Cloud Transaction Classing for Quality-Safe Idle Resource Reduction
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Solution Overview
Problem
Existing approaches to optimize cloud spending and resource utilization in cloud computing systems lack user perspective, leading to inefficient idle time and resource consumption, particularly during low and medium loads, which increases costs without generating additional transactional value.
Innovation Solution
A system and method that analyzes transaction logs to identify transaction classes based on behavioral and time attributes, correlates resource consumption and idle time data with these classes, and adjusts configuration data to optimize resource utilization and minimize idle time while maintaining user technical quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud resources remain active during low and medium loads, then user technical quality (reactivity, throughput, stability) is maintained, but resource consumption and costs increase due to idle time
Solution Approach 1:
The system dynamically adjusts resource allocation based on transaction load analysis. Configuration data is automatically generated to scale resources up during high demand and down during low demand periods, making the resource allocation flexible rather than static. This resolves the contradiction by allowing resources to be active when needed for reliability and inactive when not needed to reduce energy loss.
Solution Approach 2:
The system changes operational parameters (resource allocation levels, configuration data) based on analyzed transaction patterns and load conditions. By monitoring transaction logs and identifying optimal timeslots, the system adjusts resource consumption parameters to match actual demand, reducing idle time energy consumption while maintaining reliability during critical periods.
2Loss of energy
If cloud resources are reduced during idle time, then energy consumption is minimized, but user technical quality may be compromised
Solution Approach 1:
The system continuously monitors transaction logs and user technical quality metrics, using this feedback to adjust resource allocation. The analysis unit processes transaction data to identify patterns, and the configuration automatically adjusts resource levels based on this feedback while ensuring user quality remains within acceptable ranges. This closed-loop control prevents quality compromise while minimizing energy consumption.
Solution Approach 2:
The system performs preliminary analysis of transaction logs to predict future load patterns and proactively adjusts resource allocation before demand changes occur. By identifying transaction classes and optimal timeslots in advance, the system prepares configuration data that prevents both over-provisioning (wasting energy) and under-provisioning (compromising quality).
3Productivity
If transaction logs are analyzed and configuration data is automatically generated, then resource utilization is optimized and idle time is reduced, but system complexity increases
Solution Approach 1:
The system performs self-configuration by automatically analyzing its own transaction logs and generating optimization rules without requiring external intervention. The analysis unit processes internal transaction data, and the system autonomously generates configuration data to optimize resource allocation. This self-service approach improves productivity while limiting complexity growth, as the system manages its own complexity through automation rather than manual configuration.
Data Source
AI summary
A method, including: analyzing transaction logs of the cloud computing system within a predefined period of time to derive various transaction types with pre-defined behavioral and time attributes of the logged operations of transaction; forming at least one transaction class by clustering the derived transaction types with similar values of the behavioral and/or time attributes into the same transaction class; correlating predefined resource consumption data and/or idle time data with at least one transaction class; verifying that user related technical quality of one or more such operations of transaction is in a predetermined acceptable tolerance range; providing configuration data containing the at least one transaction class with the correlated resource consumption data and/or idle time data for the cloud computing system, if the verified user related technical quality is in the predetermined acceptable tolerance range, otherwise predefined resource consumption data and/or idle time data need to be redefined.