Automated Resource Allocation Optimization for Data Centers
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Solution Overview
Problem
Conventional methods for allocating computing resources in distributed environments, such as server racks and data centers, are inefficient and time-consuming, as they require manual effort and cannot accurately assess the vast number of possible allocations to achieve optimal operation.
Innovation Solution
An automated system translates real-world parameters into optimization functions using constraint-translation mechanisms and solver mechanisms, such as ECOS or XPRESS, to determine optimal resource placement across available structures, balancing constraints like power, cooling, and network capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual resource allocation is used, then human engineers can understand and control the allocation process, but the time required increases to days or weeks and optimal allocations cannot be assessed
Solution Approach 1:
The patent replaces manual human computation and mechanical allocation processes with automated computer-based optimization systems. The system uses software algorithms to evaluate vast numbers of possible resource allocations, power configurations, and cooling requirements, substituting human engineers' manual work with automated computational mechanisms that can process and assess optimal allocations rapidly without human intervention.
2Productivity
If automated optimization systems are used, then allocation time is reduced to a day or less, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary optimization system that acts as a bridge between the complex constraints (power, cooling, network capacity) and the resource allocation decisions. This intermediary software layer translates real-world parameters into optimization functions, manages the complexity of evaluating vast allocation possibilities, and presents simplified results to users, thereby enabling fast automated allocation while managing system complexity through abstraction.
3Reliability
If comprehensive constraints are considered (power, cooling, network capacity), then optimal operation is achieved, but the computational complexity increases beyond human capability
Solution Approach 1:
The patent transforms real-world physical constraints (power requirements, cooling capacity, network bandwidth) into standardized optimization parameters and mathematical functions. By changing the representation of these constraints from physical domain to computational domain, the system enables automated algorithms to efficiently evaluate and balance multiple competing constraints simultaneously, achieving comprehensive constraint compliance through parameter transformation rather than direct physical manipulation.
Data Source
AI summary
A computing system operates according to a method including: processing representations of housing structures with open locations for physically locating computing resources, a physical layout of the open locations, and characteristics of the structures and the resources to generate designated locations for optimally placing or allocating the computing resources in the open locations. The designated locations are generated based on analyzing multiple possible allocation or placement combinations of the computing resources into the open locations as an optimization function.