Network Traffic Flow Logger with Variable Token Bucket
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Solution Overview
Problem
Conventional network traffic flow logging systems in distributed computing systems rely on outdated hardware-based technologies and fail to leverage modern advancements, such as software-defined networks and resource virtualization, leading to inefficiencies in processing and logging network flow events.
Innovation Solution
A novel network traffic flow logger with a token bucket having variable capacity, a queue model based on arrival rates and service times, and a throttling rate controller is implemented to provide adjustable processing of network flow events, maximizing logged flows while avoiding memory depletion and target CPU utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional hardware-based network traffic flow logging systems are used, then the system structure is simple and easy to implement, but the processing capacity and efficiency are insufficient for high incoming rates of flow events
Solution Approach 1:
The network traffic flow logging system is segmented into multiple independent components: token bucket controllers (with variable capacity), queue models (based on arrival rates and service times), and throttling rate controllers. This segmentation allows each component to handle specific aspects of flow event processing independently, thereby increasing overall processing capacity while maintaining manageable complexity through modular design.
Solution Approach 2:
The system implements dynamic parameters including variable token bucket capacity, adjustable queue sizes, and configurable throttling rates. These dynamic elements allow the system to adapt its processing capacity in real-time based on incoming flow event rates, maximizing productivity without requiring a fixed complex infrastructure.
2Productivity
If the throttling rate is increased to accommodate high incoming rates of flow events, then the processing capacity improves, but the risk of memory depletion and CPU utilization issues increases
Solution Approach 1:
The system incorporates feedback mechanisms where the throttling rate controller continuously monitors queue lengths, memory usage, and CPU utilization. Based on this feedback, the controller dynamically adjusts the throttling rate to maintain optimal processing capacity while preventing memory depletion and CPU overload, thereby preserving system reliability under high load conditions.
Solution Approach 2:
The system changes key parameters dynamically: token bucket capacity is adjusted based on available memory, queue sizes are modified according to arrival rates and service times, and throttling rates are tuned to match CPU capabilities. These parameter changes allow the system to maximize productivity while maintaining stability by adapting to current system state.
3Adaptability or versatility
If traditional network traffic flow logging techniques are used, then the implementation is straightforward, but the system fails to leverage modern advancements such as software-defined networks and resource virtualization
Solution Approach 1:
The logging system is designed with universal components that can operate across different network architectures including traditional hardware-based networks, software-defined networks, and virtualized environments. The token bucket and queue models are architecture-agnostic, allowing the system to adapt to various network types while maintaining consistent processing efficiency through standardized mechanisms.
Data Source
AI summary
In various embodiments, methods and systems for implementing network traffic flow logging in a distributed computing system are provided. At a high level, the network traffic flow logger is implemented using a network traffic flow logger framework that includes a plurality of modular network traffic flow logging objects for adjustable processing of network flow events. In operation, a plurality of tokens is counted at a first throttling rate. The first throttling rate is associated with a maximum number of tokens threshold. The maximum number of tokens threshold is an adjustable threshold. A flow event is accessed, where flow events are quantified based on tokens. The flow event is queued, based on a count of the plurality of tokens or the maximum number of tokens threshold. Based on queueing the flow event, the first throttling rate is adjusted to a second throttling rate based on a queue length of the queue.