Data Processing Method for Cloud Platform Resource Optimization
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Solution Overview
Problem
Traditional data management methods face scalability limitations and high operational costs in ensuring high online availability and performance for large-scale content data storage and distribution, particularly in cloud computing environments.
Innovation Solution
The method involves determining physical addresses for data shards within physical data groups based on logic information, allowing for efficient ordering and resource allocation, which simplifies data processing and improves resource utilization through the use of virtual and physical data groups and shards, enabling dynamic resource adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data management methods are used to store and distribute large-scale content data, then data storage and distribution can be achieved, but scalability is restricted and machine costs and operation and maintenance costs increase
Solution Approach 1:
The patent segments data into shards and organizes them in physical data groups with logical data groups, allowing independent management and distribution of data segments. This segmentation enables parallel processing and distributed storage, improving scalability without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces address mapping mechanisms and indexing structures as intermediaries between logical data requests and physical data storage. These intermediaries abstract the complexity of physical data distribution, allowing efficient data access without directly managing the underlying complex storage infrastructure.
2Reliability
If high machine costs and high operation and maintenance costs are incurred, then high online availability and performance can be ensured, but costs increase
Solution Approach 1:
The patent creates a multi-functional data management system where physical data groups serve both storage and distribution functions, while logical data groups provide both organization and access control. This multi-functionality reduces the need for dedicated specialized components, lowering resource requirements while maintaining reliability.
Solution Approach 2:
The patent implements dynamic address mapping and flexible data group configurations that can adapt to changing load conditions. This dynamic approach allows the system to optimize resource allocation in real-time, maintaining high availability without permanently over-provisioning resources.
3Ease of operation
If data is distributed across multiple physical addresses, then data distribution and accessibility improve, but address management and data ordering complexity increase
Solution Approach 1:
The patent introduces logical data groups and address mapping tables as intermediaries between simple logical data identifiers and complex physical addresses. This layering allows easy data access through logical identifiers while the intermediary handles the complexity of physical address translation and management.
Solution Approach 2:
The patent adds a logical organization dimension above the physical storage dimension. Data can be accessed through logical groupings that are independent of physical address arrangements, effectively adding an abstraction dimension that simplifies access while managing physical complexity separately.
Data Source
AI summary
Provided are a data processing method and apparatus, a device, and a storage medium, which relate to the technical field of cloud computing and cloud platform. The specific implementation scheme includes: determining, according to logic information of first data acquired from an ordering tool, first physical addresses, where the first physical addresses are physical addresses of data shards in a physical data group associated with the first data; and sending the first physical addresses to the ordering tool to cause the ordering tool to order the first data according to the first physical addresses.