Distributed Data Center Traffic Imbalance Detection by Time-Weighted Indices
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Solution Overview
Problem
Existing distributed data center systems struggle to efficiently manage data traffic imbalances, leading to degraded performance due to uneven distribution across data centers, which can cause increased latency and inefficient resource allocation.
Innovation Solution
A distributed data center system that collects data samples, weight-transforms them, and divides into subsets based on time intervals to generate indices indicating balanced or imbalanced states, notifying computing devices of imbalances exceeding thresholds, allowing for corrective actions to redistribute data traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data traffic is distributed across multiple geographically dispersed data centers, then system capacity and availability are improved, but data traffic imbalances occur leading to degraded performance
Solution Approach 1:
The system continuously monitors data traffic distribution across data centers and generates imbalance indices that feed back into the load balancing mechanism. When indices exceed thresholds, the system automatically redistributes data traffic to maintain balanced loads, preventing performance degradation while preserving the benefits of distributed architecture.
Solution Approach 2:
The system transforms raw data traffic samples into weight-transformed data samples using time-series decomposition and parameter transformations. This changes the parameter representation from absolute traffic volumes to relative imbalance metrics, enabling effective detection and correction of traffic distribution issues.
2Measurement precision
If data traffic monitoring and analysis is implemented, then data traffic imbalances are detected, but system complexity increases
Solution Approach 1:
The system divides data traffic monitoring into discrete time-series segments and generates separate imbalance indices for different time intervals. This segmentation allows precise detection of imbalances at various granularities without requiring complex simultaneous analysis of all data centers at all times, managing system complexity through structured decomposition.
3Loss of time
If real-time data traffic analysis is performed, then response time to imbalances is improved, but computational resources are consumed
Solution Approach 1:
The system performs partial analysis by generating imbalance indices only when necessary and using threshold-based filtering to avoid unnecessary corrective actions. By analyzing data in time-series segments and only acting when indices exceed thresholds, the system achieves timely response to significant imbalances while conserving computational resources during normal balanced operation.
Data Source
AI summary
Detecting imbalances in data traffic for distributed data centers is described. A distributed data center system can obtain a set of data samples corresponding to respective data traffic at data centers, the respective data traffic based on data associated with an application. The distributed data center system can generate subsets of weight-transformed data samples associated with respective intervals of time. The distributed data center system can generate respective indices associated with the subsets of weight-transformed data samples. The indices can be representative of a data traffic balance at the data centers. The distributed data center system can transmit a message indicating an index failing to satisfy a threshold value, the index being associated with at least one subset of weight-transformed data samples.