Edge Pipeline Scheduling for Resource Starvation Prevention
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Solution Overview
Problem
Existing pipeline systems in edge computing environments suffer from resource starvation due to limited resources and inefficient scheduling algorithms, leading to imbalanced load distribution and increased latency, which is not effectively addressed by current methods that rely on application design or manual threshold settings.
Innovation Solution
A computer-implemented method that analyzes timing data for stages and pipelines in edge systems to detect starvation by monitoring lag times and ratios, allowing for dynamic resource reallocation to maintain balanced resource usage and prevent overallocation, thereby ensuring smooth operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If priority scheduling is used to execute higher priority processes first, then processing speed of high-priority tasks is improved, but lower priority processes suffer from starvation and increased waiting time
Solution Approach 1:
The system dynamically adjusts process priorities based on real-time resource availability and pipeline stage status. Instead of static priority scheduling, the scheduler continuously monitors resource usage and pipeline lag, adjusting priorities dynamically to prevent starvation while maintaining efficient processing of high-priority tasks.
Solution Approach 2:
The system implements feedback mechanisms where pipeline stage performance metrics (such as lag time and resource usage) are continuously monitored and fed back to the scheduler. This feedback loop enables the system to detect starvation conditions and adjust scheduling decisions accordingly, balancing speed and waiting time across all processes.
2Productivity
If resources are dynamically allocated to individual pipeline stages based on their needs, then each stage can operate efficiently, but resource imbalance causes starvation of some stages and increased overall latency
Solution Approach 1:
The system merges resource allocation decisions across multiple pipeline stages into a unified scheduling framework. Instead of independent autoscalers making isolated decisions, the system coordinates resource allocation across stages, considering the interdependencies between stages to prevent resource imbalance and starvation while maintaining high throughput.
Solution Approach 2:
The system changes resource allocation parameters dynamically based on pipeline performance metrics. By monitoring stage lag and resource usage, the system adjusts CPU and memory allocation parameters in real-time, shifting resources from well-performing stages to starving stages to maintain balanced throughput and minimize latency.
3Reliability
If manual threshold settings are used for resource allocation, then resource distribution can be controlled, but the system cannot adapt to dynamic workloads and experiences starvation
Solution Approach 1:
The system transitions from static manual threshold settings to dynamic adaptive thresholds that automatically adjust based on workload characteristics and pipeline performance. The scheduler learns from historical data and real-time metrics to optimize resource allocation for different workload types, maintaining stability while improving adaptability.
Solution Approach 2:
The system implements self-service resource allocation where the scheduling mechanism automatically monitors pipeline performance and adjusts resource distribution without manual intervention. The system serves itself by detecting starvation conditions and reallocating resources based on current needs, eliminating the rigidity of manual thresholds while maintaining reliable resource distribution.
Data Source
AI summary
A computer-implemented method, in accordance with one aspect of the present invention, includes analyzing timing data for stages in a pipeline running in an edge system for detecting starvation of one or more of the stages in the pipeline. In response to detecting one or more of the stages being starved, starvation avoidance is performed for mitigating the starvation of the starving stage(s). A computer-implemented method, in accordance with another aspect of the present invention, includes analyzing timing data for pipelines running in parallel in an edge system for detecting starvation of one or more of the pipelines. In response to detecting one or more of the pipelines being starved, starvation avoidance is performed for mitigating the starvation of the starving pipeline(s).