Disaggregated Cloud Resource Allocation for Flexible Capacity

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Solution Overview

Problem

Cloud computing faces inflexibility in resource configuration and allocation due to pre-defined fixed levels of computing resources, limiting scalability and leading to inefficient resource utilization and increased costs.

Innovation Solution

A disaggregated cloud computing system dynamically allocates hardware resources such as CPUs, memory, and storage to tenants based on their specific workload requirements, SLA agreements, and performance optimization, allowing for on-demand assembly of computer systems with flexible resource configurations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If fixed pre-configured computing resources are used, then resource allocation is simplified, but scalability and adaptability are limited

Engineering Contradiction:

Improveresource configuration flexibilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments computing resources into discrete virtualizable units (CPU cores, memory, storage, network interfaces) that can be independently allocated. This segmentation enables flexible resource configuration by allowing the system to divide and distribute hardware resources across multiple virtual machines and tenants dynamically, resolving the contradiction between adaptability and complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements dynamic resource allocation where computing resources are not fixed but can be adjusted in real-time based on workload demands. The system continuously monitors resource utilization and reconfigures allocations dynamically, enabling the infrastructure to adapt to changing requirements while maintaining manageable complexity through automated control mechanisms.

Inventive Principle:

Principle #15Dynamics

2Productivity

If fixed computing configurations are provided, then infrastructure management is easier, but resource utilization efficiency decreases

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidinfrastructure management ease

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The patent creates a universal resource pool where a single physical infrastructure can serve multiple tenants and workloads simultaneously. The virtualization layer enables the same hardware resources to be shared across diverse applications and users, dramatically improving resource utilization efficiency while the centralized management system maintains operational simplicity through unified control.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent implements self-service capabilities where the system automatically monitors resource usage patterns and dynamically reallocates resources based on actual workload demands without requiring manual intervention. This self-adjusting mechanism improves resource utilization efficiency while maintaining ease of operation through automated decision-making algorithms.

Inventive Principle:

Principle #25Self-service

3Reliability

If over-provisioning is used to ensure capacity, then service reliability is improved, but operational expenses increase

Engineering Contradiction:

Improveservice reliabilityVSAvoidoperational expenses

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent merges multiple resource pools into a unified virtualized infrastructure that serves multiple tenants. By consolidating hardware resources and managing them through a centralized platform, the system achieves economies of scale that reduce operational expenses while maintaining or improving service reliability through efficient resource sharing and load balancing across the combined infrastructure.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent dynamically changes resource allocation parameters based on actual workload conditions and service level agreements. Instead of static over-provisioning, the system adjusts CPU, memory, storage, and network parameters in real-time to match actual demands, ensuring service reliability is maintained only where needed while minimizing operational expenses through precise resource matching.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS10171375B2Constructing computing systems with flexible capacity of resources using disaggregated systems

Publication Date: 2019.01.01 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Various embodiments for allocating resources in a disaggregated cloud computing environment, by a processor device, are provided. Respective members of a pool of hardware resources are assigned to each one of a plurality of tenants based upon a classification of the respective members of the pool of hardware resources. The respective members of the pool of hardware resources are assigned to each one of the plurality of tenants independently of a hardware enclosure in which the respective members of the pool of hardware resources are physically located.