Cluster Workload Scoring to Contain Antagonistic Propagation
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Solution Overview
Problem
Cloud computing systems face challenges in managing distributed workloads due to the risk of antagonistic workloads that can cause performance degradation or outages by rapidly spreading across multiple locations, impacting shared infrastructure components.
Innovation Solution
A method and system that utilize workload keys based on characteristics to associate workloads with similar execution behaviors, monitor their history, and assign scores to determine optimal clusters for execution, isolating antagonistic workloads to specific regions or clusters while allowing non-antagonistic workloads to migrate freely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workloads are rapidly distributed to execute at multiple different locations, then workload execution speed and scalability are improved, but the risk of antagonistic workloads spreading and causing system outages increases
Solution Approach 1:
The system segments workloads by generating workload keys based on workload characteristics, allowing similar workloads to be grouped and tracked separately. This segmentation enables the system to control the propagation of antagonistic workloads to specific clusters while allowing non-antagonistic workloads to distribute freely across multiple locations, thus maintaining both high execution speed and system reliability
Solution Approach 2:
The system performs preliminary actions by determining workload keys and obtaining workload histories before executing workloads. By pre-assessing workload characteristics and checking historical data, the system can identify potentially antagonistic workloads in advance and prevent their uncontrolled propagation, while still enabling rapid execution of benign workloads
2Reliability
If workload monitoring and history tracking are implemented, then system reliability is improved, but device complexity increases
Solution Approach 1:
The workload key serves as a universal identifier that performs multiple functions: it groups similar workloads together, enables history tracking, and facilitates propagation control. This multi-functional approach allows the system to achieve reliable monitoring without adding complex separate mechanisms for each function
Solution Approach 2:
The system creates simplified copies of workload information by generating workload keys based on workload characteristics. Instead of tracking complete workload details, the system uses these key representations to monitor and control workload propagation, reducing complexity while maintaining reliability
Data Source
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AI summary
A method includes receiving a request to execute a particular workload of a plurality of workloads at a distributed computing system that includes a plurality of clusters. Each workload of the plurality of workloads includes respective workload characteristics. The method also includes determining a workload key for the particular workload based on the respective workload characteristics of the particular workload. The method also includes obtaining a workload history based on determining the workload key and, for each respective cluster of the plurality of clusters, determining a corresponding score associated with executing the particular workload at the respective cluster based on the workload history. The method also includes executing the particular workload at one of the plurality of clusters based on the corresponding score of each respective cluster of the plurality of clusters.