Data Center Routing Prioritizing Local Transfers
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Solution Overview
Problem
There is a need to minimize data transfer between servers of different data centers due to the high costs associated with transferring large volumes of media data, such as audio or video telephony data.
Innovation Solution
A method and system for managing data transfer between data centers by automatically calculating data transfer demand and capacity, prioritizing data transfer within a single data center over inter-data center transfers, and dynamically adapting to server issues such as failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data transfer between different data centers is performed, then data availability and service continuity are improved, but data transfer costs increase
Solution Approach 1:
The system segments data transfer operations by identifying local versus remote data centers, and further segments transfer priorities by maintaining buffer capacity thresholds. This allows differentiated handling of data transfers based on destination and urgency, reducing unnecessary remote transfers while maintaining reliability.
Solution Approach 2:
The system performs preliminary identification of data transfer capacity and demand for each server before actual transfers occur. By calculating data transfer distribution in advance and maintaining buffer capacities, the system prepares optimal transfer routes beforehand, preventing costly last-minute remote transfers.
2Productivity
If data transfer capacity is increased for servers, then data transfer demand can be better met, but infrastructure costs increase
Solution Approach 1:
The system dynamically calculates data transfer capacity and demand for each server based on current conditions rather than using fixed allocations. This allows flexible adjustment of capacity utilization across the network, maximizing existing infrastructure efficiency without requiring additional resources.
Solution Approach 2:
The system changes operational parameters by maintaining buffer capacity at specific thresholds (e.g., 50% of total capacity) and adjusting data transfer distribution based on real-time capacity and demand identification. This optimizes resource utilization within existing infrastructure constraints.
3Loss of energy
If data transfer is prioritized within a single data center, then data transfer costs are reduced, but service reliability may be compromised during server failures
Solution Approach 1:
The system maintains buffer capacity for each server at predetermined thresholds before failures occur. This cushioning capacity serves as a reserve that can be rapidly reallocated when server failures happen, ensuring service continuity while maintaining the primary strategy of minimizing remote data transfers under normal conditions.
Solution Approach 2:
The system continuously monitors server operational states and dynamically recalculates data transfer distribution in response to detected failures or partial failures. This feedback mechanism ensures that when local capacity is insufficient due to failures, the system automatically adjusts to maintain service reliability.
Data Source
AI summary
A system and a method of managing data transfer for a plurality of data centers may include identifying data transfer capacity and data transfer demand for each server of a plurality of data centers; calculating a data transfer distribution for the plurality of data centers that prioritizes data transfer between servers of a first data center of the plurality of data centers over data transfer between servers of the first data center and servers of the remaining data centers of the plurality of data centers based on the identified data transfer capacity and the data transfer demand; and allocating data transfers to the servers of the plurality of data centers.