NeuPAT facilitates clinical and translational research, minimizes the workload in consolidating the information, reduces errors and increases correlation of data through extensive coding.
The system is capable of performing most of the tasks in an autonomous and intelligent manner, which minimizes the workload of medical staffs, and consequently minimizes the associated psychological stress and improves the quality of patient care as well as the system performance.
The second objective is to minimize the workload imbalances among cells.
Because the objective function of the partitioning procedure is to minimize the workload, the positive values represent improvements obtained.
Objective function of moves The objective of the partitioning problem is to minimize the workload of the system.
The objectives of the proposed model was to minimize the different costs of the DCMS, increase the manpower utilization, and also minimize the workload variance between cells.
We keep increasing the jobs' slots trying to minimize the workload's makespan until we have not exceeded the available user's budget (i.e., BudgetMax metric).
We introduce the design and implementation of a novel Pareto-based scheduler that, given a user's MapReduce workload and budget range, detects map/reduce slot allocations that minimize the workload's makespan without overcharging the user.
The problem we solve in this work is how to appropriately allocate map/reduce slots to a user's jobs in order to minimize the workload's makespan and at the same time minimize the user's spending budget.
In this case, the possible slots' allocations can grow exponentially and thus determining the allocation that minimizes the workload's end-to-end execution time (i.e., makespan) and at the same does not overcharge the user, is not trivial.
More specifically, in [23], they minimize the workload's makespan while in [24], they focus on maximizing a profit metric which depends on the amount of jobs that satisfy their execution time deadlines.
