Distributed Data Center Management System for Resource Re-allocation
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Solution Overview
Problem
Traditional data centers are limited by the resource scale of a single entity server, making real-time and rapid resource re-allocation difficult, leading to inefficiencies and waste.
Innovation Solution
A data center management system is developed, incorporating a cloud platform with a calculation resource pool and shared database, allowing for distributed calculation between processing nodes and efficient resource allocation across multiple servers, eliminating the need for a master control server and enhancing calculation capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple virtual machines share the performance of one entity server, then resource consolidation is achieved, but real-time and rapid resource re-allocation becomes difficult and resource idleness occurs
Solution Approach 1:
The patent segments the traditional monolithic entity server into multiple processing nodes that form a distributed computing cluster. Each node can independently process tasks and resources are divided into smaller allocable units, enabling fine-grained resource allocation and rapid reconfiguration without affecting the entire system.
Solution Approach 2:
The system implements dynamic resource allocation where processing nodes can be added, removed, or reconfigured in real-time based on workload demands. The resource pool is not static but dynamically adjusts to application needs, allowing rapid re-allocation of computational resources without fixed assignments.
2Device complexity
If a traditional data center uses a single entity server, then system simplicity is maintained, but calculation efficiency and resource utilization are limited
Solution Approach 1:
The patent merges multiple processing nodes into a unified virtual resource pool that appears as a single logical entity to users. This combination of multiple physical servers into one virtualized resource pool maintains simplicity from the user perspective while achieving enhanced calculation efficiency through distributed processing.
Solution Approach 2:
The processing nodes are designed with universal functionality, where each node can perform multiple types of computational tasks and serve different applications. This multi-functionality allows the same hardware infrastructure to handle diverse workloads, improving overall resource utilization and calculation efficiency.
Data Source
AI summary
Provided is a data center management system including a data center, a cloud platform and an application platform. The application platform is configured to perform external network access through an application interface layer and send a calculation request to the data center. The data center includes a storage resource pool configured to perform distributed storage of files, and a network resource pool configured to send a scheduling request to the cloud platform according to the calculation request, to schedule the cloud platform. The cloud platform includes a calculation resource pool configured to perform a distributed calculation between adjacent processing nodes according to a received scheduling request, and call files in the storage resource pool, or a calculation resource in a shared database and external shared data in the shared database, and the shared database configured to collect and store the calculation resource and the external shared data.