Cloud Data Egress Planning for Predictable Cost Control
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Solution Overview
Problem
Cloud storage providers charge significant egress fees for data transfer out of their systems, which are often unexpected and difficult for businesses to manage due to lack of real-time cost estimation, leading to hidden costs and challenges in monitoring and planning data egress tasks.
Innovation Solution
A method utilizing machine learning algorithms to calculate computational costs and business criticality, reconfiguring egress plans to reduce costs, and providing recommendations for alternative data egress strategies based on historical trends and business priorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If cloud storage providers implement data egress fees, then cost control and billing accuracy are improved, but business operational flexibility and data access freedom deteriorate
Solution Approach 1:
The system performs preliminary actions by calculating and displaying estimated egress costs before data transfer occurs. It predicts costs based on data size, destination, and timing, allowing businesses to plan data egress operations in advance and understand financial implications before execution, thus resolving the contradiction between cost control and operational flexibility
Solution Approach 2:
The system implements feedback mechanisms by providing real-time cost estimates and billing projections during data egress operations. It continuously monitors actual vs. estimated costs and provides feedback to users, enabling dynamic adjustment of data transfer strategies while maintaining cost visibility and control throughout the operation
2Power
If cloud storage providers charge high egress fees, then revenue optimization is improved, but business operational flexibility deteriorates
Solution Approach 1:
The system performs preliminary calculations of egress costs based on data characteristics and destination information before transfer occurs. By estimating costs in advance using algorithms that consider data size, compression ratios, and destination pricing, the system enables businesses to plan operations that optimize revenue while maintaining flexibility in data movement decisions
Solution Approach 2:
The system utilizes parameter changes by adjusting data transfer parameters such as compression levels, transfer timing, and destination selection based on cost predictions. It dynamically modifies these parameters to optimize the balance between revenue generation and operational flexibility, allowing businesses to adapt data egress strategies to specific business needs while managing costs
3Loss of information
If cloud storage providers provide detailed cost information upfront, then business decision-making quality is improved, but system complexity and information processing requirements deteriorate
Solution Approach 1:
The system extracts and isolates cost calculation functionality from the complex data transfer process. It separates cost estimation into distinct computational steps that process data characteristics, destination information, and pricing models independently, making the system more manageable while providing transparent cost information to users
Solution Approach 2:
The system introduces an intermediary layer between data transfer operations and cost billing. This intermediary component calculates and communicates cost estimates to users before transfer occurs, simplifying the information flow and making cost transparency achievable without overwhelming system complexity. The intermediary handles cost calculations, projections, and user communication as a separate functional layer
4Ease of operation
If businesses perform data egress without cost optimization, then operational simplicity is maintained, but cost efficiency deteriorates
Solution Approach 1:
The system implements self-service by automatically calculating and presenting cost estimates based on data characteristics and destination information. It provides users with actionable cost projections and optimization recommendations without requiring manual intervention or complex configuration, maintaining operational simplicity while significantly improving cost efficiency through automated intelligence
Solution Approach 2:
The system provides feedback in the form of real-time cost estimates and optimization recommendations during data egress operations. It continuously monitors actual costs against predictions and offers guidance for cost reduction while maintaining simple user operations, enabling businesses to achieve cost efficiency without sacrificing ease of use
Data Source
AI summary
An embodiment includes computing a computational cost of a job, using a first machine learning algorithm. The job may include an original amount of an egress of data from a cloud computing environment. The embodiment includes determining, using a second machine learning algorithm, the amount of the egress of data corresponding to the job has a computer business criticality that exceeds a threshold level of business criticality. The embodiment includes analyzing a current egress plan used in computing the computational cost of the job. The embodiment includes reconfiguring the current plan to a second egress plan to reduce the computational cost of the job. The embodiment includes implementing a second plan such responsive to execution of the job, the second plan causes data egress behavior to change from the original egress of data behavior. A modified egress behavior causes an effective reduction in the egress cost of the job.