Dataset Extraction Clustering for Data Center Response Time
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Solution Overview
Problem
Data centers face inefficiencies in handling large volumes of user posting requests due to delayed dataset extraction from backend storage systems, leading to increased network traffic, resource burdens, and slow response times, especially during events where multiple users attempt to post similar content simultaneously.
Innovation Solution
The method involves predicting the availability time of datasets and clustering user posting requests through network servers to align with this predicted availability time, using a task aggregator to group tasks and manage resource access, thereby optimizing data center processing and reducing network bandwidth demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If dataset extraction is performed from backend storage system, then data becomes available for processing, but access delays occur causing slow response times
Solution Approach 1:
The system performs preliminary actions by predicting when datasets will be available and proactively clustering user posting requests before the data is actually extracted. This allows the system to prepare processing queues in advance, so that when data becomes available, multiple requests can be processed immediately without individual delays, thus improving response time while accounting for extraction delays.
2Productivity
If multiple user posting requests are processed individually, then each request receives attention, but network traffic and resource burdens increase
Solution Approach 1:
The system merges multiple individual user posting requests into a single clustered processing operation. By grouping requests that require the same dataset together and processing them as one batch after data extraction, the system reduces redundant network traffic and resource consumption while maintaining the ability to service all individual users, thus improving processing efficiency and reducing energy loss.
3Reliability
If data extraction is performed before user requests, then data availability improves, but storage resources are consumed unnecessarily
Solution Approach 1:
The system implements self-service by using prediction algorithms to determine exactly when datasets will be needed based on actual user posting patterns and event data. Rather than proactively extracting all possible data in advance, the system waits until prediction indicates user requests are imminent, then extracts only the necessary datasets at that moment. This maintains high data availability for user requests while avoiding unnecessary storage resource consumption.
Data Source
AI summary
Aspects include identifying an event associated with extraction and transmission of a dataset from a data center over a network to one or more network servers and identifying a plurality of tasks associated with a plurality of users and the event. A predicted availability time of the dataset is determined for the one or more network servers based on data extraction performance from a backend data storage system of the data center. Processing is clustered of a plurality of posting requests of two or more of the users through the one or more network servers to align with the predicted availability time of the dataset.