### Table 3, which gives a guideline to choose a load sharing policy based on the types of jobs in distributed systems. In the table, the relationship between a load sharing policy and each type of jobs is represented by symbol + (beneficial), ++ (highly beneficial) and - (non-beneficial).

2000

"... In PAGE 9: ... Table3 : Summary of the 8 load sharing policies and their impact on the job types (CPU-bound and memory-bound). There are two limits in this study.... ..."

Cited by 19

### Table 9: Combined approach versus Gang Scheduled versus Load Shared

"... In PAGE 8: ... Combining approaches, correctly mapping and dy- namically managing partitions and resources, provides better performance results that any single approach. Table9 shows the superiority of such an approach on a particular workload. WN is a workload that consists of an equal percentage of the three workloads (W1, W2, W3).... ..."

### Table 2: Summary of Gang Scheduling Parameters. speci es the maximum number of nodes allocated to an application in the load sharing partition, which can constrain the possible range of values for L and is at most the number of nodes in the partition PLS. This makes it possible for the system administrator to control and adjust this important aspect of our load sharing algorithm. These policy parameters are summarized in Table 3. Parameter Control Policy De nition

1997

Cited by 11

### Table 3: Notations, variables, and parameters of the page-free-free load sharing scheme.

2001

Cited by 7

### Table 3: Notations, variables, and parameters of the page-free-free load sharing scheme.

### Table 2. Statistical Parameters of Significant Models model

2005

"... In PAGE 7: ...e., significant models , were included in the lists ( Table2 ). We also tested the top ranked model for each data set against all other significant models for the same set.... In PAGE 7: ... Moreover, one can easily notice that models for logK1 have in general lower MAE compared to log 2, because of the lower quality of the experimental data in the later models, as it is explained in the Discussion. The analysis of models in Table2 indicated complexities with comparison methods using just one data set. A combination of model + descriptors, which provided top and very significant results for one data set, was at the bottom of the list for another data set.... In PAGE 8: ... Cumulative Plots of Descriptors Contributions to Top- Ranked Models. To provide some general estimations of all results, we selected n top-ranked models per data set characterized by the smallest MAE (see Table2 ) and the analyzed contribution of different descriptors and methods types to these models. Figure 4 show which types of descriptors were more frequently used in the top-ranked models.... In PAGE 8: ... Percentage of models (y axis) calculated using the corresponding descriptor system as a function of the number of best models (x axis) per each data type. For each data set we selected the n-best models ( Table2 ) and the calculated percents of models generated using each descriptor system. ISIDA-5 and ISIDA- average models generated using SMF fragments were not included in the analysis.... In PAGE 8: ... Percentage of models (y axis) as a function of the number of n top-ranked significant models (x axis) selected per each data type. For each data set we selected the n-best models ( Table2 ) and counted the percents of models contributed using each method. A, B, and C correspond to models calculated using a single descriptor type.... ..."

### Table 1 Parameters of fuzzy inference systems

"... In PAGE 1: ... Se- lection of important variables and adequate rules is critical for obtaining a good model. The parameters of fuzzy inference systems can be classi ed into four categories ( Table1 ): logic, struc- tural, connection, and operational. Generally speak- ing, this order also represents their relative in uence on system behavior (with logic being the most in u- ential and operational the least).... In PAGE 1: ... Our encoding of solutions (the genome) takes advantage of previous knowledge about the problem, thus reducing the search space while fa- voring the extraction of the most signi cant variables in order to provide more human-comprehensible rules. Referring to Table1 , the evolved parts of the fuzzy system in this work are: the relevant variables, the antecedents and consequents of rules, and the values of input membership functions. Thus, we evolve struc- tural, connection, and operational parameters at the same time.... In PAGE 2: ... Fuzzy system parameters Previous knowledge about the WBCD problem repre- sents valuable information to be used for our choice of fuzzy parameters. Following Table1 , we delineate below the fuzzy system set-up: Low d P High 1 Value 0 Degree of membership Figure 1 Orthogonal membership functions and their pa- rameters, plotted above as degree of membership versus input values. The orthogonality condition means that the sum of all membership functions at any point is one.... ..."

### Table 1: Parameters of the queuing model.

2000

"... In PAGE 3: ...formulas for open models [12] to solve this model with P = 2 in order to predict the response time. Table1 gives the parameters that characterize the workload we have used to solve the model for the two load sharing policies. Besides arrival rates, we have two other parameters, Dcpu(i) and Ddisk(i), which are average service demands of the CPU and the disk, respectively, at comput- ing node i for i = 1; :::; P (measured in seconds).... In PAGE 3: ... Besides arrival rates, we have two other parameters, Dcpu(i) and Ddisk(i), which are average service demands of the CPU and the disk, respectively, at comput- ing node i for i = 1; :::; P (measured in seconds). The parameter values in Table1 are collected from our trace-driven simulations which will be presented in section 4. The average disk service demands of node 1 (Ddisk(1) = 0:10 seconds) is 10 times larger than that of node 2 (Ddisk(1) = 0:01 seconds), which means that node 1 has a smaller memory capacity to cause more page faults.... ..."

Cited by 19

### Table 1. Input System Parameters in Model

"... In PAGE 2: ... The model is driven by an input set of system pa- rameter descriptions that define the scenario, includ- ing the scene classes, atmospheric state, sensor charac- teristics, and processing algorithms. Table1 contains a list of model parameters and options available, as well as their symbols used in this article. These parameters are used in analytical functions to transform the spectral reflectance first- and second- order statistics of each surface class through the spec- tral imaging process.... ..."

### Table 6: Changing association over time model

"... In PAGE 8: ...6.6% to 45.9%. Using OECD data for 1992 (US National Center for Educational Statistics, 1996, Table6 ), we see that Irish participation rates for 17 to 21 year-olds exceed UK rates substantially (see Table 1, panel A). Panel B of the same table looks at change in participation over time, and though it is very dif cult to get long series of comparable data, it suggests that participation, both overall and in the tertiary sector, has grown more strongly in Ireland than in the UK.... In PAGE 25: ... Model 11 allows the quasi-symmetry association to vary freely over time. We see from Table6 that the constant association model actually gives a satisfac- tory t to the Irish data at the conventional 5% level. We also see that the uniform... ..."