Cache and Scaler for Data Storage Peak Handling
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Solution Overview
Problem
Network computing and storage systems face challenges in maintaining consistent user experience due to lag between data storage requests and their processing, especially during peak or high request rates, where asynchronous processes can lead to significant delays in responding to storage event status requests.
Innovation Solution
Implementing a cache to temporarily store event records related to queued data storage requests and using a scaler to adjust the data storage system's capacity and dequeuing rate based on incoming request rates, allowing synchronous responses to storage event status requests and maintaining consistent performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If asynchronous processes are used to handle large volumes of data storage, then the system can process high request rates, but there is a considerable lag between when a data storage request is received and when it is fully processed
Solution Approach 1:
The patent implements a queueing mechanism that preliminarily organizes incoming data storage requests before processing. By accepting and queuing requests synchronously while processing them asynchronously, the system prepares the request structure in advance, allowing status information to be returned immediately upon queue insertion rather than waiting for complete processing. This resolves the contradiction by separating request acceptance from request processing while maintaining trackable status.
2Productivity
If asynchronous processing is used to handle peak request rates, then the system can maintain high throughput, but the user experience becomes inconsistent due to variable response times
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors queue depth and processing status, then provides real-time status information back to users through synchronous responses. The status indicator reflects the current state of each request (queued, processing, completed, failed), giving users immediate feedback without requiring them to wait for full processing. This maintains consistent user experience while preserving asynchronous throughput capability.
3Reliability
If the data storage system processes all requests synchronously to provide immediate status responses, then user experience is consistent, but the system cannot handle large volumes of requests efficiently
Solution Approach 1:
The patent segments the data storage system into distinct functional components: a request acceptance layer that provides synchronous responses, a queueing layer that buffers requests, and an asynchronous processing layer that handles actual data operations. This segmentation allows the system to provide immediate status acknowledgment for each request while delegating the computationally intensive processing to asynchronous workers. The separation enables both consistent user feedback and high throughput processing capacity.
Data Source
AI summary
Techniques described and suggested herein include implementations of caches and scalers to handle data storage requests, and storage event status requests associated with data storage requests, in a scalable fashion. For example, a data storage system, such as a data storage system implemented by a computing resource service provider in connection with providing an archival storage service or other data storage service, may be implemented to maintain a consistent response time and backend capability for incoming data storage requests, which may be a component of ensuring a consistent customer experience for customers of an associated service, with little or no regard to peaky or high data storage request rates observed by the implementing data storage system.