Edge Cloud Latency Control for Video Streaming Analytics
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Solution Overview
Problem
Edge cloud streaming analytics services face challenges in managing latency due to varying resource availability, leading to potential system overload and long tail latency in video streaming, particularly in scenarios where bucket notification events exceed available CPU and memory resources.
Innovation Solution
Implementing a host latency control notification system that explicitly informs edge streaming processes of storage and computing resource availability, allowing adjustments to TCP windows, upload intervals, or chunk sizes to optimize latency, ensuring low latency by dynamically tuning parameters such as chunk size, file size, and TCP settings based on latency analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bucket notification events are increased to improve processing speed, then productivity is improved, but system overload occurs due to exceeding CPU and memory resources
Solution Approach 1:
The system implements feedback by monitoring resource utilization metrics (CPU, memory, disk I/O, network I/O) and using this information to dynamically adjust the rate of processing bucket notification events. When resources are constrained, the system reduces the event processing rate to prevent overload, and when resources are abundant, it increases the rate to improve productivity.
Solution Approach 2:
The system dynamically adjusts its operation based on real-time resource conditions. The processing rate of bucket notification events is not fixed but adapts continuously according to the available computational resources, transforming a static system into a dynamic one that can respond to changing conditions.
2Loss of time
If upload parameters are optimized to reduce latency, then time is reduced, but resource availability constraints are violated
Solution Approach 1:
The system changes operational parameters (such as upload interval, chunk size, TCP window size) based on resource availability conditions. When resources are abundant, parameters are tuned to minimize latency; when resources are constrained, parameters are adjusted to respect resource limits, thus adapting to different operational contexts.
Solution Approach 2:
Upload parameters are not static but dynamically adjusted based on real-time resource monitoring. The system continuously adapts upload behavior to match available resources, ensuring low latency when possible while preventing resource exhaustion.
3Productivity
If resource utilization is increased to improve productivity, then productivity is improved, but system stability deteriorates due to potential overload
Solution Approach 1:
The system uses feedback from resource utilization monitoring to maintain stability. When utilization approaches dangerous levels, the system receives feedback and adjusts operations to reduce load, preventing instability while still maximizing productivity within safe operating limits.
Solution Approach 2:
The system prepares for potential overload by monitoring resource levels in advance and taking preventive action before critical thresholds are reached. This cushioning approach allows the system to maintain high productivity while having a safety margin that prevents instability.
Data Source
AI summary
A method includes determining a first upload start timestamp and a first upload end timestamp of a video file from a client device to an object storage device. The method further includes monitoring an upload of the video file from the object storage device to a server-less framework to determine a second upload start timestamp and a second upload end timestamp of the video file. The method further includes determining, by a processing device, a latency of the upload in view of the first upload timestamp, the second upload timestamp, the first upload end timestamp, and the second upload end timestamp. The method further includes providing a latency adjustment instruction to the client device in view of the latency.