Distributed Profiling Orchestrator for Resource-Constrained Clusters
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Solution Overview
Problem
Distributed computing systems face challenges in performance tuning due to the distributed nature of computing systems, which involves multiple traffic flows across networks, limiting the practicality of establishing simultaneous network connections for profiling and exhausting processing resources, especially when security requirements are considered.
Innovation Solution
A distributed performance tuning architecture is implemented, featuring a profiling orchestrator service that coordinates profiling activities across multiple nodes, establishing efficient communication and data aggregation to overcome the limitations of traditional profiling methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simultaneous network connections are established for profiling across multiple worker nodes, then profiling coverage is improved, but processing resources are exhausted
Solution Approach 1:
The patent introduces a master node as an intermediary that collects profiling data from worker nodes and forwards it to the profiling tool. This mediator architecture allows profiling to occur without requiring the profiling tool to maintain simultaneous connections to all worker nodes, thus preserving processing resources while maintaining comprehensive profiling coverage
Solution Approach 2:
The profiling system is segmented into distinct components: profiling agents on worker nodes, a master node for data collection, and a profiling tool for analysis. This segmentation distributes the profiling functionality across multiple entities, allowing each component to operate independently with reduced resource requirements
2Reliability
If security requirements are implemented for distributed profiling, then system security is improved, but connection establishment becomes less practical
Solution Approach 1:
The master node serves as a security intermediary that handles authentication and data collection centrally. Worker nodes communicate securely with the master node, which then manages data aggregation and forwarding to the profiling tool, simplifying security implementation while maintaining ease of operation
3Measurement precision
If traditional profiling methods are used in distributed systems, then profiling capability is maintained, but scalability is limited
Solution Approach 1:
The master node performs multiple functions: collecting profiling data from worker nodes, aggregating the data, and forwarding it to the profiling tool. This multi-functional design allows the system to scale by adding more worker nodes without requiring proportional increases in the profiling infrastructure
Solution Approach 2:
The master node as an intermediary enables the system to scale by centralizing data collection and management, allowing profiling capability to extend across an increasing number of worker nodes without proportionally increasing the complexity of the profiling tool
Data Source
AI summary
A method of profiling distributed computing jobs may include receiving, by a profiling orchestrator service, a profiling request initiated by a profiling client. The profiling request may identify a master node and a target application to be executed by the master node and a plurality of worker nodes. The method may further include transmitting, to a worker node of the plurality of worker nodes, a profiling command identifying a parameter reflecting execution of the target application on the worker node. The method may further include receiving profiling data comprising an execution marker associated with execution of the target application on the worker node. The method may further include forwarding the profiling data to the profiling client.