Data Replica Distribution Across Multiple Data Centers
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Solution Overview
Problem
Existing Hadoop clusters face reliability issues due to data being stored only in one data center, leading to data loss and unresponsive client requests when a data center becomes faulty.
Innovation Solution
Implementing a data read and write method that distributes replicas across multiple data centers, allowing clients to access replicas from the data center with highest data locality, and automatically switching to another data center in case of a fault.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all replicas of data are stored in one data center to ensure data locality, then data access efficiency is improved, but system reliability deteriorates when the data center becomes faulty
Solution Approach 1:
The patent segments the storage location of data replicas by distributing them across multiple data centers rather than concentrating all replicas in a single data center. This segmentation allows the system to maintain data locality benefits while avoiding single-point failures, as replicas are divided and placed in geographically separated locations.
Solution Approach 2:
The patent introduces a new dimension for replica placement by extending storage from a single data center to multiple data centers. This dimensional expansion allows the system to achieve both high data locality (by selecting the closest available replica) and high reliability (by having replicas distributed across failure-independent locations).
2Reliability
If replicas are distributed across multiple data centers to ensure reliability, then system reliability is improved, but bandwidth usage between data centers increases
Solution Approach 1:
The patent applies local quality by enabling data access to occur locally within the same data center when possible. The system prioritizes reading replicas from the local data center to avoid cross-data-center bandwidth consumption, while cross-data-center replica access serves as a fallback mechanism only when necessary for reliability.
Solution Approach 2:
The patent introduces dynamic selection of replica locations based on real-time conditions. The system can dynamically choose to access replicas locally or remotely depending on data center availability and operational status, optimizing the balance between reliability and bandwidth usage at any given moment.
3Loss of energy
If data is stored in a single data center to reduce cross-data-center access, then bandwidth usage is reduced, but data loss occurs when the data center fails
Solution Approach 1:
The patent implements preliminary action by pre-distributing data replicas across multiple data centers before any failure occurs. This proactive replication strategy ensures that backup copies are already in place and can be immediately accessed if a data center fails, preventing data loss without requiring post-failure data reconstruction that would consume bandwidth.
Data Source
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AI summary
Embodiments of this application provide a data read method, a data write method, and a server, and relate to the storage field. After a single DC is faulty, data access to the DC by a client is not affected, and a request of the client can be responded. The data read method includes: A resource management server receives a data read request from a client. The data read request is used to request a plurality of files. The resource management server reads a replica of target data from a first data center. The target data includes data of different files among the plurality of files, the first data center is a data center with highest data locality among a plurality of data centers that store replicas of the target data, and data locality is used to indicate a degree of proximity between a replica of the target data stored in a data center and the target data. The resource management server sends, to the client, the replica of the target data read from the first data center.