Bulk Request Parameter Adjustment for Cloud Resource Optimization
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Solution Overview
Problem
Cloud service providers face challenges in optimizing resource usage due to bulk requests, which can lead to suboptimal resource allocation and underutilization of resources, as these requests often impose constraints on individual unit parameters, deterring ad-hoc requests and resulting in inefficient loading conditions.
Innovation Solution
A system executes an iterative process to determine an optimal bulk request parameter-value by predicting baseline benefit values and adjusting unit parameter-values, allowing for automatic optimization of bulk request parameters to improve resource usage and increase ad-hoc requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bulk requests reserve a group of units during a selected timeframe, then resource allocation for bulk requests is improved, but resource utilization deteriorates due to underutilization and deterred ad-hoc requests
Solution Approach 1:
The patent applies dynamics by making the bulk request parameter-value adjustable and iterative. The system dynamically modifies the parameter-value based on predicted ad-hoc requests and resource usage patterns, transitioning from a static allocation to a flexible, adaptive allocation that optimizes resource utilization while maintaining bulk request commitments.
Solution Approach 2:
The patent directly applies parameter changes by modifying the bulk request parameter-value (such as price, discount, or resource guarantee level) to balance bulk request satisfaction with ad-hoc request acceptance. The iterative process adjusts this parameter to find the optimal value that maximizes overall resource utilization.
2Reliability
If bulk requests impose constraints on individual unit parameters, then bulk request satisfaction is improved, but ad-hoc requests are deterred resulting in inefficient loading conditions
Solution Approach 1:
The system dynamically adjusts the bulk request parameter-value to balance constraints. By iteratively modifying this value based on predicted ad-hoc request volume and resource usage, the system maintains flexibility to accommodate both bulk and ad-hoc requests without overly restrictive constraints.
Solution Approach 2:
The patent applies partial action by implementing selective constraints on bulk requests. Rather than imposing uniform strict constraints on all bulk requests, the system adjusts the parameter-value to apply only the necessary level of constraint, allowing maximum flexibility for ad-hoc requests while still satisfying bulk request commitments.
3Productivity
If manual monitoring and adjustment of bulk request parameters is performed, then resource optimization can be achieved, but operational complexity and time consumption increase
Solution Approach 1:
The patent applies self-service by implementing an automated iterative process that independently monitors predicted ad-hoc requests and resource usage, then automatically adjusts the bulk request parameter-value without manual intervention. The system serves itself by making real-time optimizations based on incoming data.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring predicted ad-hoc requests and resource usage patterns, then using this feedback to iteratively adjust the bulk request parameter-value. This closed-loop control enables automatic optimization without manual time investment.
4Reliability
If the bulk request parameter-value is set to maximize bulk request benefits, then bulk request allocation is improved, but overall resource usage efficiency deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the bulk request parameter-value from a fixed optimization target to a dynamically adjusted value. The iterative process modifies this parameter based on predicted ad-hoc requests and actual resource usage, finding the optimal balance point that maximizes overall efficiency rather than just bulk request benefits.
Solution Approach 2:
The system transitions from static parameter setting to dynamic adjustment. By continuously monitoring resource usage patterns and ad-hoc request predictions, the system adapts the bulk request parameter-value in real-time to maintain optimal resource usage efficiency while still satisfying bulk request commitments.
Data Source
AI summary
Some examples describes herein relate to handling bulk requests for resources. In one example, a system can determine a bulk request parameter-value associated with a bulk request. The system can then predict a baseline benefit value, which can be a benefit value when the bulk request parameter-value is used as a lower boundary for a unit parameter-value. The system can also determine a lower boundary constraint on the unit parameter-value independently of the bulk request parameter-value. The system can then execute an iterative process using the baseline benefit value and the lower boundary constraint. Based on a result of the iterative process, the system can determine whether and how much the bulk request parameter-value should be adjusted. The system may adjust the bulk request parameter-value accordingly or output a recommendation to do so.