Intelligent Cloud Operations Software Migration
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Solution Overview
Problem
The increasing complexity and size of software solutions driven by trends like Big Data, IoT, ML, and AI lead to higher operational costs and a larger carbon footprint, necessitating efficient management and resource optimization in cloud computing environments.
Innovation Solution
Implementing intelligent control over software programs hosted by cloud providers, using operational and experiential data to evaluate resource consumption and user interaction quality, and issuing commands to optimize resource usage, such as moving software to lower-cost providers or adjusting resource allocation based on demand, security, and compliance considerations, leveraging both static rulesets and self-learning neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software solutions expand to accommodate Big Data, IoT, ML, and AI trends, then functionality and capability are improved, but operational costs and carbon footprint increase
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring operational data and experiential data to adjust infrastructure resource consumption in real-time. The system dynamically provisions and deprovisions resources based on actual usage patterns, allowing the software solution to adapt its resource requirements rather than maintaining fixed allocations, thereby reducing operational costs while preserving functionality.
Solution Approach 2:
The system changes operational parameters by analyzing metrics such as user interaction quality, resource consumption rates, and usage patterns to optimize infrastructure configuration. By modifying parameters like resource allocation thresholds, provisioning schedules, and consumption limits based on measured performance, the system achieves cost optimization without compromising software capabilities.
2Power
If cloud computing resources are increased to support larger software systems, then computational capability is improved, but carbon footprint increases
Solution Approach 1:
The system implements periodic monitoring and evaluation cycles where operational data is collected, analyzed, and used to adjust resource allocation at regular intervals. This periodic action allows the system to optimize resource usage over time, ensuring that computational capabilities are maintained only when needed, thereby reducing overall energy consumption and carbon footprint.
Solution Approach 2:
The patent employs feedback mechanisms where experiential data from user interactions and operational data from infrastructure consumption are continuously fed back into the system to refine resource allocation decisions. This feedback loop enables the system to adjust computational resource usage based on actual performance and user needs, preventing wasted energy on unused capabilities while maintaining necessary computational power.
3Productivity
If infrastructure resources are optimized to reduce costs, then operational efficiency is improved, but service availability may be compromised
Solution Approach 1:
The system performs preliminary actions by analyzing operational data and experiential data in advance to predict when resource optimization might impact service availability. By proactively adjusting resource allocation based on usage patterns and performance trends, the system can maintain service levels while reducing costs, rather than reacting to availability issues after they occur.
Solution Approach 2:
The patent implements self-service mechanisms where the system automatically monitors its own performance and adjusts resource allocation without external intervention. The system self-evaluates operational efficiency and service availability metrics, making autonomous decisions to optimize resources while maintaining service levels, thereby eliminating the trade-off between efficiency and reliability.
4Use of energy by moving object
If software is migrated to lower-cost host providers, then operational cost is reduced, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the software system into modular components that can be independently migrated between host providers. This modular architecture allows the system to migrate only specific services or functions to lower-cost providers while maintaining others on current infrastructure, reducing the overall complexity of migration operations and enabling incremental cost optimization.
Data Source
AI summary
Embodiments control software hosted by cloud providers and private clouds. Operational data received from the software correlates: •a time, •a user at the time, and •a software component accessed by the user at the time. The operational data relates to resources (e.g., storage, computational, network) that are consumed. Experiential data characterizing a quality of user interaction, is also received. The experiential data may be derived from operational data (e.g., inferred from time lags), or received separately as feedback. Operational data and experiential data are processed according to a ruleset or a neural network. Based upon the result, which optimizes towards costs, experience data (e.g., time lags) or policies (e.g. security constraints), a command causes an actuator to act upon the software. The actuator may trigger movement to a different host (e.g. different cloud provider). The move functionality, if applied to many customers at once, embodies a cloud provider switch service.