voltage Scheduling for Fixed-Priority Hard Real-time Systems
by Han-Saem Yun, Jihong Kim

Table III. Experimental Results for Synthesized Jobs (Class 2) Normalized Energy

in On Energy-Optimal Voltage Scheduling for Fixed-Priority Hard Real-time Systems
by Han-Saem Yun, Jihong Kim

Table IV. Experimental Results for Synthesized Jobs (Class 3) Normalized Energy

in On Energy-Optimal Voltage Scheduling for Fixed-Priority Hard Real-time Systems
by Han-Saem Yun, Jihong Kim

Table I. System power of a 10-cube system Case A : Uniform job size/Uniform residence time (dependent distr.) Case B : Normal job size/Hyperexponential residence time (dependent distr.) Mean job size = 5-cube, Mean residence time = 5 time units Input Case A Case B

in A Lazy Scheduling Scheme for Improving Hypercube Performance
by Prasant Mohapatra, Chansu Yu, Chita R. Das, Jong Kim

Table I. System power of a 10-cube system Case A : Uniform job size/Uniform residence time (dependent distr.) Case B : Normal job size/Hyperexponential residence time (dependent distr.) Mean job size = 5-cube, Mean residence time = 5 time units Input Case A Case B

in A Lazy Scheduling Scheme for Hypercube Computers
by Chansu Yu, Chita R. Das, Prasant Mohapatra, Prasant Mohapatra

Table 7. Log-normal with trimming method simulation results indicating percentage of correct job wait time predictions.

in Predicting Bounds on Queuing Delay in Space-shared Computing Environments
by unknown authors

Table 6. Log-normal without history-trimming method simulation results indicating percent- age of correct job wait time predictions.

in Predicting Bounds on Queuing Delay in Space-shared Computing Environments
by unknown authors
Results 1 - 10 of 15,561