Description:FIELD OF THE INVENTION
The present invention relates to the field of cloud computing and artificial intelligence, specifically to dynamic task scheduling and resource allocation using predictive analytics. It more particularly pertains to AI-driven systems employing deep reinforcement learning, real-time feedback, and blockchain for efficient, energy-aware cloud resource management. BACKGROUND OF THE INVENTION
Dynamic cloud task management systems with adaptive processor allocation aim to address key challenges in cloud computing, such as efficiently managing fluctuating workloads, prioritizing tasks, conserving energy, scaling resources seamlessly, ensuring fault tolerance, and optimizing costs. By employing advanced algorithms, these systems dynamically allocate processors to meet real-time demands, enhance resource utilization, and maintain high performance while reducing operational expenses.
CN104158800A invention introduces an intelligent task allocation method and device to optimize task distribution. It determines task types using a preset decision table and assigns tasks either to a designated service operator or, if unspecified, to appropriate business operators based on preset distribution rules. This approach enhances task management efficiency, optimizes resource allocation, minimizes errors, and improves work productivity in business systems.
US8838801B2 method optimizes cloud computing by analyzing workload characteristics to improve resource allocation. It identifies the workload's architecture, selects a section for static analysis, and determines key workload attributes. Based on this analysis, a matching subset of cloud resources is selected and suggested to a job scheduler for efficient workload execution, ensuring optimal resource utilization.
US20140278807A1 cloud computing system collects data from multiple cloud providers to create performance and cost models. Users input their preferences, and the system predicts the best configurations. These optimized options are presented to the user for selection, allowing the system to operate efficiently across different cloud services.
Dynamic cloud task management systems with adaptive processor allocation aim to address key challenges in cloud computing, such as efficiently managing fluctuating workloads, prioritizing tasks, conserving energy, scaling resources seamlessly, ensuring fault tolerance, and optimizing costs. By employing advanced algorithms, these systems dynamically allocate processors to meet real-time demands, enhance resource utilization, and maintain high performance while reducing operational expenses.
Aspect Existing Solutions Proposed Method
Workload Prediction Basic heuristic or rule-based methods for workload estimation. Uses deep reinforcement learning for accurate workload prediction and proactive resource allocation.
Adaptability Limited adaptability; often static or semi-dynamic allocation strategies. Incorporates real-time feedback loops for continuous learning and dynamic strategy refinement.
Energy Efficiency Energy metrics are often overlooked or secondary considerations. Prioritizes energy-efficient allocation by integrating consumption metrics into the decision-making process.
Scalability struggles with scalability under highly dynamic workloads. Designed for seamless scalability, adapting to fluctuating demands efficiently.
Task Clustering Tasks grouped based on resource requirements alone. Context-aware clustering considers interdependencies and execution contexts for optimized utilization.
Fault Tolerance Reactive fault-tolerance mechanisms that address failures after they occur. Proactive multi-layered fault-tolerance system reallocates resources in anticipation of failures.
Transparency and SLA Compliance Limited mechanisms for ensuring SLA compliance and transparency. Leverages blockchain technology to securely record allocation decisions and ensure SLA compliance.
Sustainability Focused primarily on performance, with limited emphasis on sustainability. Balances performance with sustainability by optimizing energy usage and resource allocation.

OBJECTIVES OF THE INVENTION
Main objective of the present invention is to develop an AI-driven system for predictive workload analysis and dynamic processor allocation in cloud environments.
Another objective of the present invention is to implement real-time feedback loops for continuous refinement of allocation strategies using deep reinforcement learning.
Another objective of the present invention is to enable context-aware task clustering for optimized resource utilization and reduced latency.
Another objective of the present invention is to integrate energy-aware allocation mechanisms for sustainable and efficient processor usage.
Another objective of the present invention is to ensure SLA compliance and transparency through blockchain-based logging of resource decisions and task executions.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The proposed AI-Driven Predictive Allocation with Real-Time Feedback Loops introduces an innovative approach to dynamic cloud task management systems with adaptive processor allocation. This method employs deep reinforcement learning to anticipate workload patterns and resource demands, enabling proactive processor allocation that prevents bottlenecks. The system incorporates real-time feedback loops to continuously refine its allocation strategies based on task execution outcomes, ensuring adaptability to changing conditions. Context-aware task clustering groups tasks by interdependencies and execution contexts, optimizing resource utilization and minimizing latency. Energy-aware allocation prioritizes energy-efficient processor usage, integrating consumption metrics without compromising performance. Additionally, a multi-layered fault-tolerance mechanism proactively reallocates resources in response to predicted failures or unexpected surges, enhancing system reliability. Finally, blockchain technology ensures SLA compliance and transparency by securely recording resource allocation decisions and task execution logs. This approach combines predictive intelligence, adaptability, and sustainability, addressing limitations in existing solutions and paving the way for enhanced cloud computing efficiency.
Herein enclosed an AI-driven predictive allocation system for dynamic cloud task management comprising:
an input data layer configured to receive task logs, virtual machine (VM) performance metrics, and energy consumption data;
a prediction and analysis module employing deep reinforcement learning for predicting workload patterns and resource demands;
a task clustering engine configured to group tasks based on interdependencies and execution contexts;
a resource management module comprising a resource allocation engine and an energy-aware allocation unit for assigning processors based on predicted needs;
a fault tolerance manager configured to reallocate resources in response to predicted failures or surges; and
a blockchain ledger configured to record resource allocation and execution data for SLA compliance and transparency.
The prediction and analysis module is configured to operate in real-time and receive performance feedback from the execution layer to refine the deep reinforcement learning model continuously.
The energy-aware allocation unit is adapted to prioritize processor selection based on energy consumption metrics without degrading system performance.
The task clustering engine dynamically forms clusters of tasks with similar execution contexts and inter-task dependencies to improve latency and efficiency in processor utilization.
The fault tolerance manager provides proactive resource reallocation using predicted anomalies or failure patterns derived from system feedback.
The blockchain ledger maintains immutable records of allocation decisions and task processing details to ensure auditability and SLA compliance.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
FIGURE 2: DYNAMIC CLOUD TASK MANAGEMENT SYSTEM
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In some embodiments of the present invention, relates to an AI-driven predictive allocation system for dynamic cloud task management utilizing deep reinforcement learning and real-time feedback loops. The system predicts workload patterns and allocates processing resources proactively to prevent system bottlenecks.
In some embodiments of the present invention, it comprises an input data layer to collect task logs, virtual machine performance metrics, and energy consumption data; a prediction and analysis module to forecast resource demands; and a task clustering engine to group interdependent tasks.
In some embodiments of the present invention, a resource management module performs context-aware and energy-efficient processor allocation, while a fault tolerance manager handles proactive reallocation during anomalies or surges.
In some embodiments of the present invention, the system incorporates a blockchain ledger to ensure secure and transparent logging of resource allocations and task executions for SLA compliance.
In some embodiments of the present invention, by combining predictive intelligence, adaptive feedback mechanisms, energy-aware processing, and fault-tolerant capabilities, the invention enhances efficiency, reliability, and sustainability in cloud computing environments.
Herein enclosed an AI-driven predictive allocation system for dynamic cloud task management comprising:
an input data layer configured to receive task logs, virtual machine (VM) performance metrics, and energy consumption data;
a prediction and analysis module employing deep reinforcement learning for predicting workload patterns and resource demands;
a task clustering engine configured to group tasks based on interdependencies and execution contexts;
a resource management module comprising a resource allocation engine and an energy-aware allocation unit for assigning processors based on predicted needs;
a fault tolerance manager configured to reallocate resources in response to predicted failures or surges; and
a blockchain ledger configured to record resource allocation and execution data for SLA compliance and transparency.
The prediction and analysis module is configured to operate in real-time and receive performance feedback from the execution layer to refine the deep reinforcement learning model continuously.
The energy-aware allocation unit is adapted to prioritize processor selection based on energy consumption metrics without degrading system performance.
The task clustering engine dynamically forms clusters of tasks with similar execution contexts and inter-task dependencies to improve latency and efficiency in processor utilization.
The fault tolerance manager provides proactive resource reallocation using predicted anomalies or failure patterns derived from system feedback.
The blockchain ledger maintains immutable records of allocation decisions and task processing details to ensure auditability and SLA compliance.
EXAMPLE 1
BEST METHOD
The proposed AI-Driven Predictive Allocation with Real-Time Feedback Loops introduces an innovative approach to dynamic cloud task management systems with adaptive processor allocation. This method employs deep reinforcement learning to anticipate workload patterns and resource demands, enabling proactive processor allocation that prevents bottlenecks. The system incorporates real-time feedback loops to continuously refine its allocation strategies based on task execution outcomes, ensuring adaptability to changing conditions. Context-aware task clustering groups tasks by interdependencies and execution contexts, optimizing resource utilization and minimizing latency. Energy-aware allocation prioritizes energy-efficient processor usage, integrating consumption metrics without compromising performance. Additionally, a multi-layered fault-tolerance mechanism proactively reallocates resources in response to predicted failures or unexpected surges, enhancing system reliability. Finally, blockchain technology ensures SLA compliance and transparency by securely recording resource allocation decisions and task execution logs. This approach combines predictive intelligence, adaptability, and sustainability, addressing limitations in existing solutions and paving the way for enhanced cloud computing efficiency.
NOVELTY:
The framework employs reinforcement learning and predictive analytics for intelligent task scheduling. This approach ensures optimal resource utilization by analyzing historical and real-time data to predict workload patterns. Cloud task management system combines AI-driven prediction, real-time learning, efficient resource allocation, fault tolerance, and blockchain transparency to enhance adaptability, sustainability, and reliability.

, Claims:1. An AI-driven predictive allocation system for dynamic cloud task management comprising:
an input data layer configured to receive task logs, virtual machine (VM) performance metrics, and energy consumption data;
a prediction and analysis module employing deep reinforcement learning for predicting workload patterns and resource demands;
a task clustering engine configured to group tasks based on interdependencies and execution contexts;
a resource management module comprising a resource allocation engine and an energy-aware allocation unit for assigning processors based on predicted needs;
a fault tolerance manager configured to reallocate resources in response to predicted failures or surges; and
a blockchain ledger configured to record resource allocation and execution data for SLA compliance and transparency.
2. The system as claimed in claim 1, wherein the prediction and analysis module is configured to operate in real-time and receive performance feedback from the execution layer to refine the deep reinforcement learning model continuously.
3. The system as claimed in claim 1, wherein the energy-aware allocation unit is adapted to prioritize processor selection based on energy consumption metrics without degrading system performance.
4. The system as claimed in claim 1, wherein the task clustering engine dynamically forms clusters of tasks with similar execution contexts and inter-task dependencies to improve latency and efficiency in processor utilization.
5. The system as claimed in claim 1, wherein the fault tolerance manager provides proactive resource reallocation using predicted anomalies or failure patterns derived from system feedback.
6. The system as claimed in claim 1, wherein the blockchain ledger maintains immutable records of allocation decisions and task processing details to ensure auditability and SLA compliance.