Cloud Load Balancing via Geographic Proximity and Resource Scoring
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Solution Overview
Problem
Existing cloud management systems face challenges in load balancing for container-based microservices, particularly in associative container environments, where resource expansion and service migration are inflexible, and traffic distribution is not effectively managed, leading to delays and inefficiencies.
Innovation Solution
A cloud management method that monitors resource availability across clusters, calculates scores based on geographical proximity and resource status, and performs load balancing by redirecting traffic to clusters with sufficient resources, minimizing response delays and optimizing service distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If container-based microservices are used to efficiently use internal resources and enable rapid application distribution, then resource utilization and deployment speed are improved, but the ability to expand resources in response to increasing user traffic is limited
Solution Approach 1:
The system segments the service deployment across multiple independent clusters distributed across different geographic regions. Each cluster can independently handle traffic and provide services, allowing the system to scale horizontally by adding more cluster segments rather than being limited by a single container platform's resource capacity.
Solution Approach 2:
The patent introduces geographic location as an additional dimension for service deployment and traffic routing. Instead of only considering resource availability within a single platform, the system distributes services across multiple geographic regions and uses location-based scoring to route traffic, thereby expanding resource capacity in a spatial dimension.
2Reliability
If distributed collaboration type container platform technology is used to provide elastic service extension and regional distribution, then service availability and flexibility are improved, but traffic distribution (load-balancing) function suitable for multi-cluster environment is not provided
Solution Approach 1:
The system changes the parameters used for load balancing from simple round-robin or random distribution to a multi-factor scoring system that considers resource availability, geographic proximity, and service ownership. This parameter transformation enables intelligent traffic distribution across clusters while maintaining service availability.
Solution Approach 2:
The patent introduces an intermediary load balancing mechanism that sits between the client and multiple clusters. This intermediary receives traffic, evaluates cluster scores based on multiple criteria, and routes traffic to the most suitable cluster, thereby simplifying the complexity of direct multi-cluster communication while ensuring reliable service delivery.
3Ease of operation
If load balancing is performed without considering geographic location and resource status, then implementation simplicity is maintained, but response delays increase and service efficiency decreases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and maintaining scores for each cluster based on resource availability and geographic location before traffic arrives. When a request comes in, the system can quickly select the best cluster using the pre-computed scores, avoiding time-consuming real-time calculations and reducing response delays while maintaining operational simplicity.
Data Source
AI summary
There is provided a cloud management method and apparatus for performing load balancing so as to make a service in a cluster that is geographically close in an associative container environment and has a good resource current status. The cloud management method according to an embodiment includes: monitoring, by a cloud management apparatus, available resource current statuses of a plurality of clusters, and selecting a cluster that owns a first service supported by a first cluster an available resource rate of which is less than a threshold value; calculating, by the cloud management apparatus, scores regarding an available resource current status and geographical proximity of each cluster; and performing, by the cloud management apparatus, load balancing of the first service, based on a result of calculating the scores. Accordingly, a delay in a response speed of a service that is required in a distributed environment can be minimized, and a service can be supported to be processed in a geographically close cluster through analysis of geographical closeness (proximity) between an access location where there is a user request and a cluster in which services are distributed.