Predictive Cloud Resource Migration for Cost and Carbon Efficiency
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Solution Overview
Problem
Cloud architectures are designed based on static resource availability at the time of design, leading to inefficiencies in cost-effectiveness and sustainability, particularly in managing cloud resources.
Innovation Solution
A method using mathematical models to estimate resource requirements and availability, allowing workload migration to different computing resources based on historical usage patterns and carbon footprint considerations, optimizing resource utilization and sustainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If cloud architecture is designed based on static resource availability at the time of design, then the architecture is simple and easy to implement, but it leads to inefficient cost-effectiveness and sustainability
Solution Approach 1:
The patent applies dynamics by transitioning from static architecture design to dynamic workload migration. The system continuously monitors workload characteristics and resource availability, then migrates workloads between compute instances based on real-time conditions. This dynamic approach optimizes cost-effectiveness and sustainability while maintaining architectural flexibility through automated migration mechanisms.
Solution Approach 2:
The patent utilizes parameter changes by modifying workload placement decisions based on changing resource parameters such as availability, cost, and sustainability metrics. The system adjusts migration timing and target selection by monitoring parameters like resource utilization patterns, pricing variations, and environmental factors, enabling adaptive optimization of cloud resource allocation.
2Device complexity
If static architecture is used based on current resources, then the system is simple to design, but it cannot account for future usage and leads to inefficient resource management
Solution Approach 1:
The patent applies preliminary action by analyzing historical workload data and predicting future resource requirements before migration occurs. The system proactively identifies optimal migration timing and target instances based on forecasted patterns, preparing migration strategies in advance rather than reacting to current conditions alone. This enables efficient resource management while keeping the architecture relatively simple through automated prediction and planning.
3Productivity
If workload migration is implemented to optimize resources, then cost-effectiveness and sustainability improve, but system complexity increases
Solution Approach 1:
The patent applies self-service by implementing automated workload migration mechanisms that operate without manual intervention. The system autonomously monitors resource conditions, evaluates migration candidates, executes migrations based on predefined policies, and manages the entire optimization process automatically. This self-service approach achieves cost-effectiveness and sustainability improvements while minimizing the operational complexity burden on users.
Solution Approach 2:
The patent utilizes feedback by continuously monitoring workload performance, resource availability, and migration outcomes to refine future migration decisions. The system learns from historical migration data and adjusts its algorithms and policies based on actual results, creating an iterative optimization loop that improves cost-effectiveness and sustainability while managing complexity through automated learning and adaptation.
Data Source
AI summary
A method for managing computing resources for workload is provided. The method includes estimating, by one or more processors using a first mathematical model, a time period indicative of a minimum requirement of the computing resources for the workload. Further, the method includes determining, by the one or more processors using a second mathematical model, a probability of availability of a first type of computing resources during the estimated time period of the minimum requirement. Further, the method includes comparing the determined probability of availability of the first type of computing resources with an availability threshold. Furthermore, the method includes migrating the workload to the first type of computing resources at the estimated time period upon determining that the determined probability of availability is meeting the availability threshold.