Cloud Autoscaling Service with Dynamic Execution Type Routing
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Solution Overview
Problem
Current autoscaling technologies in cloud services only provide the same quality of service based on service request load, failing to efficiently manage diverse service loads and varying response times across multiple clients, especially with the rise of microservices and edge services.
Innovation Solution
An apparatus and method for autoscaling cloud services that dynamically generate or delete secondary services with different execution types (application type and function type) based on client-specific response time requirements, using scaling information to balance load and optimize resource usage, with the load balancer directing requests to appropriate service instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autoscaling is applied to provide the same quality of service based on service request load, then service availability is improved, but resource efficiency deteriorates due to inability to differentiate between diverse service loads
Solution Approach 1:
The patent applies local quality by differentiating service handling based on client-specific requirements. The service determination unit identifies whether a service request requires low-latency processing or can tolerate higher latency, and routes accordingly to application-type or function-type services. This allows each service instance to be optimized for its specific function rather than all instances being identical, improving resource efficiency while maintaining availability.
Solution Approach 2:
The patent segments services into two distinct types: application-type services for low-latency requests and function-type services for higher-latency requests. This segmentation allows the system to allocate resources differently for different service categories, preventing over-provisioning of high-performance resources for all services and thus improving overall resource efficiency while maintaining service availability.
2Adaptability or versatility
If multiple service types with different response times are introduced to meet diverse client needs, then service versatility is improved, but system complexity increases
Solution Approach 1:
The patent implements universality by creating a multi-functional service architecture where both application-type and function-type services can handle the same underlying service requests. The service determination unit universally applies the same decision logic to route requests to appropriate service types, and the load balancer universally manages both service types through a common interface. This allows the system to provide versatile service handling while maintaining manageable complexity through standardized routing and management mechanisms.
3Measurement precision
If service requests are routed based on client-specific response time requirements, then service precision is improved, but processing overhead increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing the service determination unit that contains client information and service type associations. When a service request arrives, the system has already prepared the routing logic and service type mappings, allowing for rapid determination without complex real-time analysis. This preliminary preparation reduces processing overhead while maintaining precise routing based on client-specific response time requirements.
Data Source
AI summary
Disclosed herein are an apparatus and method for autoscaling a service shared in a cloud. The apparatus may include memory in which at least one program is recorded and a processor for executing the program, and the program may perform autoscaling by which at least one second service for performing the same function as a first service is additionally generated or deleted depending on a load that is incurred when multiple clients call the first service in the cloud. The at least one second service may be set to one of two or more execution types having different response times depending on a response time required by each of the multiple clients.