Service Chain Data Prioritization via Distributed Queue Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing volume of data transfer between remote and central computing sites in information technology infrastructure poses challenges in prioritizing data processing, leading to inefficiencies and delays, particularly in reactive data analysis and manual intervention, where timely and prioritized data transfer is crucial for proactive issue resolution.
Innovation Solution
Implementing data transfer prioritization logic that monitors processing queues, determines prioritization information, and selectively routes data through high or low priority queues based on metadata, allowing for dynamic adjustment of prioritization levels and limited duration elevated priority processing, utilizing credit-based management to self-serve prioritization without central authority intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data transfer prioritization is implemented across multiple services in a service chain, then data processing efficiency and timeliness are improved, but system complexity and overhead increase
Solution Approach 1:
The prioritization logic is segmented and distributed across multiple services in the service chain. Each service independently monitors its own processing queues and applies prioritization rules to data passing through it, rather than having a centralized control point. This segmentation allows the system to achieve comprehensive data prioritization while keeping individual service complexity manageable.
Solution Approach 2:
The system performs preliminary actions by pre-configuring prioritization rules and metadata associations with data before it enters the service chain. Data items are tagged with priority information upfront, allowing subsequent services to quickly determine queue placement without complex real-time analysis, thus improving efficiency while maintaining simplicity.
2Loss of time
If manual intervention and reactive data analysis are used, then system operation is simple, but data transfer delays and response time increase
Solution Approach 1:
The system implements self-service automation where data items automatically carry priority information through metadata, and each service in the chain autonomously determines queue placement based on this metadata. This eliminates the need for manual intervention in prioritization decisions while ensuring timely data transfer, as the system serves itself without human input.
Solution Approach 2:
The system incorporates feedback mechanisms where services monitor their processing queues and adjust data routing decisions based on current queue states and priority levels. This automated feedback loop ensures that critical data is consistently routed through high-priority queues, reducing transfer delays without requiring manual monitoring or intervention.
3Adaptability or versatility
If centralized authority controls data prioritization, then prioritization decisions are uniform, but system responsiveness and flexibility decrease
Solution Approach 1:
Centralized prioritization control is segmented into distributed decision-making across multiple services. Each service maintains autonomy to make prioritization decisions based on local queue conditions and data metadata, while still adhering to overall system priorities. This segmentation increases flexibility and adaptability while avoiding the complexity of centralized real-time control.
Solution Approach 2:
The system applies local quality by allowing each service to make prioritization decisions tailored to its specific context and queue conditions, rather than enforcing uniform centralized decisions. Each service can adapt prioritization behavior to local requirements while maintaining consistency through shared metadata standards, enhancing overall system flexibility.
Data Source
AI summary
An apparatus comprises at least one processing device configured to monitor, by a first service in a service chain, a first set of processing queues comprising two or more different processing queues associated with two or more different priority levels. The processing device is also configured to process, by the first service, a given portion of data stored in at least one of the two or more different processing queues in the first set of processing queues. The processing device is further configured to determine prioritization information associated with the given portion of the data and to select, based on the prioritization information, a given one of two or more different processing queues in a second set of processing queues associated with a second service in the service chain, and to store the given portion of the data in the given processing queue in the second set of processing queues.