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TABLE I UNIX#2FPOSIX.4 RT#2FTS priority class with the rate monotonic emulation

in A soft real time scheduling server in UNIX operating system
by Hao-hua Chu, Klara Nahrstedt 1997
Cited by 20

TABLE I UNIX/POSIX.4 RT/TS priority class with the rate monotonic emulation .

in A Soft Real Time Scheduling Server in UNIX Operating System
by Hao-hua Chu, Klara Nahrstedt

TABLE I UNIX/POSIX.4 RT/TS priority class with the rate monotonic emulation .

in A Soft Real Time Scheduling Server in UNIX Operating System
by Hao-Hua Chu, Klara Nahrstedt

Table 4 Typing of dynamic processes. (note: we identify n[%] with n)

in Type based discretionary access control
by Michele Bugliesi, Dario Colazzo, Silvia Crafa, Università Ca Foscari 2004
"... In PAGE 9: ... 3.2 Typing of processes and dynamic processes The typing rules for (dynamic) processes, in Table4 , complete the presentation of the type system. We remark that the typing rules validate dynamic processes, hence also the (proper) processes of the source calculus.... ..."
Cited by 7

Table 1: Elan4 Capability Allocation for Dynamic Processes

in Abstract High Performance Support of Parallel Virtual File System (PVFS2) over Quadrics ∗
by unknown authors
"... In PAGE 3: ... Second, a large range of contexts is provided on each node. Table1 shows the format of Elan4 capability for all PVFS2 processes. On each node, the first context is dedicated to the server process, and the rest of the contexts are left for the client processes.... In PAGE 3: ... The VPID needed to identify an elan4 process is calculated with this formula: node id (j i+1)+(ctx i). A client process obtains the corre- sponding parameters from the PVFS2 fstab entry as shown on the third row of Table1 . A logfile-based atomic mechanism is intro- duced to serialize the acquisition of contexts by multiple processes on a single node.... ..."

Table 4: Analysis for dynamic process allocation (selected clauses) should therefore estimate the maximal requirements of the instances of these processes as follows:

in Static and Dynamic Processor Allocation for Higher-Order Concurrent Languages
by Hanne Riis Nielson, Flemming Nielson 1995
Cited by 10

Table 4-1 Resource utilisation with dynamic allocateable processes excluded.

in Fixed Priority Scheduling Analysis of the Powertrain Management application example USING THE SCHEDULITE Tool
by Jens Larsson
"... In PAGE 14: ... The utilisation of the different resources we retrieved is presented in the table below. Comparing Table4 -1 with Table 4-2 the increase in utilisation of the GCU and the ABSCU depends only on added server tasks receiving messages. For the MMICU the increase depends both on added server tasks and that the dynamic allocateable tasks are allocated at the MMICU.... In PAGE 14: ... We can see that the system is schedulable since no deadlines are missed. As Node Utilisation MMICU 13,12% ECU 39,3% GCU 2,38% ABSCU 2,80% CAN 82,68% Table4 -2 Resource utilisation with all... ..."

Table 1: Dynamic loading process at node j4

in A strategic model for dynamic traffic assignment
by Younes Hamdouch
"... In PAGE 13: ... Tables 8 and 9 contain the relevant input data for the base scenario A. In scenario B, some arc capacities are modified (see Table1 0) and demand is multiplied by a factor 2 throughout. The modifications relevant to scenario C are displayed in Table 11.... In PAGE 21: ...Demand OD(t) Demand (1,20,0) 25 (20,1,0) 25 (1,20,1) 40 (20,1,1) 40 (1,20,2) 55 (20,1,2) 55 (1,20,3) 30 (20,1,3) 30 (1,20,4) 20 (20,1,4) 20 (1,24,0) 20 (24,1,0) 20 (1,24,1) 35 (24,1,1) 35 (1,24,2) 50 (24,1,2) 50 (1,24,3) 25 (24,1,3) 25 (1,24,4) 15 (24,1,4) 15 (7,20,0) 15 (20,7,0) 15 (7,20,1) 30 (20,7,1) 30 (7,20,2) 45 (20,7,2) 45 (7,20,3) 20 (20,7,3) 20 (7,20,4) 10 (20,7,4) 10 (7,24,0) 15 (24,7,0) 15 (7,24,1) 30 (24,7,1) 30 (7,24,2) 45 (24,7,2) 45 (7,24,3) 20 (24,7,3) 20 (7,24,4) 10 (24,7,4) 10 Table 9: Sioux Falls: OD information (scenario A) Arc Capacity Arc Capacity (8,16) 40 (16,8) 40 (16,17) 40 (17,16) 40 (19,20) 30 (20,19) 30 Table1 0: Sioux Falls: arc capacities for scenario B... In PAGE 22: ...Demand (7,20,0) 60 (7,20,1) 120 (7,20,2) 180 (7,20,3) 80 (7,20,4) 40 Table1 1: Sioux Falls: demand for OD pair (7,20) (scenario C) # iter. jW j Gap (%) Cpu (sec) 0 40 6.... In PAGE 22: ...3279 4157.09 Table1 2: Sioux Falls results (scenario A) # iter. jW j Gap (%) Cpu (sec) 0 40 32.... In PAGE 22: ...5053 7894.81 Table1 3: Sioux Falls results (scenario B)... In PAGE 23: ...6316 8447.17 Table1 4: Sioux Falls results (scenario C)... In PAGE 24: ...416 (24,4) 21.549 Table1 5: Sioux Falls: minimum costs after 50 iterations (scenario A)... In PAGE 25: ...108 (24,4) 26.843 Table1 6: Sioux Falls: minimum costs after 50 iterations (scenario B)... In PAGE 26: ...658 (24,4) 26.880 Table1 7: Sioux Falls: minimum costs after 50 iterations (scenario C)... ..."

Table 1 The operational semantics Dynamic Processes: a;b;::: denote either variables of tagged names of the form n[j].

in Type based discretionary access control
by Michele Bugliesi, Dario Colazzo, Silvia Crafa, Università Ca Foscari 2004
"... In PAGE 5: ... Speci cally, fn(n[j]( x : t):A) = fn(A) [ fng [ fm j m 2 jg, and similarly for the remaining (dynamic) process constructs. The dynamics of the calculus, in Table1 , is de ned as usual in terms of an auxiliary relation of structural congruence. Structural congruence is exactly as in the pG, but re- lies on the different de nition of free names discussed above.... In PAGE 5: ... We remark here that the tags are only instrumental to record the ow of each name, and have no further effect on the reductions available for processes. We make this precise by relating our ow-sensitive semantics of Table1 with the original reduction semantics of pG. The latter, which we denote with 7!, is de ned on processes (rather than on dynamic processes) exactly as we do here, but uses the standard communication rule, namely: nhm1;:::;mki:P j n(x1:t1;:::;xk:tk):Q 7! P j Qfmi=xi g in place of our (red comm) rule.... ..."
Cited by 7

Table 1 Summary and comparison of dynamics models and links to cognitive processing

in Spatially Extended Chaos and the Perception of Form - Computer and biological modeling of shape perception, emergent attention foci, and reversible depth percepts
by David Lee Demaris, David Lee Demaris
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