SYSTEM FOR DYNAMIC RESOURCE ALLOCATION IN CLOUD COMPUTING

Abstract

The present invention relates to a system for dynamic resource allocation in cloud computing environments, utilizing real-time monitoring, predictive analytics, and intelligent optimization to efficiently manage computational resources such as CPU, memory, and network bandwidth. The system comprises a monitoring module for collecting usage metrics, a predictive analytics engine using machine learning to forecast future demand, a resource allocation manager for adjusting resources dynamically, and a policy engine enforcing predefined business rules. By continuously refining decisions through an optimization algorithm, the system enhances performance, reduces costs, and adapts seamlessly to varying workloads across public, private, and hybrid cloud setups.

Specification

Description:The embodiments of the present invention generally relates to the field of cloud computing and resource management systems. More specifically, it pertains to a system and method for dynamic resource allocation in cloud computing environments. This invention leverages real-time monitoring, predictive analytics, and machine learning algorithms to optimize the allocation of computational resources such as CPU, memory, storage, and network bandwidth. It is applicable to various cloud setups, including public, private, and hybrid clouds, aiming to enhance performance, reduce costs, and ensure efficient resource utilization based on varying workload demands.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the unders , Claims:1. A system for dynamic resource allocation in cloud computing environments, comprising:
a monitoring module configured to collect real-time data on resource usage metrics from a plurality of cloud resources, including but not limited to CPU utilization, memory usage, network bandwidth, and disk I/O;
a predictive analytics engine that analyzes historical data and utilizes machine learning algorithms to forecast future resource demand;
a resource allocation manager configured to dynamically adjust the allocation of cloud resources based on real-time monitoring data and demand predictions, including the scaling up or down of virtual machines, containers, or memory and CPU resources;
a policy engine that enforces predefined policies related to resource allocation, considering business objectives such as cost optimization, performance maximization, and compliance with service level agreements (SLAs); and
an optimization algorithm that refines resource allocation decisions based on real-time conditions to maximize

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