Meta-Learning Resource Allocation for Adaptive Cluster Workloads

The system employs self-service through automated meta-learning models and actuator engines that dynamically adjust resource allocation without manual intervention. The actuator engine automatically receives task information, determines optimal resource configurations, and implements allocation changes, enabling the system to serve itself in optimizing resource distribution while improving productivity.

The patent implements dynamics by transitioning from static resource allocation to dynamic adaptation. The system continuously learns from historical task data and adjusts resource allocation in real-time based on current task requirements. The actuator engine dynamically reconfigures computing, storage, and networking resources according to evolving job patterns, resolving the contradiction between efficiency improvement and system complexity.

The patent replaces manual algorithm selection with automated machine learning models. The meta-learning diagnostic models automatically analyze task characteristics and predict optimal resource allocation strategies without human intervention. This substitution of mechanical/manual processes with automated intelligent systems improves predictive accuracy while the modular architecture manages the inherent complexity through systematic automation.

The actuator engine serves as an intermediary between task information and resource allocation decisions. It receives preprocessed task data, consults trained meta-learning models, and translates predictions into concrete resource configuration actions. This intermediary layer automates the complex algorithm selection process, improving predictive accuracy while encapsulating complexity within a manageable intermediate component.

The system applies preliminary action by pre-processing historical task information before model training. The preprocessor standardizes and prepares historical data in advance, extracting relevant features and organizing them for efficient model consumption. This preliminary preparation enables the meta-learning models to quickly adapt to diverse job patterns while managing data processing complexity through structured pre-computation.

The patent segments the data processing workflow into distinct modular components: data collection, preprocessing, model training, and actuation. Each component handles specific aspects of historical data utilization independently. This segmentation allows the system to leverage historical data for improved adaptability while containing processing complexity within discrete, manageable modules that can be developed and maintained separately.