Cross-Platform Container Scheduling with Hierarchical Fairness
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Solution Overview
Problem
Existing distributed computing approaches face limitations in temporal scope, resource utilization, and platform-specific optimization, failing to provide flexibility, performance, and fairness across different processing paradigms.
Innovation Solution
A cross-platform scheduling method called X-O, which determines container dimensions, assigns tasks to appropriate nodes and owners based on resource requirements, and generates container assignments incorporating scheduling requirements and utilization objectives, ensuring long-term fairness and platform-specific optimization through intelligent hierarchical scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing distributed cluster frameworks are used to consolidate data center resources, then resource consolidation is achieved, but the temporal scope is limited and resource utilization is insufficient
Solution Approach 1:
The patent implements dynamic scheduling that adapts to changing workloads and resource availability over time. The system continuously monitors resource usage and adjusts container assignments dynamically, enabling both short-term responsiveness and long-term resource optimization. This dynamic approach resolves the contradiction by making the scheduling system flexible across different time scales rather than static.
Solution Approach 2:
The system performs preliminary actions by pre-configuring container templates and resource allocations in advance. Container templates define resource requirements and configurations that can be quickly instantiated when needed. This preliminary preparation enables rapid deployment while maintaining long-term resource planning, thus extending the effective temporal scope without sacrificing utilization efficiency.
2Productivity
If centralized managers are used to consolidate resources, then resource consolidation is achieved, but platform-specific optimization is precluded
Solution Approach 1:
The patent segments the scheduling system into hierarchical layers: a centralized resource manager for overall coordination and platform-specific schedulers for local optimization. Each platform can define its own scheduling policies and requirements, while the centralized manager ensures global resource consolidation. This segmentation allows both centralized resource management and platform-specific adaptations to coexist.
Solution Approach 2:
The system applies local quality by allowing different platforms to have customized scheduling parameters and optimization criteria tailored to their specific needs. Each platform can specify its own resource requirements, performance metrics, and scheduling preferences, while still participating in the unified resource pool. This enables platform-specific optimization without sacrificing overall resource consolidation.
3Productivity
If existing scheduling approaches are used, then basic resource allocation is achieved, but flexibility and fairness across different processing paradigms are not provided
Solution Approach 1:
The patent implements a universal container-based scheduling framework that can accommodate multiple processing paradigms and workloads. The system uses standardized container templates that can represent different types of computational tasks while maintaining a unified scheduling interface. This universality provides both basic resource allocation and the flexibility to handle diverse workloads fairly across different processing paradigms.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor resource allocation outcomes and fairness metrics across different platforms and workloads. Based on this feedback, the scheduler adjusts container assignments to improve both efficiency and fairness over time. This closed-loop control enables the system to adapt to varying requirements while maintaining balanced resource distribution across different processing paradigms.
Data Source
AI summary
Methods, systems, and computer program products for cross-platform scheduling with fairness and platform-specific optimization are provided herein. A method includes determining dimensions of a set of containers in which multiple tasks associated with a request are to be executed; assigning each of the containers to a processing node on one of multiple platforms based on the dimensions of the given container, and to a platform owner selected from the multiple platforms based on a comparison of resource requirements of each of the multiple platforms and the dimensions of the given container; and generating container assignments across the set of containers by incorporating the assigned node of each container in the set of containers, the assigned platform owner of each container in the set of containers, one or more scheduling requirements of each of the platforms, one or more utilization objectives, and enforcing a sharing guarantee of each of the platforms.