Cluster-Based Resource Pools for Cloud Workload Allocation

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Solution Overview

Problem

Current resource management systems in cloud computing face challenges in efficiently allocating and managing resources across distributed clusters, particularly in determining optimal resource pool separation and ensuring resilience and high availability, especially when handling varying customer demands and workload distribution.

Innovation Solution

The implementation of a resource management system that uses best fit/chunking algorithms, IU-based service provision, tolerance and ghost processing, and dynamic/distributed management services with monitoring and decision processes to assess customer resource needs, allocate virtual machines, and manage resource distribution across clusters, ensuring efficient resource utilization and resilience.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If resources are allocated across distributed clusters to handle varying customer demands, then resource utilization improves, but system complexity increases

Engineering Contradiction:

Improveresource utilizationVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments resources into cluster-based resource pools, where each pool is associated with specific customers or workload types. This segmentation allows independent management and allocation strategies for different resource pools, improving utilization without overwhelming system complexity through standardized pool management interfaces.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A resource management system acts as an intermediary layer between physical infrastructure and customer workloads. This intermediary handles allocation decisions, monitoring, and coordination across clusters, abstracting the complexity from both infrastructure management and customer access while optimizing resource utilization.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If resource pool separation is optimized for high availability, then service reliability improves, but resource allocation flexibility decreases

Engineering Contradiction:

Improveservice availabilityVSAvoidallocation flexibility

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system implements dynamic resource pool separation where allocation boundaries and constraints can be adjusted based on current system state, customer priorities, and failure scenarios. This allows the system to maintain high availability through structured separation while adapting allocation flexibility in response to changing conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes allocation parameters dynamically, adjusting resource pool assignments, separation constraints, and allocation rules based on service level requirements, customer demands, and system health. This enables maintenance of reliability guarantees while preserving flexibility through parameter adjustment rather than fixed structural constraints.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If monitoring and decision processes are implemented for dynamic management, then service level guarantees improve, but processing overhead increases

Engineering Contradiction:

Improveservice level guaranteeVSAvoidprocessing overhead

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system implements feedback-based monitoring where resource usage, system state, and service level compliance are continuously measured and fed back to allocation decisions. This feedback mechanism ensures service level guarantees are maintained while optimizing processing overhead by adjusting monitoring intensity and decision frequency based on system conditions.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The monitoring and decision processes are distributed across the system infrastructure itself, with components autonomously making allocation decisions based on local state and predefined policies. This self-service approach reduces centralized processing overhead while maintaining service level guarantees through decentralized intelligence.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11226846B2Systems and methods of host-aware resource management involving cluster-based resource pools

Publication Date: 2022.01.18 EMC IP HLDG CO LLC

AI summary

Systems and methods are disclosed for managing resources associated with cluster-based resource pool(s). According to illustrative implementations, innovations herein may include or involve one or more of best fit algorithms, infrastructure based service provision, tolerance and/or ghost processing features, dynamic management service having monitoring and/or decision process features, as well as virtual machine and resource distribution features.