ALGORITHM FOR DISTRIBUTED DATA PROCESSING IN MULTI-CLOUD ENVIRONMENTS

Abstract

The present invention relates to an algorithm for efficient distributed data processing across multi-cloud environments. The algorithm dynamically allocates data processing tasks to multiple cloud platforms based on real-time resource availability, optimizing performance, and ensuring fault tolerance. It incorporates intelligent load balancing, data synchronization, and recovery mechanisms to handle cloud failures and ensure continuous data processing. The system optimizes resource usage to minimize operational costs, using predictive models and real-time monitoring to select the most cost-effective cloud instances. This invention enhances the scalability, reliability, and cost-efficiency of cloud-based data processing systems.

Specification

Description:The embodiments of the present invention generally relates to the field of distributed data processing, and more particularly, to an algorithm for optimizing data processing across multi-cloud environments. The invention is applicable to cloud computing systems that use multiple cloud platforms to handle large-scale data storage and processing tasks, ensuring efficient resource utilization, seamless data synchronization, and fault tolerance.
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.

The rapid growth of cloud computing has transformed the way businesses manage and process their data. Organizations , Claims:1. A method for distributed data processing in a multi-cloud environment, comprising the steps of:
identifying available cloud resources across multiple cloud platforms;
segmenting input data into smaller chunks for distribution across the available cloud resources;
dynamically allocating tasks to cloud instances based on real-time resource availability, including computational power and storage capacity;
synchronizing data across the distributed cloud platforms using a consistency model to ensure data integrity;
monitoring cloud resource health and dynamically redistributing tasks in response to resource failures or performance degradation.

2. The method of claim 1, wherein the load balancing step utilizes a machine learning model to predict resource requirements and optimize task allocation based on historical performance data.

3. The method of claim 1, wherein the data synchronization step uses a quorum-based approach to ensure eventual consistency between cloud platforms.

4. The method of claim 1, wher

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