Overload Protection for Data Sinks in Distributed Systems
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Solution Overview
Problem
Large distributed systems, such as content delivery networks, face challenges in managing and processing the vast amounts of data generated by edge services, leading to potential overload of data sinks due to spikes in data volume and complexity, which existing technologies struggle to handle effectively.
Innovation Solution
A system that includes a data collection system with an overload protection mechanism, which processes reporting data from edge servers and IoT devices, applies traffic shaping actions such as thinning and throttling based on policies defined by data sink owners to control the volume and nature of data sent to data sinks, ensuring that each sink receives data within its capacity limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data collection system receives all reporting data from edge servers, then data completeness is improved, but data sink overload occurs
Solution Approach 1:
The system performs preliminary actions by monitoring data volume metrics and detecting overload conditions before the data sink becomes overwhelmed. Traffic shaping actions are triggered proactively based on predicted or emerging overload patterns, preventing the complete loss of data integrity while protecting the data sink from failure.
Solution Approach 2:
The system dynamically adjusts traffic shaping parameters based on real-time monitoring of data volume, data sink capacity, and overload conditions. The traffic shaping configuration changes adaptively to balance data completeness and data sink stability, allowing the system to maintain optimal performance under varying load conditions.
2Reliability
If traffic shaping actions are applied to control data volume, then data sink overload is prevented, but data processing time increases
Solution Approach 1:
The system segments traffic shaping operations into distinct phases: monitoring phase, analysis phase, and enforcement phase. By dividing the data stream into manageable segments and applying traffic shaping rules incrementally, the system reduces the computational overhead compared to analyzing and controlling the entire data stream simultaneously, thereby reducing processing time while maintaining data sink stability.
Solution Approach 2:
The traffic shaping component acts as an intermediary between the data collection system and the data sink, filtering and regulating data flow before it reaches the data sink. This intermediary layer offloads the burden of volume control from the data sink itself, allowing the data sink to process data more efficiently without being overwhelmed by raw volume management tasks.
3Adaptability or versatility
If multiple policies are applied to different data sinks, then data sink-specific requirements are met, but system complexity increases
Solution Approach 1:
The system implements a universal traffic shaping framework that can apply different policies to multiple data sinks through a single unified interface. The traffic shaping component is designed to be multi-functional, handling various data types, volume metrics, and policy configurations through consistent mechanisms, thereby reducing the operational complexity despite supporting diverse data sink requirements.
Solution Approach 2:
The system manages complexity by parameterizing policies rather than hardcoding different handling logic for each data sink. By changing parameters such as volume thresholds, shaping algorithms, and priority levels, the same traffic shaping infrastructure can adapt to different data sink requirements, reducing configuration complexity while maintaining high adaptability.
Data Source
AI summary
Described in this document, among other things, is an overload protection system that can protect data sinks from overload by controlling the volume of data sent to those data sinks in a fine-grained manner. The protection system preferably sits in between edge servers, or other producers of data, and data sinks that will receive some or all of the data. Preferably, each data sink owner defines a policy to control how and when overload protection will be applied. Each policy can include definitions of how to monitor the stream of data for overload and specify one or more conditions upon which throttling actions are necessary. In embodiments, a policy can contain a multi-part specification to identify the class(es) of traffic to monitor to see if the conditions have been triggered.