Cloud-Native Reservation Manager for Server Cluster Resource Allocation
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Solution Overview
Problem
In cloud native environments with multiple server clusters, efficiently managing resource reservations and allocations across shared resources is challenging due to varying resource requirements and priority levels, leading to suboptimal application deployment and execution.
Innovation Solution
A method that automatically receives reservation requests, identifies qualified server clusters based on resource requirements and priority levels, pre-empts or migrates existing reservations to ensure optimal resource allocation, and assigns the request to the most suitable server cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If resources are shared among multiple applications across server clusters, then resource utilization efficiency is improved, but resource allocation complexity and management difficulty increase
Solution Approach 1:
The patent segments the resource management system into multiple independent server clusters, each with its own resource pool. The reservation manager divides the overall resource allocation task into cluster-level allocations, where each cluster independently manages its resources. This segmentation reduces the complexity of global resource management while maintaining high utilization through local optimization.
Solution Approach 2:
The patent implements preliminary action by establishing reservation mechanisms before applications actually need resources. The reservation manager pre-allocates resource quotas to applications based on predicted needs and priority levels. This advance preparation simplifies real-time allocation decisions and improves resource utilization by preventing resource contention during actual execution.
2Reliability
If reservation requests are processed with strict priority levels, then application performance for high-priority applications is improved, but resource allocation flexibility and adaptability decrease
Solution Approach 1:
The patent applies dynamics by making the priority level configuration adjustable and adaptable. While priority levels provide structured resource allocation, the system allows dynamic modification of priority assignments based on changing application needs and resource availability. The reservation manager can adapt resource distribution in real-time, balancing the need for reliable performance with allocation flexibility.
Solution Approach 2:
The patent utilizes parameter changes by allowing the reservation manager to adjust resource allocation parameters such as quota amounts, priority weights, and cluster selection criteria. These parameters can be modified based on resource availability, application performance metrics, and changing business requirements, enabling the system to maintain both reliability and adaptability.
3Productivity
If multiple server clusters are evaluated for reservation assignment, then optimal resource allocation is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent implements partial action by evaluating only a subset of server clusters for each reservation request rather than all available clusters. The reservation manager uses filtering criteria such as resource availability, geographic proximity, and application requirements to narrow down the candidate cluster set. This partial evaluation maintains optimal allocation quality while significantly reducing processing time compared to exhaustive evaluation of all clusters.
4Adaptability or versatility
If existing reservations are pre-empted or migrated to accommodate new requests, then resource allocation adaptability is improved, but system stability and operational complexity increase
Solution Approach 1:
The patent applies preliminary anti-action by implementing compensation mechanisms before pre-emption or migration occurs. When existing reservations need to be adjusted to accommodate new high-priority requests, the system first provisions alternative resources or creates migration plans to minimize disruption. This preliminary preparation reduces the negative impact on system stability while maintaining the necessary adaptability for dynamic resource allocation.
Data Source
AI summary
A method includes receiving a reservation request corresponding to resource requirements of an application and including role-based access and first priority level indicators. A reservation subset of existing reservations is identified based on the role-based access indicator, and server clusters are evaluated based on the resource requirements, the first priority level indicator, the reservation subset, and server cluster resources. When the server clusters are determined to include at least one qualified server cluster, a first server cluster is identified either as a sole qualified server cluster or by ranking multiple qualified server clusters by calculating a score of each qualified server cluster, an existing reservation of the reservation subset assigned to the first server cluster is pre-empted based on the first priority level indicator relative to priority level indicators of the one or more existing reservations, and the reservation request is assigned to the first server cluster.