Dynamic resource allocation for scalable distributed big data processing in high-performance computing environments

Abstract

A system for dynamic resource allocation in high-performance computing environments, comprising: a monitoring module for collecting real-time performance metrics from distributed compute nodes; a predictive analytics engine that uses deep learning models to predict resource requirements; a reinforcement learning controller that dynamically allocates resources based on workload variations; a decentralized orchestration layer to ensure fault tolerance and scalability; intelligent load balancing to reallocate workloads based on real-time insights; and an energy optimization module to reduce power consumption.

Description

Technical field of the invention

[0001] The present invention relates to high-performance computing (HPC) and big data analytics. More specifically, it is an AI-driven dynamic resource allocation framework that optimizes computational workloads in distributed big data environments, improving scalability, efficiency, and real-time responsiveness. Background of the invention

[0002] The exponential growth of big data has increased the demand for scalable and efficient computing resources. High-performance computing environments enable the processing of large amounts of data through distributed architectures. However, traditional resource allocation methods are inefficient and lead to problems such as underutilization, network congestion, and high latency. Static allocation mechanisms do not dynamically adapt to fluctuating workloads, leading to bottlenecks in data-intensive applications. An intelligent, adaptive solution is required to optimize resource utilization while minimizing computation time and energy consumption. Brief description of the invention

[0003] The following summary is intended to provide the reader with a basic understanding of the invention. It is not a comprehensive overview of the disclosure and does not highlight essential elements of the invention or define its scope. Its sole purpose is to present some of the concepts disclosed herein in a simplified form as a prelude to the detailed description later.

[0004] The present invention provides a dynamic resource allocation framework that leverages artificial intelligence and machine learning to optimize resource allocation in high-performance computing clusters. The system continuously monitors workload fluctuations and dynamically allocates CPU, GPU, memory, and network bandwidth resources based on predictive analytics. This adaptive resource management increases computational efficiency, reduces latency, and optimizes energy consumption. Detailed description of the invention

[0005] It should be understood that the present disclosure is not limited to the construction details and component arrangements shown herein. The invention may be embodied in other forms and practiced in various ways. The terminology used herein is for the purpose of description only and should not be interpreted as limiting.

[0006] The invention uses reinforcement learning and neural networks for predictive modeling to predict resource requirements and proactively adjust allocation. By leveraging real-time telemetry data, the system ensures optimal load balancing and fault tolerance across distributed nodes. Furthermore, the framework integrates a decentralized control mechanism to avoid single points of failure and increase reliability and scalability.

[0007] The dynamic resource allocation system includes the following main components: • Monitoring module: Continuously collects real-time performance metrics, including CPU / GPU utilization, memory usage, and network throughput from each node in the HPC cluster. • Predictive analytics engine: Uses deep learning models to predict resource requirements based on historical usage patterns and workload variations. • Reinforcement learning controller: Implements an adaptive decision algorithm that dynamically allocates resources to maximize efficiency while minimizing power consumption and latency. • Decentralized orchestration layer: Distributes control across multiple nodes to prevent single points of failure and increase system robustness. • Intelligent load balancing: Redistributes workloads based on predictive insights to ensure balanced resource utilization across distributed environments. • Energy optimization module: Dynamically adjusts power consumption to workload intensity to reduce energy costs and environmental impact. • Fault tolerance mechanism: Detects and mitigates hardware / software failures by reallocating workloads and resources in real time.

[0008] It will be appreciated that the invention described above may be embodied in other specific forms without departing from the scope or essential characteristics of the disclosure. Therefore, it is to be understood that the invention is not limited to the foregoing illustrative details, but is defined by the appended claims.

Claims

[1] A system for dynamic resource allocation in high-performance computing environments, comprising: a monitoring module for collecting real-time performance metrics from distributed compute nodes; a predictive analytics engine that uses deep learning models to predict resource requirements; a reinforcement learning controller that dynamically allocates resources based on workload variations; a decentralized orchestration layer to ensure fault tolerance and scalability; intelligent load balancing to reallocate workloads based on real-time insights; and an energy optimization module to reduce power consumption. [2] The system of claim 1, wherein the predictive analytics engine uses neural networks to analyze historical workload data.