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In this paper, we look at several measures of selection quality and derive respective allocation rules for the multi-objective computing budget allocation problem.
In an optimal computing budget allocation problem, different measures of selection quality determine how the best set of designs can be identified and how the simulation budget should be allocated among the designs.
Authors in [42] examine the budget allocation problem in the context of MapReduce workflows, while in [56], they determine the optimal cluster setup for a Hadoop workload.
Treating the budget allocation problem by solving the entire optimal conditions can obtain a budget allocation where the competitive equilibrium has exactly identical individual utility value for each user.
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We focus on one of the most extensively studied crowdsourcing operations, the MAX operation (finding the best element in a collection under human criteria), and we study the problem of budget allocation into rounds for this operation.
This work improves the searching efficiency of optimal computing budget allocation (OCBA) in the stochastic multimodal problem.
In this paper, the problem of computing budget allocation for finding the simplest good enough design is considered.
The example given in section Case 1: initialization problem (the initialization problem) was based on availability budget allocation.
From the above analysis, we infer that there are two main steps in solving the multi-user power allocation problem within the power budget and the PU interference constraint.
The stereotypical project manager is consumed by problems of feature creep and budget allocation, the engineer speaks softly but carries a much bigger stick.
An efficient simulation budget allocation procedure, called EOC-m, is developed for this problem.
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