Cloud Data Persistence Engine for Distributed Storage Optimization
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Solution Overview
Problem
In cloud-based data hosting architectures, storing identical data on multiple servers across geographical locations leads to inefficiencies, as data accessed infrequently is stored on all servers, wasting resources and slowing access times.
Innovation Solution
Implementing a cloud data persistence system where data objects are stored only on servers that frequently or recently accessed by clients, with a network of cache and origin servers to dynamically distribute and update data objects based on request frequency and location, allowing servers to request and route data objects efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all data is stored on each server in the system, then any server can fulfill any client request, but storage resources are wasted and access efficiency decreases
Solution Approach 1:
The patent segments the data storage system into multiple distributed servers, each maintaining a subset of the total data objects. Instead of replicating all data across every server, the system divides the data universe into segments that are distributed across the server network, allowing efficient storage while maintaining accessibility through the distributed architecture and origin server coordination.
Solution Approach 2:
Each server is configured with locally stored data objects that are relevant to its geographic location and client base. The system allows different servers to have different data sets tailored to local access patterns, rather than uniform data replication. This local quality approach reduces redundant storage while ensuring that commonly accessed data is available locally.
2Ease of operation
If all data is stored on each server, then data can be accessed from any location, but access speed decreases due to inefficient storage
Solution Approach 1:
The system performs preliminary actions by pre-loading and caching frequently accessed data objects on local servers before they are actually requested by clients. The origin server proactively manages data distribution to cache servers based on predicted access patterns, so that when clients request data, it is already available locally rather than needing to be retrieved from remote storage.
Solution Approach 2:
The patent introduces origin servers and cache servers as intermediary components between clients and the central data storage. These intermediaries buffer and cache data objects, reducing the need for direct client-to-origin communication and improving access speed by serving data from closer, faster storage locations.
3Quantity of substance
If data is distributed across multiple servers based on access frequency, then storage efficiency improves, but system complexity increases
Solution Approach 1:
The origin servers and cache servers are designed with multi-functionality, serving multiple purposes: they act as data storage nodes, data distribution points, access intermediaries, and coordination hubs. This universal design reduces the need for specialized components and simplifies the overall system architecture while maintaining storage efficiency through intelligent data placement.
Solution Approach 2:
The distributed server system implements self-service mechanisms where servers automatically request and receive data objects from the origin server when needed, without requiring centralized manual configuration. The origin server autonomously manages data distribution to cache servers based on system state and access patterns, reducing operational complexity.
4Reliability
If servers request data objects from origin servers when not locally available, then data freshness is maintained, but communication overhead increases
Solution Approach 1:
The system performs preliminary data distribution by the origin server proactively pushing data objects to cache servers before they are requested by clients. This preliminary action reduces the need for reactive requests from cache servers, maintaining data freshness while minimizing communication overhead by pre-positioning data at the network edge.
Solution Approach 2:
The origin server maintains continuous useful action by constantly monitoring data access patterns and dynamically adjusting data distribution to cache servers. This continuous adaptation ensures data freshness is maintained through timely updates while optimizing communication efficiency by only transferring data that is actually needed based on current access patterns.
Data Source
AI summary
Various cloud data persistence systems and methods are described. In some embodiments, a client may request a data object from a server. This data object may contain a link to a media asset and other information linked to the video file, such as a title and duration. This media asset may be stored in a different location. If this first server does not have the data object stored locally, it may contact a second server to determine if it has the data object stored. If the second server does not have the data object stored, it may contact a data object origin server, which may have all existing data objects stored. This data object origin server may then send the data object to the second server, which may in turn transmit the data object to the first server. The first server may then transmit the data object to the client.