Ephemeral Cloud Containers for Secure Data Processing
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Solution Overview
Problem
Persistent computing systems are inefficient and costly due to their inflexibility in managing fluctuating computing needs, especially when handling large data sets, and pose challenges in securing data when multiple clients' data is managed by a third-party vendor.
Innovation Solution
Implementing ephemeral cloud-based computing resources that can be scaled up or down based on real-time needs, allowing for cost-effective and secure data processing and analysis by provisioning and terminating resources as required, while ensuring data security through secure access management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If persistent computing systems are built to handle worst-case scenarios, then data processing capability is improved, but cost and resource efficiency deteriorate
Solution Approach 1:
The patent implements dynamic computing resources that can be provisioned and deprovisioned based on real-time workload demands. Instead of static persistent systems, the invention uses ephemeral containers that are created when needed and destroyed when not needed, allowing the system to adapt its resource allocation dynamically to match actual processing requirements without over-provisioning for worst-case scenarios
Solution Approach 2:
The system changes the operational parameters of computing resources by transitioning between different states (provisioned/deprovisioned, active/inactive) based on workload conditions. This allows the system to optimize resource utilization by adjusting parameters such as container lifecycle, resource allocation levels, and system state transitions rather than maintaining fixed parameters
2Productivity
If persistent systems are built for maximum computing resources, then handling large data sets is improved, but cost increases
Solution Approach 1:
The patent employs ephemeral computing containers that are created on-demand and destroyed after use, replacing expensive persistent infrastructure with shorter-lived, more cost-effective computing resources. These containers are provisioned only when processing is needed and are automatically terminated afterward, eliminating the need to pay for continuously maintained persistent systems with maximum capacity
3Adaptability or versatility
If third-party vendors manage multiple clients' data, then service versatility is improved, but data security deteriorates
Solution Approach 1:
The patent segments the computing environment into isolated client-specific containers, where each client's data and processing operations occur in separate, isolated environments. This segmentation prevents cross-contamination between clients while allowing the third-party vendor to serve multiple clients, thus maintaining both service versatility and data security through architectural isolation
Solution Approach 2:
The system introduces an intermediary layer (the ephemeral containerization platform) that mediates between the third-party vendor's infrastructure and multiple clients' data. This intermediary ensures that each client's data remains isolated and secure while still allowing the vendor to provide unified service management across multiple clients
Data Source
AI summary
Systems and methods for generating secure ephemeral cloud-based computing resources for data operations is provided. In one or more examples, a computing hub can receive a data set as well as one or more business process instructions from an external entity. In response to receiving the data set and business process instructions, the computing hub can create one or more containers within a persistent data storage computing resource and store the data set in the one or more containers. The computing hub can generate one or more cloud-based computing resources on one or more cloud-based computing resource platforms based on the received business processes. The computing hub can be configured to arbitrate access to the stored data set from the generated cloud-based computing resources and can be also configured to track lineage information of the data set as it is process by the one or more cloud-based computing resources.