Containerized Computing Hub for Rapid Data Center Expansion
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Solution Overview
Problem
Current data centers face challenges in efficiently scaling computing power and storage capacity to handle high network traffic and simultaneous data processing demands, particularly in applications like financial transactions and search engine services, where large volumes of data need to be processed and stored quickly and reliably.
Innovation Solution
A modular data center system comprising a connecting hub with docking regions providing electrical power, data network interfaces, and cooling fluid supply and return, along with shipping containers that house modular computing environments. Each container includes processing units, heat exchangers for cooling, and docking members for easy connection to the hub, allowing incremental addition of computing power and storage capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If traditional data centers are expanded to increase computing power and storage capacity, then the system can handle high network traffic and data processing demands, but the deployment time and system complexity increase significantly
Solution Approach 1:
The data center is divided into modular computing environments (containers) that can be independently manufactured, tested, and deployed. Each container encapsulates complete computing clusters with standardized interfaces, allowing parallel assembly and rapid deployment without increasing overall system complexity
Solution Approach 2:
Complete computing clusters are pre-assembled, pre-configured, and pre-tested within standardized containers before deployment. This preliminary preparation includes installing processing units, storage devices, networking equipment, and cooling systems in factory settings, enabling quick plug-and-play deployment at the data center site
2Productivity
If more computing devices are added to handle high network traffic, then data processing capacity increases, but the system complexity and difficulty of integration increase
Solution Approach 1:
Standardized docking interfaces are designed to universally support multiple functions including power delivery, data networking, cooling fluid connection, and signaling. This universal interface approach allows any computing cluster to be integrated into the data center without custom integration work, maintaining low system complexity while enabling high data processing capacity
Solution Approach 2:
Multiple computing functions (processing, storage, networking, cooling, power distribution) are merged into integrated computing clusters within containers. This consolidation reduces the number of separate components and interfaces that need to be managed, simplifying system integration while increasing overall data processing capacity
3Power
If computing clusters are integrated into existing data centers, then computing power is added, but the integration process becomes complex and time-consuming
Solution Approach 1:
Computing clusters are pre-integrated with all necessary connections (power, networking, cooling) configured within standardized containers before arrival at the data center. This preliminary integration eliminates on-site configuration complexity and enables simple plug-and-play deployment by docking containers with the data center infrastructure
Solution Approach 2:
Standardized docking interfaces provide universal connectivity for power, data, and cooling across all computing clusters. This standardization enables seamless integration of new clusters into existing data centers without complex custom integration procedures, maintaining ease of operation while adding computing power
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid deployment and expansion of data centers with high computing power and storage, efficient heat management, and the ability to handle large volumes of simultaneous data processing and storage, improving performance and reliability in demanding applications.
Implementation Method 1
a heat exchanger configured to remove heat generated by the plurality of processing units by circulating cooling fluid from the supply through the heat exchanger and discharging it into the return
Data Source
AI summary
A computer system may include a connecting hub having a plurality of docking regions and be configured to provide to each docking region electrical power, a data network interface, a cooling fluid supply and a cooling fluid return; and a plurality of shipping containers that each enclose a modular computing environment that incrementally adds computing power to the system. Each shipping container may include a) a plurality of processing units coupled to the data network interface, each of which include a microprocessor; b) a heat exchanger configured to remove heat generated by the plurality of processing units by circulating cooling fluid from the supply through the heat exchanger and discharging it into the return; and c) docking members configured to releaseably couple to the connecting hub at one of the docking regions to receive electrical power, connect to the data network interface, and receive and discharge cooling fluid.