Client-Side Data Processing for Server Workload Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing web traffic load and server workload in distributed computing environments lead to higher operational costs, longer response times for clients, and increased network loads, as servers bear the burden of processing and storing large amounts of data.
Innovation Solution
Shift at least part of the data processing from the server to the client, where the client receives raw data from the server, processes it to obtain result data, and stores it for peer-to-peer sharing, thereby reducing the server's workload and leveraging client-side computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the server processes and stores all data requests, then data availability is ensured, but server workload and operational costs increase
Solution Approach 1:
The patent segments the data processing function between server and client. The server provides raw data, while the client performs the actual processing and storage of result data. This segmentation reduces server workload by transferring processing responsibilities to clients, while maintaining data availability through distributed storage across multiple clients.
Solution Approach 2:
Clients serve themselves by processing raw data locally and storing result data in their own memory. Each client becomes self-sufficient for processing requests, reducing dependency on the server for every operation. The server only needs to provide raw data initially, after which clients independently handle processing and can even serve other clients.
2Measurement precision
If the server processes all data requests, then processing accuracy is maintained, but response time increases
Solution Approach 1:
The client performs data processing in advance by receiving raw data, processing it to generate result data, and storing it locally before any subsequent requests arrive. This preliminary action eliminates the need to wait for server processing each time a request is made, significantly reducing response time while maintaining accuracy since the same processing logic is applied.
Solution Approach 2:
The client creates a local copy of the processed result data in its memory after receiving raw data from the server. This copy can be served immediately to requesting clients without involving the server in the actual data retrieval process, reducing response time while maintaining data accuracy through identical processing.
3Stability of the object's composition
If the server handles all data transmission, then data consistency is ensured, but network load increases
Solution Approach 1:
The patent extracts the result data from the server and places it in the client's memory. Once extracted and stored locally, the data no longer needs to be transmitted over the network for subsequent access. This extraction reduces network load by eliminating repeated transmissions, while data consistency is maintained through the initial server-to-client transfer and subsequent peer-to-peer sharing.
Solution Approach 2:
The client acts as an intermediary between the server and other clients. Instead of all clients communicating directly with the server, the client that has processed and stored the result data serves as a local intermediary, providing data to other clients without involving the server in each transmission, thereby reducing network load.
4Productivity
If more server resources are allocated, then processing capacity increases, but operational costs increase
Solution Approach 1:
Instead of increasing server resources to handle more processing, the patent inverts the approach by enabling clients to perform processing themselves. Clients use their own computing resources to process raw data and generate result data, thereby increasing overall processing capacity without requiring additional server resources or incurring higher operational costs.
Solution Approach 2:
The patent makes clients multi-functional by enabling them to perform not only data consumption but also data processing and storage functions. This universality allows the distributed system to leverage the computing resources of all clients, effectively increasing processing capacity across the network without concentrating resources on the server, thus avoiding increased operational costs.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Data is processed in a distributed computing environment with at least one server and a plurality of clients comprising at least a first client and a second client. The first client sends a first request to the server to obtain result data, receives raw data from the server as a response to the first request, processes the raw data to obtain the result data and stores the result data, and sends the result data to the second client in response to receiving a third request to obtain the result data from the second client.