Cloud Algorithm Security Protocol Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud computing systems face challenges in optimizing carbon dioxide emissions and financial costs due to complex interactions between runtime, power consumption, and CO2 emissions, which are difficult to predict using traditional techniques, especially when prioritizing quick application execution or cost-effectiveness in secure environments.
Innovation Solution
The method involves determining calibration parameters and cost values for executing single static assignment statements in a cloud computing environment, mapping these values to CO2 emission values, and using a linear program to minimize total CO2 emissions by optimizing the selection of security protocols such as garbled circuits or homomorphic encryption for each statement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If more processing units are allocated to execute algorithms faster in secure cloud computing environments, then runtime is reduced, but CO2 emissions and financial costs increase
Solution Approach 1:
The algorithm is divided into multiple statements, and each statement is independently assigned to a security protocol (garbled circuits or homomorphic encryption) based on its specific characteristics and cost implications. This segmentation allows optimization of individual statement execution to minimize overall CO2 emissions while maintaining acceptable runtime performance.
Solution Approach 2:
The system dynamically selects between different security protocols (garbled circuits vs. homomorphic encryption) for each statement based on calibrated cost values that reflect CO2 emissions, runtime, and financial costs. This parameter change approach enables the system to adjust protocol selection to optimize the trade-off between speed and environmental impact.
2Loss of time
If more processing units are allocated to execute algorithms faster, then runtime is reduced, but financial costs increase
Solution Approach 1:
The algorithm is divided into multiple statements, and each statement is independently assigned to a security protocol (garbled circuits or homomorphic encryption) based on its specific characteristics and cost implications. This segmentation allows optimization of individual statement execution to minimize overall financial cost while maintaining acceptable runtime performance.
Solution Approach 2:
The system dynamically selects between different security protocols (garbled circuits vs. homomorphic encryption) for each statement based on calibrated cost values that reflect CO2 emissions, runtime, and financial costs. This parameter change approach enables the system to adjust protocol selection to optimize the trade-off between speed and environmental impact.
3Productivity
If traditional optimization techniques are used for cloud computing, then runtime and cost are optimized, but CO2 emissions cannot be accurately predicted or optimized
Solution Approach 1:
The system uses calibrated cost values that are determined through feedback from actual cloud computing environment executions. These calibrated values capture the relationship between protocol selection, runtime, and CO2 emissions, enabling accurate prediction and optimization of emissions while maintaining execution efficiency.
Solution Approach 2:
The system introduces CO2 emission calibration parameters alongside existing runtime and cost parameters. This expands the optimization framework to include environmental factors, enabling simultaneous optimization of productivity and emission prediction accuracy through a unified parameter set that reflects real-world cloud computing behavior.
Data Source
AI summary
Systems and method for deploying CO2 emission and financial cost optimized secured algorithms to cloud computing environments are disclosed. Algorithms are converted into a single state assignment representation that includes a combination of statements that represent sub operations of the algorithm. Runtime and power consumption cost values associated with executing the statements in the cloud are calibrated by executing the statements in a particular configuration of a cloud some number of time with multiple security protocols and then analyzing the results. CO2 emission and financial cost values are mapped to the calibrated runtime and power consumption cost values. The mapped CO2 emission and financial cost values and the calibrated runtime and power consumption cost values are used by a linear program to optimize a partitioning vector of indicators that define which security protocol will be used to execute each statement in the cloud when the algorithm is deployed.