Cloud Resource Source Selection With Best-Arm Configuration Search
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Solution Overview
Problem
Existing optimization methods, such as Bayesian optimization algorithms, are ineffective for jointly optimizing cloud provider selection and node configuration due to the high dimensionality of the joint selection-configuration space.
Innovation Solution
A Best-Arm Identification (BAI) algorithm is applied to iteratively eliminate sources and optimize configurations of computerized resources by associating arms with respective sources and connecting them to optimizers, progressively identifying an optimal source with an optimized configuration, using a gradually increasing computational budget.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Bayesian optimization algorithms are used to optimize cloud provider selection and node configuration, then optimization capability is improved, but computational complexity increases due to high dimensionality of the joint selection-configuration space
Solution Approach 1:
The patent segments the joint optimization problem into two separate sub-problems: (1) cloud provider selection using multi-armed bandit algorithms, and (2) node configuration optimization using Bayesian optimization. This segmentation reduces the dimensionality of the search space by treating provider selection and configuration optimization as independent problems that can be solved sequentially or in parallel, thereby reducing computational complexity while maintaining optimization effectiveness.
2Manufacturing precision
If the computational budget for optimizing each source's configuration is increased, then the quality of configuration optimization is improved, but the overall computational workload increases
Solution Approach 1:
The patent applies preliminary action by first using the multi-armed bandit algorithm to identify the most promising cloud provider before investing significant computational resources into configuration optimization. The bandit algorithm quickly eliminates suboptimal providers, allowing the system to concentrate the computational budget on configuring resources for only the top-performing provider(s), rather than evenly distributing resources across all possible providers and configurations.
Data Source
AI summary
The invention is notably directed to a computer-implemented method, which aims at jointly identifying an optimal source of computerized resources and optimizing a configuration of the computerized resources. The method comprises configuring a Best-Arm Identification algorithm, in order to (i) associate arms of the algorithm with respective sources of computerized resources and (ii) connect the arms to one or more optimizers. Each of the optimizers is designed to optimize a configuration of such computerized resources. Next, the method iteratively executes the Best-Arm Identification algorithm to progressively eliminate the sources, with a view to eventually identifying one of the sources as an optimal source with an optimized configuration. Several iterations are accordingly performed. During each iteration, each of the arms is pulled and the rewards earned by pulling the arms are computed. Pulling each arm causes to optimize a configuration of computerized resources of a respectively associated source.