Dynamic Event Queue Balancing for Mobile Data Streams
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Solution Overview
Problem
Managing high volumes of mobile communication device events in wireless communication networks is challenging due to varying event flows, requiring dynamic scaling of event queues and processing threads to maintain efficient resource allocation and prevent overload or inefficiency.
Innovation Solution
Implementing a system that dynamically instantiates and adjusts the number of event queues and processing threads based on predicted event volumes and queue depth thresholds, using event stream prediction models and balancing rules to reflect application layer conditions, allowing for proactive scaling and load rebalancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of event queues and processing threads is increased to handle high event volumes, then processing capacity and reliability are improved, but resource allocation efficiency and system complexity worsen
Solution Approach 1:
The patent implements dynamic scaling of event queues and processing threads based on real-time event volume monitoring and predictive modeling. The system automatically adjusts the number of queues and threads up or down according to predicted event inflows, transforming a static configuration into a dynamic one that adapts to changing loads, thereby maintaining high processing capacity while optimizing resource allocation efficiency.
Solution Approach 2:
The system uses predictive modeling to anticipate future event volumes before they occur. By analyzing historical event patterns and predicting upcoming event inflows, the system proactively scales resources in advance of actual load increases, ensuring processing capacity is ready before demand arises, rather than reacting after overload occurs.
2Device complexity
If static resource allocation is used to simplify system management, then device complexity is reduced, but productivity and adaptability to varying event flows worsen
Solution Approach 1:
The system implements self-service automation where the event processing platform automatically monitors event queue depths, predicts future event volumes using predictive models, and adjusts the number of event queues and processing threads without manual intervention. This autonomous resource management maintains low operational complexity while achieving high processing efficiency through continuous adaptation to varying event flows.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor actual event queue depths and processing performance. This real-time feedback is fed into predictive models that adjust resource allocation predictions, creating a closed-loop control system that automatically optimizes processing efficiency while maintaining simple system management through automation.
3Reliability
If resources are over-provisioned to prevent overload, then reliability is improved, but resource allocation efficiency and energy consumption worsen
Solution Approach 1:
The predictive modeling system analyzes historical event patterns and timing to forecast future event inflows. By predicting when event volumes will increase, the system provisions processing resources in advance of actual demand spikes, ensuring system stability and preventing overload without requiring permanent over-provisioning of resources that would waste energy during low-utilization periods.
Solution Approach 2:
The system dynamically adjusts resource allocation based on real-time event queue monitoring and predictive model outputs. When events are detected or predicted, processing threads and queues are activated or scaled up; when event volumes decrease, resources are scaled down. This dynamic behavior maintains system stability during high loads while optimizing energy efficiency during low-utilization periods.
Data Source
AI summary
A method of processing a stream of mobile communication device data events. The method comprises determining by a script executing on a computer system a number of events on a first number of data event queues, where the events are mobile communication device data events waiting to be processed and the first number of data event queues are associated with a first topic, comparing the number of events on the first number of queues to a predefined queue depth threshold associated, based on the comparison, creating additional queues associated with the first topic to establish a second number of queues associated with the first topic, rebalancing the queues associated with the first topic by moving some of the events stored on the queues to the additional queues, and creating additional event processing threads based on creating the additional queues.