Container Placement via Occupancy Pods for Legacy Workloads
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Solution Overview
Problem
There is a need to efficiently manage workloads across different public clouds of various providers, as the surge in migrating workloads from private datacenters to public clouds has led to a requirement for optimizing the use of compute resources, particularly in identifying and utilizing excess capacity within datacenters to deploy containerized applications effectively.
Innovation Solution
A method is implemented that deploys data collecting agents on machines in datacenters to gather resource consumption data, computes excess capacity, and uses this information to deploy containerized applications, while also optimizing resource allocation and migration to ensure sufficient resources for legacy workloads, utilizing controllers and load balancers to manage and distribute API calls efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If containerized applications are deployed on machines with existing legacy workloads, then resource utilization is improved, but resource allocation complexity increases
Solution Approach 1:
The patent introduces an occupancy Pod as an intermediary mechanism that represents legacy workload resource consumption. This Pod acts as a mediator between the legacy workload and the container manager, providing a standardized interface for resource tracking and allocation decisions, thereby simplifying the management of mixed workload environments
Solution Approach 2:
The system creates a unified resource management framework where both legacy workloads and containerized applications are managed through common mechanisms (Pods, resource quotas, scheduling). This universal approach allows the same infrastructure and management tools to handle diverse workload types, reducing operational complexity
2Productivity
If excess capacity is utilized to deploy additional containers, then resource efficiency is improved, but reliability of legacy workloads may deteriorate
Solution Approach 1:
The system implements continuous monitoring of resource consumption by legacy workloads through agents and occupancy Pods. This feedback mechanism allows the container manager to detect when legacy workloads need additional resources and automatically respond by migrating or removing containerized applications to maintain legacy workload performance
Solution Approach 2:
The system reserves capacity for legacy workloads and uses occupancy Pods to pre-account for their resource consumption. This cushioning approach ensures that legacy workloads have guaranteed resource availability before containerized applications are deployed, preventing performance degradation
3Measurement precision
If manual monitoring and management of resource capacity is performed, then control precision is improved, but labor requirements increase
Solution Approach 1:
The system implements self-service automation where agents on machines automatically collect resource consumption data, occupancy Pods automatically report their resource usage, and the container manager automatically makes deployment and migration decisions. This eliminates manual monitoring and management while maintaining precise capacity tracking
Solution Approach 2:
Automated feedback loops continuously monitor resource capacity and trigger appropriate actions. The system self-adjusts by receiving consumption data, analyzing excess capacity, and automatically deploying or migrating workloads without human intervention, maintaining precision while eliminating labor requirements
Data Source
AI summary
Some embodiments provide a novel method for deploying containerized applications. The method of some embodiments deploys a data collecting agent on a machine that operates on a host computer and executes a set of one or more workload applications. From this agent, the method receives data regarding consumption of a set of resources allocated to the machine by the set of workload applications. The method assesses excess capacity of the set of resources for use to execute a set of one or more containers, and then deploys the set of one or more containers on the machine to execute one or more containerized applications. In some embodiments, the set of workload applications are legacy workloads deployed on the machine before the installation of the data collecting agent. By deploying one or more containers on the machine, the method of some embodiments maximizes the usages of the machine, which was previously deployed to execute legacy non-containerized workloads.