Server Classification Engine for Resource Allocation
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Solution Overview
Problem
Existing network-based computing service providers face challenges in efficiently allocating computer resources, such as virtual machine instances, due to the complexity of evaluating numerous variables and conditions, leading to sub-optimal performance, resource fragmentation, and increased operational load.
Innovation Solution
A server classification engine is employed to intelligently select servers for resource allocation by analyzing previous placements, current server conditions, and operational metrics, using machine-learning techniques to predict successful hosting probabilities and adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource allocation methods are used, then implementation is simple, but resource allocation efficiency is low
Solution Approach 1:
The patent implements feedback mechanisms where allocation decisions are continuously monitored and used to refine future allocations. The system tracks resource usage patterns, allocation success rates, and system state changes, feeding this information back into the allocation algorithm to improve efficiency over time while managing complexity through iterative optimization.
Solution Approach 2:
The allocation system performs self-optimization by automatically analyzing system state and making allocation decisions without requiring manual intervention. The system serves itself by continuously learning from past allocations and adapting its strategy, thereby improving resource allocation efficiency while maintaining operational simplicity for users.
2Measurement precision
If comprehensive variable evaluation is performed, then allocation accuracy is improved, but operational load increases
Solution Approach 1:
The patent segments the comprehensive evaluation process into distinct phases: initial system state assessment, candidate resource identification, detailed suitability evaluation, and final selection. This segmentation allows the system to evaluate multiple variables thoroughly for accuracy while managing operational load by processing evaluations in discrete, manageable steps rather than simultaneously.
Solution Approach 2:
The system performs preliminary actions by pre-evaluating resource characteristics and system state parameters before actual allocation occurs. Historical data and predicted future states are analyzed in advance, allowing the system to make accurate allocation decisions based on pre-computed information, thereby reducing real-time operational load while maintaining high allocation accuracy.
3Productivity
If manual allocation decisions are made, then control precision is high, but productivity is low
Solution Approach 1:
The patent introduces an intermediary allocation system that acts as a mediator between manual control inputs and actual resource allocation. The system translates high-level allocation criteria and constraints into detailed allocation decisions, preserving the control precision of manual input while achieving the allocation speed of automated processing. The intermediary layer interprets operator intent and executes allocations efficiently without requiring direct manual control of each allocation action.
Data Source
AI summary
Various systems, processes, and techniques may be used to allocate computer resources. In particular implementations, systems and processes for allocation of computer resources may include the ability to determine whether a request for allocation of computer resources has been received and determine a set of server computers able to fulfill requirements of the request. The systems and processes may also include the ability to identify one or more server computers in the set likely to successfully provide the computer resources and allocate the requested computer resources on an identified server computer.