A system for optimizing the allocation of computing resources

Abstract

A system (100) for optimizing the allocation of computing resources, comprising: a data acquisition unit (102) comprising a retrieval processor (102a) configured to retrieve historical performance data, which is then stored in a data storage module (104); a centralized processing unit (106) comprising a dedicated processor and a graphics processing unit (GPU) for performing a plurality of resource allocation functions, the centralized processing unit further comprising: a prediction module (106a) configured to implement machine learning algorithms to analyze historical performance data and generate workload forecasts; a performance monitoring module (106b) configured to track real-time system metrics, including CPU utilization, memory consumption, and network latency; a resource allocation module (106c) configured to dynamically adjust the computing resources based on the workload forecasts and real-time system metrics; a container orchestration module (106d) configured to manage the deployment and scaling of microservices across distributed containers; and an administrative user interface module (108) comprising a user display (110) and a visualization module (112), wherein the visualization module (112) generates representations of performance metrics and the user display (110) is configured to display system performance metrics and enable configuration of scaling policies.

Description

FIELD OF THE INVENTION

[0001] The present disclosure relates to a system for optimizing the allocation of computing resources. More specifically, the present invention relates to a machine learning-based system for optimizing performance and resource utilization in real time. BACKGROUND OF THE INVENTION

[0002] System scalability is critical to ensure optimal performance as the number of users or data volume increases. Existing solutions struggle with maintaining efficiency and managing resources, especially in cloud-based and distributed computing environments.

[0003] One of the state-of-the-art solutions, titled “Adaptive Resource Allocation in Cloud Systems,” uses a static allocation strategy that does not efficiently handle sudden workload spikes, resulting in resource waste or performance bottlenecks.

[0004] Another prior art, entitled “Performance Scaling in Distributed Databases,” uses sharding and partitioning methods that are complex to implement and lack adaptability to real-time workload changes.

[0005] Another prior art, entitled “Elastic Load Balancing for Web Applications,” uses load balancers to distribute traffic but often does not take into account the underlying computing power, which can degrade system performance.

[0006] The descriptions of the state of the art indicate that existing solutions have limitations in terms of adaptability, real-time resource management and achieving balanced performance under fluctuating requirements.

[0007] In light of the previous discussion, it is clear that there is a need for a system that enables real-time performance optimization and resource allocation. SUMMARY OF THE INVENTION

[0008] The present disclosure relates to a system for optimizing the allocation of computing resources. The present invention relates to a system that optimizes performance and resource utilization in real time. It uses machine learning algorithms to predict workload fluctuations and adjust resource allocation accordingly. The system offers seamless integration with existing infrastructure, thus improving overall efficiency and user satisfaction. The main aspect of the invention is to provide a scalable system that adapts to real-time user demand. Another aspect of the invention includes intelligent resource management using predictive analytics. The system ensures cost-effective operations in cloud environments by optimizing resource allocation. Furthermore, the solution supports horizontal and vertical scaling with minimal user disruption.Finally, the invention integrates monitoring and feedback mechanisms for continuous optimization.

[0009] The present disclosure is intended to provide a system for optimizing the allocation of computing resources. The system comprises: a data acquisition unit having a retrieval processor configured to obtain historical performance data, which is then stored in a data storage module; and a central processing unit having a dedicated processor and a graphics processing unit for performing multiple resource allocation functions, the central processing unit further comprising: a prediction module configured to implement machine learning algorithms for analyzing historical performance data and generating workload forecasts; a performance monitoring module configured to track real-time system metrics including CPU utilization, memory consumption, and network latency;a resource allocation module configured to dynamically adjust compute resources based on workload forecasts and real-time system metrics; and a container orchestration module configured to manage the provisioning and scaling of microservices across distributed containers. The system further includes an administrative user interface module including a user display and a visualization module, wherein the visualization module generates representations of performance metrics and the user display is configured to display system performance metrics and enable the configuration of scaling policies.

[0010] An objective of the present disclosure is to provide a system for optimizing the allocation of computing resources.

[0011] Another objective of the present disclosure is to provide a scalable software solution that dynamically adapts to workload fluctuations to maintain optimal performance.

[0012] Another objective of this disclosure is to ensure efficient resource utilization by leveraging advanced load prediction algorithms.

[0013] Another objective of this disclosure is to enable seamless scaling in cloud environments while minimizing costs.

[0014] Another objective of this disclosure is to improve the user experience by reducing latency and maximizing system availability.

[0015] Another objective of this disclosure is to be easily integrated into existing software architectures and to be adaptable to different deployment environments.

[0016] To further clarify the advantages and features of the present disclosure, a more detailed description of the invention will be given with reference to specific embodiments thereof illustrated in the accompanying drawings. It should be noted that these drawings represent only typical embodiments of the invention and are therefore not to be considered limiting its scope. The invention will be described and explained in additional detail and in greater detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE CHARACTERS

[0017] These and other features, aspects, and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. Fig. 1 shows a block diagram of a system for optimizing the allocation of computing resources according to an embodiment of the present disclosure. Fig. 2 illustrates a block diagram showing the architecture of the scalable proposed system according to an embodiment of the present disclosure.

[0018] Furthermore, those skilled in the art will appreciate that elements in the drawings are shown for convenience and may not necessarily be drawn to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art who would benefit from the description herein. DETAILED DESCRIPTION:

[0019] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation upon the scope of the invention is thereby intended, since such changes and further modifications to the illustrated system, and such further applications of the principles of the invention as illustrated therein, are contemplated as would normally occur to one skilled in the art to which the invention pertains.

[0020] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0021] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, the occurrences of the phrase "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not all refer to the same embodiment.

[0022] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps not only includes those steps, but may also include other steps not expressly listed or inherent in such process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" does not preclude, without further limitation, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0024] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0025] The functional units described in this specification have been referred to as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may contain executable code and may, for example, comprise one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, function, or other construct.However, the executable file of an identified device need not be physically located together, but may comprise different instructions stored in different locations which, when logically linked together, constitute the device and fulfill the stated purpose of the device.

[0026] Indeed, executable code of a device or module may be a single instruction or multiple instructions, and may even be distributed across several different code segments, among different applications, and across multiple storage devices. Similarly, operational data may be identified and represented herein within the device and embodied in any suitable form and organized in any suitable type of data structure. The operational data may be captured as a single set of data or distributed across different locations, including different storage devices, and may exist, at least in part, as electronic signals in a system or network.

[0027] References in this specification to "a selected embodiment," "an embodiment," or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the appearances of the phrases "a selected embodiment," "in an embodiment," or "in an embodiment" in various places in this specification do not necessarily refer to the same embodiment.

[0028] Furthermore, the described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the disclosed subject matter. However, one of ordinary skill in the art will recognize that the disclosed subject matter may be practiced without one or more of the specific details, or with different methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosed subject matter.

[0029] According to the example embodiments, the disclosed computer programs or modules may be executed in many example ways, for example, as an application stored in the memory of a device or as a hosted application executing on a server and communicating with the device application or browser via a variety of standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs may be written in example programming languages ​​that execute from memory on the device or from a hosted server, for example, BASIC, COBOL, C, C++, Java, Pascal, or scripting languages ​​such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.

[0030] Some of the disclosed embodiments comprise or otherwise involve data transmission over a network, such as communicating various inputs or files over the network. The network may comprise, for example, one or more of the following: the Internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g., a PSTN, Integrated Services Digital Network (ISDN), a cellular network, and Digital Subscriber Line (xDSL)), radio, television, cable, satellite, and / or any other delivery or tunneling mechanism for transmitting data. The network may comprise multiple networks or subnetworks, each of which may comprise, for example, a wired or wireless data path. The network may comprise a circuit-switched voice network, a packet-switched data network, or any other network capable of transmitting electronic communications.For example, the network may include networks based on Internet Protocol (IP) or Asynchronous Transfer Mode (ATM), and may support voice using, for example, VoIP, Voice over ATM, or other comparable protocols used for voice data communications. In one implementation, the network includes a cellular network configured for the exchange of text or SMS messages.

[0031] Examples of the network include, but are not limited to, a Personal Area Network (PAN), a Storage Area Network (SAN), a Home Area Network (HAN), a Campus Area Network (CAN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a Virtual Private Network (VPN), an Enterprise Private Network (EPN), the Internet, a Global Area Network (GAN), and so on.

[0032] Fig. 1 shows a block diagram of a system (100) for optimizing the allocation of computing resources according to an embodiment of the present disclosure.

[0033] With reference to Fig. 1, the system (100) comprises a data acquisition unit (102) comprising a retrieval processor (102a) configured to obtain historical performance data, which is then stored in a data storage module (104); and a centralized processing unit (106) comprising a dedicated processor and a graphics processing unit (GPU) for performing a plurality of resource allocation functions, the centralized processing unit (106) further comprising: a prediction module (106a) configured to implement machine learning algorithms for analyzing historical performance data and generating workload forecasts; a performance monitoring module (106b) configured to track real-time system metrics such as CPU utilization, memory consumption, and network latency;a resource allocation module (106c) configured to dynamically adjust computing resources based on the workload forecasts and real-time system metrics; and a container orchestration module (106d) configured to manage the provisioning and scaling of microservices across distributed containers. The system (100) further comprises an administrative user interface module (108) comprising a user display (110) and a visualization module (112), wherein the visualization module (112) generates representations of performance metrics and the user display (110) is configured to display system performance metrics and enable the configuration of scaling policies.

[0034] In one embodiment, the prediction module (106a) is further configured to implement supervised learning and reinforcement learning techniques for workload prediction, detect seasonal variations and anomalies in workload patterns, and continuously update the machine learning models based on real-time feedback.

[0035] In one embodiment, the performance monitoring module (106b) is further configured to implement anomaly detection algorithms to identify performance irregularities, monitor predefined performance thresholds, and trigger automatic scaling actions when thresholds are exceeded.

[0036] In one embodiment, the resource allocation module (106c) is further configured to perform horizontal scaling by adjusting the number of resource instances, vertical scaling by changing resource capacities, and automatic release of resources during times of lower demand.

[0037] In one embodiment, the container orchestration module (106d) is further configured to: implement load balancing across distributed containers, perform continuous updates to maintain service continuity, and ensure high availability and fault tolerance of microservices.

[0038] In one embodiment, the administrative user interface module (108) is further configured to: generate interactive data visualizations and heatmaps using a visualization module (112) that uses the dedicated GPU to generate visualizations; display historical performance trends and analytics via the user display (110); enable customization of scaling thresholds and policies; and provide diagnostic tools for system optimization.

[0039] In one embodiment, the resource allocation module (106c) is further configured to implement a feedback loop system that collects performance metrics following changes in resource allocation, analyzes the effectiveness of allocation decisions, and adjusts allocation strategies based on historical effectiveness.

[0040] In one embodiment, the data storage module (104) is configured to: manage historical performance data; store configuration settings and scaling policies; and archive system metrics for trend analysis.

[0041] In one embodiment, the data acquisition unit (102), the data storage module (104), the central processing unit (106) and its modules, and the administrative user interface module (108) may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like.

[0042] The present invention relates to a system for optimizing performance and resource utilization in real time. The proposed system uses machine learning algorithms to predict workload fluctuations and adjust resource allocation accordingly. The system provides seamless integration with existing infrastructure, thus improving overall efficiency and user satisfaction.

[0043] Fig. 2 illustrates a block diagram showing the architecture of the scalable proposed system according to an embodiment of the present disclosure.

[0044] According to Fig. 2, the system mainly includes a machine learning-based forecasting module, a real-time performance monitoring and auto-scaling module, a container orchestration for the microservice management module, and an administrator user interface for system insights and policy configuration.

[0045] The machine learning-based forecasting module uses advanced machine learning algorithms to predict future system utilization. By analyzing historical performance data and current system metrics, the software solution generates accurate utilization forecasts. These forecasts are critical for decision-making regarding dynamic resource allocation. In particular, the predictive model considers trends, seasonal fluctuations, and anomalies to proactively optimize resource management. The system ensures that resources are neither underutilized nor overutilized, thereby improving performance efficiency. The machine learning models are continuously updated to adapt to changing utilization patterns, leveraging techniques such as supervised learning, reinforcement learning, and real-time feedback loops.

[0046] Real-time performance monitoring and automatic scaling are achieved using a comprehensive monitoring system that tracks performance indicators such as CPU utilization, memory consumption, and network latency in real time. When certain predefined performance thresholds are exceeded, the monitoring system initiates automatic resource scaling. This scaling process ensures that the system maintains optimal efficiency by dynamically adjusting resource availability. For example, if an increase in demand is detected, the system can allocate additional resources to prevent performance degradation. Conversely, when demand decreases, resources are released to save energy and reduce operating costs. The monitoring mechanism uses anomaly detection algorithms to identify performance irregularities and ensure system stability.

[0047] Container orchestration platforms are used to efficiently manage microservices. The system uses technologies such as Kubernetes or Docker Swarm to orchestrate containers, ensuring high availability, fault tolerance, and efficient load balancing. Container orchestration automates the deployment, scaling, and management of microservices and enables seamless communication between them. By evenly distributing workloads across containers and implementing continuous updates, the system minimizes downtime and ensures service continuity. Furthermore, it efficiently manages the underlying infrastructure, allocates resources based on real-time demand, and enables horizontal scaling of services as needed.

[0048] In one embodiment, a dedicated user interface (UI) is provided to give administrators complete visibility into system performance. This interface displays metrics such as resource utilization, workload forecasts, and performance analytics. Administrators can also use the UI to customize scaling policies, set performance thresholds, and configure alerts. The interface is designed to be intuitive and interactive, offering features such as data visualizations, heat maps, and historical performance trends. Furthermore, the UI provides diagnostic tools for troubleshooting and optimizing system settings. Through the UI, administrators can fine-tune scaling algorithms to align with organizational goals and ensure the system remains flexible and responsive to evolving requirements.

[0049] This invention relates to a dynamic system that ensures optimal performance and resource efficiency by predicting workload requirements and adjusting resource allocation in real time. The system includes modules configured for predictive analytics, monitoring mechanisms, and a framework for dynamic resource allocation. This invention increases operational efficiency, minimizes costs, and improves the user experience in cloud-based environments.

[0050] The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Furthermore, the actions of any flowchart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0051] Advantages, other benefits, and solutions to problems have been described above with respect to specific embodiments. However, the advantages, benefits, solutions to problems, and any components that may cause an advantage or solution to occur or become more apparent are not to be construed as a critical, required, or essential feature or component of any or all of the claims. REFERENCES 100 A system for optimizing the allocation of computing resources. 102 Data acquisition unit 102a Retrieval processor 104 Data storage module 106 Central Processing Unit 106a Forecast module 106b Performance Monitoring Module 106c Resource Allocation Module 106d Container Orchestration Module 108 Administrative User Interface Module 110 User display 112 Visualization module 202 User Interface 204 Surveillance system 206 Predictive Analytics Module 208 Dynamic Resource Allocator 210 Resource Pool 212 Cloud Infrastructure 214 Feedback mechanism 216 Send Insights To Predict 220 Collects performance data

Claims

[1] A system (100) for optimizing the allocation of computing resources, comprising: a data acquisition unit (102) comprising a retrieval processor (102a) configured to retrieve historical performance data, which is then stored in a data storage module (104); a centralized processing unit (106) comprising a dedicated processor and a graphics processing unit (GPU) for performing a plurality of resource allocation functions, the centralized processing unit further comprising: a prediction module (106a) configured to implement machine learning algorithms to analyze historical performance data and generate workload forecasts; a performance monitoring module (106b) configured to track real-time system metrics, including CPU utilization, memory consumption, and network latency; a resource allocation module (106c) configured to dynamically adjust the computing resources based on the workload forecasts and real-time system metrics; a container orchestration module (106d) configured to manage the deployment and scaling of microservices across distributed containers; and an administrative user interface module (108) comprising a user display (110) and a visualization module (112), wherein the visualization module (112) generates representations of performance metrics and the user display (110) is configured to display system performance metrics and enable configuration of scaling policies. [2] The system (100) of claim 1, wherein the prediction module (106a) is further configured to implement supervised learning and reinforcement learning techniques for workload prediction, detect seasonal variations and anomalies in workload patterns, and continuously update the machine learning models based on real-time feedback. [3] The system (100) of claim 1, wherein the performance monitoring module (106b) is further configured to implement anomaly detection algorithms to identify performance irregularities, monitor predefined performance thresholds, and trigger automatic scaling actions when thresholds are exceeded. [4] The system (100) of claim 1, wherein the resource allocation module (106c) is further configured to perform horizontal scaling by adjusting the number of resource instances, vertical scaling by changing resource capacities, and automatic release of resources during times of lower demand. [5] The system (100) of claim 1, wherein the container orchestration module (106d) is further configured to: implement load balancing across distributed containers, perform rolling updates to maintain service continuity, and ensure high availability and fault tolerance of microservices. [6] The system (100) of claim 1, wherein the administrative user interface module (108) is further configured to: generate interactive data visualizations and heatmaps using a visualization module (112) that uses the dedicated GPU to generate visualizations; display historical performance trends and analytics via the user display (110); enable adjustment of scaling thresholds and policies; and provide diagnostic tools for system optimization. [7] The system (100) of claim 1, wherein the resource allocation module (106c) is further configured to implement a feedback loop system that: collects performance metrics following changes in resource allocation; analyzes the effectiveness of allocation decisions; and adjusts allocation strategies based on historical effectiveness. [8] The system (100) of claim 1, wherein the data storage module (104) is configured to manage historical performance data, store configuration settings and scaling policies, and archive system metrics for trend analysis.

Images