Hybrid Cloud Task Scheduling via Data Anonymization
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Solution Overview
Problem
Hybrid cloud environments face challenges in efficiently scheduling tasks that require private data, as existing methods are limited by the inelastic nature of private clouds and lack fine-grained granularity in data handling, making it difficult to execute tasks securely and efficiently across both private and public clouds.
Innovation Solution
A computer-implemented method and system for task scheduling in a hybrid cloud that decomposes jobs into tasks, identifies tasks needing private data, anonymizes data before execution in the external cloud using anonymizing functions from the private cloud, and de-anonymizes data as necessary, allowing for secure and elastic computation by dynamically allocating resources and handling sensitive data at a fine granularity level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tasks requiring private data are executed in the external cloud, then resource elasticity and computation efficiency are improved, but data security and privacy preservation deteriorate
Solution Approach 1:
The patent segments data into two distinct types: anonymized data (safe for external cloud processing) and non-anonymized private data (kept in private cloud). This segmentation allows tasks to be executed in the external cloud using anonymized data while maintaining security by keeping sensitive data isolated, thus resolving the contradiction between resource elasticity and data security.
Solution Approach 2:
The patent introduces anonymization as an intermediary process that transforms private data into anonymized data before external cloud processing. This intermediary mechanism enables secure data sharing by acting as a buffer between the private data source and the external cloud, allowing resource elasticity improvement without compromising data security.
2Object-affected harmful factors
If all data is kept in the private cloud, then data security is maintained, but resource inelasticity and computation efficiency deteriorate
Solution Approach 1:
The patent divides data into anonymized and non-anonymized portions, allowing anonymized data to be shared with the external cloud for additional resource utilization. This segmentation enables the system to overcome private cloud resource inelasticity while maintaining security through selective data sharing.
Solution Approach 2:
The patent applies partial anonymization where only the portions of data needed for external processing are anonymized and shared, while the complete private data remains secured in the private cloud. This partial action approach enables computation efficiency improvement without fully exposing private data, thus maintaining security while gaining resource elasticity.
3Adaptability or versatility
If fine-grained data handling is implemented, then task execution flexibility is improved, but system complexity increases
Solution Approach 1:
The patent implements fine-grained data handling by segmenting data into anonymized and non-anonymized portions at the task level. This segmentation provides high task execution flexibility by allowing selective processing in different cloud environments, while the automated anonymization process manages the complexity burden.
Solution Approach 2:
The system employs automated anonymization functions that self-manage the complexity of fine-grained data handling. The anonymization process automatically identifies which data portions need anonymization for each task, reducing manual intervention complexity while maintaining fine-grained control for task execution flexibility.
Data Source
AI summary
Task scheduling in a hybrid cloud that includes a private cloud and an external cloud is performed. First a job to be performed is identified. Then, the job identified is decomposed into a set of tasks, whereby a task schedule is obtained for tasks of the set of tasks to be executed across the hybrid cloud. Next, a task to be executed in the external cloud that requires private data from the private cloud for its execution is detected from the task schedule. Finally, one or more non-anonymized portions of the private data are anonymized before execution of the detected task in the external cloud, by executing an anonymizing function from the private cloud. De-anonymization functionality may similarly be involved.