Beta Distribution Simulation for Free Pool Capacity Planning
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Solution Overview
Problem
Current capacity planning methods for on-demand computing resources are inaccurate due to reliance on partial or estimated data and fail to account for simultaneous high usage by multiple customers, leading to inefficient resource allocation and potential waste.
Innovation Solution
A computer-implemented process using a beta distribution simulation to model customer resource utilization, iteratively calculating processing engine differentials to optimize the free pool size by adjusting the number of processing engines, ensuring convergence on target utilization levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If capacity is increased by adding engines to meet anticipated needs of each new customer, then customer service requirements are met, but resource waste increases due to inaccurate projections
Solution Approach 1:
The patent applies preliminary action by performing iterative beta distribution simulations before final capacity decisions are made. The system pre-calculates the optimal free pool size by modeling multiple possible utilization scenarios, allowing capacity planners to make informed decisions about engine allocation before customers actually consume resources. This prevents both over-provisioning and under-provisioning of capacity.
Solution Approach 2:
The patent changes parameters by using beta distribution to model customer utilization patterns instead of relying on simple historical averages or static projections. The iterative simulation varies utilization parameters across their probability distributions to determine how much free pool capacity is needed to meet target utilization levels under different scenarios, enabling dynamic and accurate capacity planning.
2Adaptability or versatility
If existing methods add capacity in direct relation to anticipated needs, then individual customer requirements are addressed, but multi-customer simultaneous high usage scenarios are not accounted for
Solution Approach 1:
The patent merges individual customer utilization models into a collective free pool calculation. Instead of planning capacity for each customer separately, the system combines their beta distribution utilization patterns and iteratively simulates their combined impact on the shared free pool. This allows accurate estimation of the free pool size needed to handle multiple customers simultaneously experiencing high usage.
Solution Approach 2:
The patent implements feedback through iterative simulation that continuously refines the free pool estimation. The process calculates processing engine differentials for each iteration, compares results against target utilization levels, and adjusts the free pool size calculation accordingly. This iterative feedback loop continues until convergence, ensuring the final free pool estimate accurately reflects multi-customer scenarios.
3Ease of operation
If partial or estimated data is used for capacity planning projections, then planning can proceed with available information, but projection accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by performing iterative beta distribution simulations before final capacity decisions are made. The system pre-calculates the optimal free pool size by modeling multiple possible utilization scenarios, allowing capacity planners to make informed decisions about engine allocation before customers actually consume resources. This prevents both over-provisioning and under-provisioning of capacity.
Solution Approach 2:
The patent changes parameters by using beta distribution to model customer utilization patterns instead of relying on simple historical averages or static projections. The iterative simulation varies utilization parameters across their probability distributions to determine how much free pool capacity is needed to meet target utilization levels under different scenarios, enabling dynamic and accurate capacity planning.
Data Source
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AI summary
The invention comprises a computer-implemented process for managing computing resources provided to customers in an on-demand data center. The process comprises: providing a shared computing environment; providing to each customer one or more logical partitions of computing resources within the shared computing environment; allocat ing at least one processing engine to each logical partition; modeling a selected customer's resource utilization as a beta distribution; iteratively select ing a random resource utilization value from the beta distribution and, for each logical partition, calculating a processing engine differential; for each iteration, calculating a collective processing engine differential until the collective processing engine differential converges on an optimal processing engine differential; and adjusting the number of processing engines by the optimal processing engine differential to achieve an optimal free pool size.