Description:The present invention pertains to the field of cloud computing and, more specifically, focuses on power optimization techniques for resource management in cloud computing environments. The invention employs machine learning algorithms to enhance the efficiency of resource allocation, thereby reducing power consumption and contributing to sustainable and environmentally friendly cloud computing practices.
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 understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Cloud computing has emerged as a pivotal technology, providing scalable and on-demand access to a variety of computing resources. As the adoption of cloud services continues to grow, so does the demand for efficient resource management strategies to optimize performance and address environmental concerns, particularly in the context of power consumption.

Traditional resource management approaches often fall short in dynamically adapting to the varying workloads and fail to optimize power usage effectively. Consequently, there is a pressing need for innovative solutions that can intelligently allocate resources, ensuring optimal performance while minimizing energy consumption.

The escalating environmental impact of data centers and cloud infrastructure has accentuated the importance of developing sustainable practices. Power consumption represents a significant portion of operational costs, making it imperative to devise techniques that not only enhance efficiency but also contribute to the overall reduction of energy consumption in cloud computing environments.

The present invention recognizes these challenges and leverages the capabilities of machine learning to introduce a dynamic and adaptive system for power optimization. By analyzing historical workload patterns and making real-time predictions, the invention aims to revolutionize resource management in cloud computing, leading to reduced operational costs, enhanced performance, and a more sustainable cloud infrastructure.

OBJECTIVE OF THE INVENTION

Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.

The primary objective of the present invention is to provide a novel and efficient solution for power optimization in cloud computing environments. The invention seeks to address the challenges associated with traditional resource management methods by integrating machine learning techniques into the decision-making processes.
SUMMARY OF THE INVENTION
This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

The present invention introduces a pioneering approach to power optimization in cloud computing environments, leveraging advanced machine learning techniques. In summary, the system dynamically manages and allocates computing resources based on historical workload patterns and real-time data, leading to enhanced energy efficiency and reduced operational costs. By employing a proactive model that anticipates future resource requirements, the invention ensures optimal performance while minimizing power consumption, addressing the limitations of traditional resource management methods. The adaptability of the system to varying workloads and its scalability across diverse cloud environments underscore its potential to revolutionize resource management practices, contributing significantly to the sustainability and efficiency of cloud computing infrastructure.

In essence, the invention offers a comprehensive solution to the challenges associated with power optimization in cloud computing, marking a significant advancement in the pursuit of environmentally conscious and cost-effective cloud services.
BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary system for power optimization in cloud computing environments, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, 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. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

The invention introduces a sophisticated system for power optimization in cloud computing environments, seamlessly integrating machine learning algorithms into the resource management framework. The detailed description below outlines the key components and functionalities of the invention.

1. Machine Learning Module: At the core of the system lies a machine learning module responsible for analyzing historical workload data. This module employs advanced algorithms, such as neural networks, trained on extensive datasets encompassing diverse workload patterns. The machine learning model develops a deep understanding of the relationships between specific workloads and resource requirements, forming the basis for predictive decision-making.

2. Resource Allocation Module: The resource allocation module dynamically adjusts computing resources based on predictions generated by the machine learning module. In real-time, the system monitors current workload and power consumption, feeding this data into the machine learning model for continuous refinement. By adopting an adaptive approach, the system optimizes resource allocation to align precisely with the evolving demands of applications and services hosted within the cloud environment.

3. Monitoring Module: A dedicated monitoring module is incorporated to collect real-time data on power consumption and workload. This module interfaces with the cloud infrastructure, collecting metrics related to CPU usage, memory utilization, and other relevant parameters. The collected data serves as a critical input for both the machine learning module and the resource allocation module, ensuring that the system remains responsive to immediate changes in workload and power requirements.

4. Training Mechanism: To enhance the accuracy of predictions, the machine learning module includes a continuous training mechanism. Periodically, the model is updated with the latest historical data and real-time observations. This ensures that the system remains adaptive to evolving workload patterns, maintaining a high level of prediction precision and responsiveness to dynamic changes in the cloud environment.

5. Proactive Power Optimization: The invention's proactive nature enables it to anticipate future resource requirements based on the learned patterns. This foresight allows the system to make preemptive adjustments to resource allocations, preventing underutilization or over-provisioning. The result is a finely tuned resource management strategy that maximizes efficiency, minimizes power consumption, and optimally balances performance.

6. Scalability and Integration: Designed with scalability in mind, the system seamlessly integrates into various cloud computing environments. It accommodates different application workloads, infrastructure configurations, and scales to meet the demands of both small-scale deployments and large-scale cloud infrastructures. This adaptability ensures the widespread applicability of the power optimization techniques across diverse cloud computing scenarios.

The detailed description illustrates the innovative combination of machine learning and dynamic resource management in the invention. By providing a holistic solution to power optimization challenges in cloud computing, the system stands poised to redefine industry standards, offering a sustainable and efficient approach to resource allocation and energy consumption in the rapidly evolving landscape of cloud services.

In one embodiment of the invention, the machine learning module is further enhanced to include a dynamic workload prediction and resource scaling mechanism. The system not only analyzes historical data but continuously monitors incoming workloads in real-time. By predicting future workload trends, the machine learning module proactively adjusts resource allocations, scaling up or down computing resources to meet anticipated demand. This embodiment ensures an even more responsive and finely-tuned resource management system, effectively minimizing both underutilization and over-provisioning of resources. The dynamic workload prediction and resource scaling contribute to the system's ability to adapt swiftly to changing patterns, optimizing power consumption and maintaining optimal performance in the face of varying workloads.

In yet another embodiment, the invention extends its capabilities to integrate with hybrid cloud environments. Recognizing the prevalence of multi-cloud strategies, the system incorporates algorithms that consider workload distribution across diverse cloud platforms. The machine learning model is trained on data reflecting interactions and dependencies between workloads in different cloud environments. This embodiment facilitates seamless resource allocation and power optimization, not only within a single cloud but also across a hybrid infrastructure. By leveraging insights from various platforms, the system ensures comprehensive resource management, maximizing efficiency and minimizing power consumption on a broader scale. The hybrid cloud integration embodiment enhances the versatility of the invention, addressing the evolving needs of organizations adopting diverse cloud deployment strategies.

The invention introduces a machine learning-based approach to optimize power consumption in cloud computing environments. By continuously analyzing workload patterns, the system dynamically adjusts resource allocations to match the demand, thereby minimizing power usage. The machine learning model is trained on historical data to predict future resource requirements and make proactive adjustments.
While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.

, Claims:1. A system for power optimization in cloud computing environments, comprising:
• a machine learning module configured to analyze historical workload data;
• a resource allocation module configured to dynamically adjust resource allocations based on predictions from the machine learning module;
• a monitoring module to collect real-time data on power consumption and workload.

2. The system of claim 1, wherein the machine learning module utilizes a neural network trained on historical workload patterns to predict future resource requirements.

3. A method for power optimization in a cloud computing environment, comprising:
• collecting historical workload data;
• training a machine learning model on the historical data to predict future resource requirements;
• dynamically adjusting resource allocations based on predictions from the machine learning model.

4. The method of claim 3, further comprising:
• monitoring real-time power consumption and workload;
• updating the machine learning model based on the real-time data.

5. A computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 3.