Microservice Pod State Management for Java Warm-Up Latency
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Solution Overview
Problem
Current resource management systems for Java-based microservices in cloud environments struggle to balance resource utilization and service quality, particularly due to the warm-up process of Java programs, leading to inefficiencies and latency issues.
Innovation Solution
A resource management system comprising a Microservice Resource Manager, Coordinator, and Pod Resource Manager, which implement a combined horizontal and vertical scaling method, introducing new Pod states and preemptive memory swapping to optimize CPU and memory resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If auto-scaling is used to maximize resource utilization, then CPU and memory resource utilization is improved, but service quality and latency SLAs deteriorate due to warm-up time requirements of Java programs
Solution Approach 1:
The patent segments Pod replicas into different states (Running, Warming-up, Suspended) and applies different resource management strategies to each state. This allows the system to distinguish between active service-providing Pods and those undergoing warm-up or suspension, enabling selective resource allocation that maintains service quality while improving overall resource utilization.
Solution Approach 2:
The patent implements preliminary warm-up actions by maintaining a pool of Pods in the Warming-up state before they are needed for handling requests. This ensures that when requests arrive, warmed-up Pods are immediately available to handle traffic without latency spikes, while the system can suspend less-active Pods to reclaim resources.
2Productivity
If more Pods are deployed to handle high request volumes, then service capacity is improved, but resource overhead increases during low load periods
Solution Approach 1:
The patent dynamically adjusts the number of active Pods and their states based on real-time request volumes and service needs. During high load, the system scales up by creating more Pods and transitioning them to Running state. During low load, it suspends excess Pods to reclaim resources, achieving dynamic resource allocation that matches actual service demands.
Solution Approach 2:
The patent recovers resources by suspending Pods that are not currently needed for handling requests. Suspended Pods release their allocated CPU and memory resources back to the pool, allowing the system to reduce overall resource consumption during low-demand periods while maintaining the ability to quickly scale up when needed.
3Reliability
If Java programs undergo warm-up process to reach peak performance, then service performance is improved, but latency increases during the warm-up period
Solution Approach 1:
The patent performs preliminary warm-up actions by maintaining a pool of Pods in the Warming-up state before they are needed for handling requests. This ensures that when requests arrive, warmed-up Pods are immediately available to handle traffic without latency spikes, while the system can suspend less-active Pods to reclaim resources.
Solution Approach 2:
The patent creates a cushion of warmed-up Pods that are ready to handle requests immediately. By maintaining this buffer pool, the system prepares service capacity in advance, ensuring that sudden request spikes do not cause latency issues while still allowing resource reclamation during stable low-demand periods.
Data Source
AI summary
A resource management system for a stateless microservice architecture, applicable to a machine cluster running a Linux operating system and a Kubernetes container orchestration platform; the resource management system has a Linux kernel, installed on the machine cluster, for specific system calls; a Microservice Resource Manager for each microservice, installed on a control node of the machine cluster, equipped with a plurality of sub-managers driven to generate buoys indicating a number of Pod replicas in corresponding states based on statistical data; a Coordinator for each microservice, installed on the control node of the machine cluster, for controlling state transitions, creation, and deletion of Pods for a corresponding microservice based on the buoys generated by the plurality of sub-managers; and a Pod Resource Manager, installed on each compute node of the machine cluster, for monitoring state changes of the Pods and executing corresponding Pods resource management operations.