Container Pool Management for Resource Isolation
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Solution Overview
Problem
Container-based virtualization faces challenges such as resource overconsumption by rogue containers, underutilization of data centers, and difficulties in scaling and reallocating resources, which existing management technologies are unable to effectively address.
Innovation Solution
A management platform and host agents work together to divide clusters into container pools with set resource limits and priorities, allowing for real-time adjustment and balancing of resources across hosts, ensuring efficient utilization and isolation of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If container-based virtualization is used to deploy large numbers of containers, then resource utilization efficiency is improved, but the risk of rogue containers consuming all resources and crashing the host increases
Solution Approach 1:
The patent implements resource segmentation by dividing host resources into isolated container pools with defined boundaries. Each container is allocated specific resource quotas (CPU, memory, storage) that cannot be exceeded, preventing rogue containers from consuming all host resources. The host agent enforces these segmentation boundaries through real-time monitoring and enforcement mechanisms.
Solution Approach 2:
The host agent serves as an intermediary between containers and host resources. It monitors resource consumption of each container, enforces resource limits, and prevents any single container from exceeding its allocated quota. This intermediary layer blocks harmful actions by rogue containers before they can affect host stability or other containers.
2Reliability
If resources are allocated to individual containers, then container isolation is improved, but data center resource utilization becomes underutilized
Solution Approach 1:
The patent implements dynamic resource allocation where container resource limits are not fixed but can be adjusted in real-time based on actual workload demands. The host agent continuously monitors resource usage patterns and dynamically modifies container quotas to optimize utilization. This allows the system to maintain strong isolation boundaries while adapting resource allocation to match actual needs, preventing both over-provisioning and underutilization.
3Reliability
If container resource limits are enforced, then prevention of resource overconsumption is improved, but resource reallocation flexibility across hosts is reduced
Solution Approach 1:
The host agent implements continuous feedback loops by monitoring container resource consumption in real-time and comparing it against defined limits. When containers approach their quotas, the system provides feedback signals that trigger automatic resource adjustment mechanisms. This feedback-driven approach maintains strict resource control while enabling dynamic reallocation, as the system can respond to changing conditions and redistribute resources based on actual demand patterns across multiple hosts.
Data Source
AI summary
In one embodiment, a system has host machines forming a cluster. Each host machine runs containers, where each container includes a segment of hardware resources associated with the host machine, a segment of an operating system utilized by the host machine, and at least one application. Host agents operate on the host machines. Each host agent collects operational parameters associated with the containers on each host machine. A management platform is operative to divide the cluster into container pools, where each container pool includes a sub-set of computation resources in the cluster and has associated container pool metrics including a priority level and computation resource limits. Operational parameters are collected from the host agents. The operational parameters are evaluated in accordance with the container pool metrics.