For robust solutions, workload statistics and mathematical models are key assets, and for viable engineering, these must be carefully designed to ensure acceptable overhead.
It is effective and robust for scheduling parallel workload in the cloud.
It has the clear advantage of detecting the onset and relative level of operator stress (a robust correlate of mental workload), and further, of capturing this information for multiple individuals within a distributed team operating environment.
To ensure robust experimental replication and manageable workloads, we employed a staggered honey bee collection scheme, which was repeated every week for a total of five weeks.
However, existing real-time ORB middleware standards such as RT-CORBA do not adequately address the challenges of (1) managing unpredictable workload, and (2) providing robust performance guarantees portably across different platforms.
The microarray procedure alone was not expected to comprehensively identify the complete S. aureus essential gene list, but was applied as a screen to reduce the workload for the more robust but laborious PCR-based analysis.
Then, we show that our approach is robust for various buffer sizes and workloads.
These changes have created a more robust process that is less susceptible to workload and work circumstances variations, as manifest by substantial improvement in the average time of delivery of the last case of the day.
This paper shows how to exploit partial workload information (system load) to design a scheduler that provides robust performance across heavy-tailed and light-tailed workloads.
The application of two fuzzy logic controllers has been investigated as robust solutions for enforcing desired service performances in face of unpredictable server workloads: a Mamdani type fuzzy logic controller (FLC) and a simulated annealing optimized FLC (SAOFLC).
