Asynchronous Cache Update for Data Freshness and Latency
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Solution Overview
Problem
Data storage operations in cloud environments face significant latency due to remote storage servers, and existing caching methods often return stale data from local caches, which can impact performance.
Innovation Solution
Implementing a server system that maintains a cache of objects and uses asynchronous computations to determine object values, allowing for immediate return of cached values before computation completion and updating the cache with fresh values post-completion, thereby reducing latency and ensuring data freshness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is fetched from remote cloud storage servers, then data freshness is improved, but latency increases significantly
Solution Approach 1:
The system performs preliminary actions by starting asynchronous computations to fetch data from remote storage before the actual read request completes. The server initiates background tasks that populate the cache in advance, so when a read request arrives, the data is already available or nearly available, eliminating the need to wait for remote storage operations.
Solution Approach 2:
The patent introduces a cache as an intermediary layer between the client and remote cloud storage servers. This cache holds copies of frequently accessed data objects, allowing read requests to be served from the local cache rather than fetching data from remote storage, thereby reducing latency while maintaining data freshness through periodic asynchronous updates.
2Loss of time
If data is returned from local cache immediately, then latency is reduced, but data freshness deteriorates
Solution Approach 1:
The system maintains continuous useful action by running asynchronous computations in the background that continuously update the cache with fresh data from remote storage. This allows the cache to serve read requests immediately while simultaneously maintaining data freshness through ongoing background synchronization, eliminating the trade-off between immediate response and data currency.
Solution Approach 2:
Asynchronous computations perform preliminary data fetching and cache updates before read requests require the data. By proactively populating the cache with fresh data in the background, the system ensures that when read requests arrive, the cache contains up-to-date information, allowing immediate responses without sacrificing data freshness.
3Productivity
If asynchronous computations are used to fetch data, then system responsiveness is improved, but complexity of data management increases
Solution Approach 1:
The system implements self-service by having asynchronous computations automatically manage cache updates without requiring manual intervention or complex coordination. The background tasks autonomously fetch data from remote storage and update the cache, reducing the burden on the main request-handling logic and simplifying data management while maintaining high system responsiveness.
Data Source
AI summary
A processing system server and methods for performing asynchronous data store operations. The server includes a processor which maintains a cache of objects in memory of the server. The processor executes an asynchronous computation to determine the value of an object. In response to receiving a request for the object occurring before the asynchronous computation has determined the value of the object, a value of the object is returned from the cache. In response to receiving a request for the object occurring after the asynchronous computation has determined the value of the object, a value of the object determined by the asynchronous computation is returned. The asynchronous computation may comprise at least one future, such as a ListenableFuture, or a process or thread. The asynchronous computation may determine the value of the object by querying at least one additional server.