DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

We set up eight different experimental conditions by combining three independent factors as
shown in Table 2. We use two different network topologies as in Fig. 3, which are non-optimal
and optimal topologies. Two resource allocation policies are used: round-robin allocation and
constant allocation. In round-robin allocation the components in each machine are assigned equal
weights and in constant allocation according to the components’ load indices as in (11). To
implement PS scheduling we use a weighted round-robin scheduling in which CPU time
received by each component in a round is equal to its assigned weight. Also, the distribution of
P i (k) can be deterministic or stochastic. While using stochastic distribution we repeat 5
experiments.
Table 2. Experimental design
Condition Topology Resource allocation P i (k)
Con1 Non-optimal Round-Robin Deterministic
Con2 Non-optimal Round-Robin Exponential
Con3 Non-optimal
Constant Deterministic
Con4 N on-optimal Constant Exponential
Con5 Optimal Round-Robin Deterministic
Con6 Optimal Round-Robin Exponential
Con7 Optimal Constant Deterministic
Con8 Optimal Constant Exponential
Numerical results fr om the experimentation are shown in Table 3. The last two conditions
(Con7 and Con8), which use the optimal network topology and constant resource allocation,
gives a performance close to T * and outperforms other conditions significantly. Also, constant
allocation for both non-optimal (Con3 and Con4) and optimal (Con7 and Con8) topologies, gives
a performance superior to round-robin allocation and close to lower bound performance T LB in
both deterministic and stochastic environments. These facts support the optimality of the
constant resource allocation and consequently the validity of the method of quantifying the
minimal completion time.
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