Modular Computing Containers for Rapid Data Center Scaling
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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
1Productivity
If traditional data centers are used to scale computing power and storage capacity, then data processing and storage capabilities are improved, but deployment time and system complexity increase
Solution Approach 1:
The data center system is divided into independent modular computing environments (containers) that can be manufactured, tested, and deployed separately. Each container encapsulates complete computing stacks including servers, storage, networking, and cooling, allowing parallel development and rapid deployment without coordinating complex interdependencies
Solution Approach 2:
Computing stacks are pre-assembled, pre-tested, and pre-configured within containers before deployment. The modular units come ready-to-run with all components integrated and validated, eliminating on-site assembly and configuration time. Containers can be staged and prepared in advance while infrastructure work proceeds independently
2Productivity
If traditional data centers are used to handle high network traffic, then data processing capability is improved, but infrastructure complexity and cost increase
Solution Approach 1:
The modular computing containers are designed as universal, standardized units that can be deployed in various configurations to meet different computing demands. Each container provides a complete, self-contained computing environment with standardized interfaces for power, networking, and cooling, reducing the need for custom infrastructure designs
Solution Approach 2:
Computing stacks are nested within container enclosures, which themselves nest within larger data center infrastructure. The hierarchical nesting allows standardized containers to be systematically integrated into existing facilities, simplifying infrastructure management through consistent, repeatable deployment patterns
3Productivity
If computing power is scaled up to meet high-demand applications, then processing capacity is improved, but cooling requirements and energy consumption increase
Solution Approach 1:
The modular containers integrate computing hardware, cooling systems, and power distribution into unified, co-optimized units. By merging these subsystems at the container level, the design enables efficient heat removal closer to heat sources and reduces energy losses associated with separate, distributed cooling infrastructure
Solution Approach 2:
The container enclosure serves as an intermediary thermal management zone between hot computing components and the external cooling environment. This intermediate structure provides controlled thermal pathways and heat exchanger interfaces that improve cooling efficiency while isolating the computing stacks from direct environmental exposure
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 computing power and storage capacity, facilitating efficient processing and storage of large data volumes, and supports high-bandwidth network interfaces, thus addressing the need for scalable and reliable data processing in high-demand 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.