Fig. 7 Resource allocation decisions for experiment III under dynamic-FAA Fig. 8 Resource allocation decisions for experiment III under PAA.
Fig. 5 Resource allocation decisions for experiment II under dynamic-FAA Fig. 6 Resource allocation decisions for experiment II under PAA.
We have developed a novel experimental design framework using bounded-error methods and biologically relevant design metrics to select desirable time point locations where additional measurements will be collected for the purpose of improving resource allocation for biological experiments.
Ray Tune, for instance takes full advantage of this by managing its own scarce resource allocation to hyperparameter search experiments (e.g., GPU allocation).
In this section, we analyze and compare the performance of the two proposed resource allocation methods in numerical experiments.
We demonstrate in detail, the effects of QoS parameters and channel conditions on the resource allocation decisions via numerical experiments.
An even stronger approach would be to consider, when collecting new data, not only how to use existing data to supplement the proposed data collection, but how the new data could be used by others in the future, and how best to design the experiments and resource allocation for the project to facilitate that re-use.
In the greenhouse experiment, differences in resource allocation due to plant neighbor were only identified under conditions of simulated herbivory, and there was a strong interaction between these two factors.
Simulation experiments comparing three resource allocation schemes demonstrate the advantage of our dynamic approach in providing differentiated service qualities, preserving QoS levels in failure scenarios and also improving the overall performance while reducing the resource usage cost.
This paper discusses the adaptation of the COMSOAL approach to the resource allocation problem as well as a designed experiment used to investigate the appropriateness of COMSOAL for a known set of resource allocation test problems.
In each experiment, players made partnership and resource allocation decisions in a simulated disaster environment.
