Host Cluster Assignment for Virtual Endpoint Workloads
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Solution Overview
Problem
As the number of hosts increases in a computing environment, it becomes difficult to assign virtual endpoints to hosts that provide the desired physical configuration, leading to inefficient resource allocation and potential performance issues.
Innovation Solution
The method involves identifying a host to be added to a computing environment, determining its physical resources, and assigning it to a host cluster based on those resources. Additionally, virtual machines can be migrated to optimize resource utilization across clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of hosts increases in a computing environment, then resource capacity and scalability are improved, but host assignment complexity and management difficulty increase
Solution Approach 1:
The patent segments hosts into distinct clusters based on their physical resource configurations (e.g., high-memory hosts, high-CPU hosts, storage-optimized hosts). Each cluster is characterized by promoted physical resources that define its specialization. This segmentation automates the assignment process by matching virtual endpoint requirements to appropriate cluster types, thereby managing complexity despite increasing host quantities.
Solution Approach 2:
The patent utilizes parameter changes by defining clusters through promoted physical resource parameters (memory capacity, CPU power, storage type). When hosts are added to the environment, the system evaluates their physical resource parameters and automatically assigns them to clusters whose promoted parameters match the host's capabilities, enabling scalable and automated host management.
2Adaptability or versatility
If hosts with different physical configurations are employed, then versatility and workload specialization are improved, but assignment difficulty and configuration management increase
Solution Approach 1:
The patent applies local quality by creating clusters with specific promoted physical resources tailored to particular workload requirements. For example, certain clusters promote memory resources for database workloads, while others promote CPU resources for compute-intensive tasks. This localized optimization allows versatile workload support while simplifying assignment through clear matching criteria between virtual endpoint needs and cluster specializations.
Solution Approach 2:
The system enables self-service automation where newly added hosts automatically evaluate their own physical resource characteristics and are assigned to appropriate clusters without manual intervention. The management system performs automated evaluation of host configurations and assigns hosts to clusters based on matched promoted resources, reducing assignment difficulty while maintaining versatile workload support.
3Measurement precision
If manual host assignment is used, then control precision is maintained, but time consumption and operational efficiency decrease
Solution Approach 1:
The patent implements preliminary action by pre-defining cluster types with specific promoted physical resources that correspond to different workload requirements. When hosts are added or virtual endpoints need assignment, the system can quickly match requirements to pre-established cluster categories, maintaining precise host selection while dramatically reducing assignment time compared to manual evaluation and selection processes.
Data Source
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AI summary
Described herein are systems, methods, and software to manage the assignment of hosts to host clusters and the assignment of virtual endpoints to the host clusters. In one implementation, a management service identifies a host to be added to a computing environment and identifies physical resources available on the host. The management service further determines a host cluster for the host from a plurality of host clusters in the computing environment based on the physical resources available on the host and assign the host to the host cluster.