Multi-Cloud Budget Control With Adaptive Workload Scaling
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Solution Overview
Problem
Existing cloud server management systems lack real-time budget monitoring and adaptive control mechanisms, leading to potential overspending, fixed and inflexible budget thresholds, and difficulty in implementing unified budget strategies across disparate providers.
Innovation Solution
A multi-cloud budget control system (BCS) that continuously monitors cloud server usage, applies customizable multi-threshold strategies, and dynamically adjusts operations to prevent overspending by scaling workloads and user access based on historical data and real-time spending patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time budget monitoring and adaptive control mechanisms are implemented, then spending control and operational efficiency are improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring budget templates with multiple spending thresholds and associated actions before actual spending occurs. The budget template component stores predefined thresholds and actions, so when spending reaches certain levels, the appropriate actions are automatically triggered without requiring real-time complex decision-making, thus improving spending control while managing system complexity.
Solution Approach 2:
The budget control system implements self-service mechanisms where the system automatically monitors spending, compares it against thresholds, and executes predefined actions without human intervention. The processor automatically determines when spending correlates to threshold levels and implements the corresponding actions from the budget template, reducing the need for complex manual oversight while maintaining reliable spending control.
2Ease of manufacture
If fixed budget thresholds are used, then implementation simplicity is improved, but adaptability to changing spending patterns deteriorates
Solution Approach 1:
The system applies dynamics by allowing budget templates to be dynamically created, modified, and updated based on changing spending patterns and requirements. While individual threshold-action pairs remain fixed for simplicity, the entire budget template can be adapted over time. The system supports multiple thresholds within a single template, enabling it to adapt to different spending scenarios while maintaining the simplicity of fixed threshold comparisons.
Solution Approach 2:
The system enables parameter changes by allowing the budget template parameters (thresholds, actions, spending limits) to be modified based on historical data analysis and changing organizational needs. The template can be reconfigured with different threshold levels and associated actions, providing adaptability to new spending patterns while maintaining the structured simplicity of predefined threshold-based control.
3Measurement precision
If multi-threshold strategies are applied, then spending control precision is improved, but processing time increases
Solution Approach 1:
The system applies segmentation by dividing the budget control into multiple discrete threshold levels, each with specific spending ranges and associated actions. Instead of using a single complex continuous control mechanism, the budget template segments spending control into manageable discrete thresholds. This allows precise control at each spending level while keeping processing simple by comparing actual spending against predefined threshold values rather than performing complex continuous optimization.
Data Source
AI summary
Operation of various data servers included in a cloud data server are monitored and controlled to prevent a client from exceeding their available budget for hosting their workloads at the cloud server. Business rules and associated actions can be configured regarding operational cost/usage of hosting client workloads versus an available budget. As a cost approaches or exceeds a defined budget, respective operations and number of available data servers can be auto-scaled/throttled to prevent the client from exceeding the budget while attempting to maintain operation of as many workloads as possible. An operational budget, and also operational spend, throughout a given budget cycle can be predicted. A current cost/usage can be compared with the predicted budget/usage and a current operation of the cloud server can adjusted based on similarity/dissimilarity between the current cost/usage and the predicted budget/usage.