Cloud Backup Load Balance via Dynamic Server Quota Ratios
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Solution Overview
Problem
Current data protection solutions in cloud environments face unbalanced server loads due to varying data generation rates across multiple applications and virtual machines, leading to inefficient resource allocation and human resource wastage in manually managing server associations.
Innovation Solution
Implementing a round robin mechanism to determine backup quota ratios for multiple servers, selecting a server for each application based on these ratios, and automatically allocating data backup tasks to achieve load balance across servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If each data center is bounded to one corresponding back-end server for data protection, then data protection can be implemented, but the server load becomes unbalanced due to varying data generation rates across different applications and virtual machines
Solution Approach 1:
The system segments the data center into multiple applications and virtual machines, each with independent backup requirements. Instead of binding entire data centers to single servers, the patent divides backup tasks into smaller units that can be dynamically allocated to different servers based on current load conditions, achieving both data protection and load balance.
Solution Approach 2:
The patent implements dynamic server selection where the binding between data centers and back-end servers is not fixed but changes based on real-time server load conditions. The system continuously monitors server loads and dynamically adjusts which server handles backup for which application, transforming the static one-to-one binding into a dynamic many-to-many relationship.
2Ease of manufacture
If manual management of server associations is used, then server binding can be established, but human resources are wasted and resource allocation is inefficient
Solution Approach 1:
The system implements self-service automation where the backup management system automatically monitors server loads, selects appropriate servers for each backup task, and dynamically adjusts bindings without human intervention. This eliminates manual management overhead while optimizing resource allocation based on real-time conditions.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors server load metrics and uses this information to make intelligent decisions about task allocation. The feedback loop enables automatic adjustment of server bindings based on current system state, improving resource allocation efficiency without manual input.
3Device complexity
If servers are statically bound to data centers, then simple management is achieved, but servers may become overloaded or underloaded leading to inefficient resource utilization
Solution Approach 1:
The patent transforms the static server-data center binding into a dynamic relationship where bindings are continuously adjusted based on server load conditions. The system monitors load metrics and automatically rebalances task allocation, allowing servers to adapt to changing conditions and maintain optimal utilization without complex manual management.
Solution Approach 2:
The system changes the parameter of server binding from fixed to variable, allowing the association between servers and data centers to fluctuate based on load parameters. This enables automatic load balancing where the binding configuration adapts to changing system conditions, improving resource utilization while maintaining manageable complexity.
Data Source
AI summary
Embodiments of the present disclosure provide a method, an electronic device and a computer program product for load balance. The method comprises: determine, in a round robin period, backup quota ratios for a plurality of servers used for data backup and associated with a cloud service platform; select, based on the backup quota ratios, one server from the plurality of servers; and in response to receiving a request for backup of data from an application on the cloud service platform, cause the backup of the data from the application to the selected server. In this way, load balance for a plurality of backup servers associated with a cloud service platform is achieved.