Multi-Cluster Task Provisioning by Priority and Geographic Proximity
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Solution Overview
Problem
Traditional cloud-based task provisioning systems face limitations in parallel task execution due to the number of pods within a cluster and fail to account for geographical proximity, leading to increased latency and inefficient resource allocation.
Innovation Solution
A method for provisioning tasks across multiple clusters based on a priority-based backlog queue and a proximity-based allocation process, which involves identifying network element locations, geographic cluster locations, and prioritizing the nearest cluster for task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If round robin allocation is used for task provisioning, then task allocation is simple to implement, but task execution speed and resource utilization efficiency deteriorate due to failure to account for geographic proximity
Solution Approach 1:
The patent changes the allocation parameter from simple round-robin indexing to geographic proximity-based selection. The system calculates distance metrics between task locations and cluster locations, then provisions tasks to the nearest available cluster. This parameter change resolves the contradiction by maintaining allocation simplicity through automated distance calculation while dramatically improving task execution speed through geographic optimization.
Solution Approach 2:
The patent introduces a geographic dimension to the task allocation process. Instead of allocating tasks based solely on cluster availability indices, the system incorporates spatial coordinates and calculates geographic distances. This additional dimensional consideration enables the system to provision tasks to geographically nearest clusters, improving execution speed without sacrificing allocation simplicity.
2Device complexity
If tasks are provisioned without considering geographic proximity, then allocation process is simple, but system latency increases due to distant cluster connections
Solution Approach 1:
The patent changes the provisioning parameter from arbitrary cluster selection to geographic proximity-based selection. By calculating and comparing distance metrics between task locations and cluster locations, the system automatically selects the nearest cluster. This resolves the contradiction by keeping the allocation process computationally simple while dramatically reducing system latency through geographic optimization.
Solution Approach 2:
The patent performs preliminary geographic location identification and distance calculation before task provisioning. The system pre-processes location data for both tasks and clusters, then uses this pre-computed information to rapidly determine the nearest cluster. This preliminary action reduces real-time provisioning complexity while minimizing latency by ensuring optimal cluster selection.
3Productivity
If traditional cluster provisioning is used, then resource allocation follows first-come-first-served, but resource utilization efficiency deteriorates due to inability to distribute tasks across multiple clusters
Solution Approach 1:
The patent segments the task provisioning process into distinct components: geographic location identification, distance calculation, and cluster selection. By dividing the provisioning logic into these manageable segments, the system can effectively distribute tasks across multiple clusters while maintaining manageable complexity. Each segment handles a specific aspect of the provisioning decision, making the overall multi-cluster management process systematic and efficient.
4Loss of time
If geographic proximity is considered in task provisioning, then task execution latency is reduced, but provisioning process complexity increases
Solution Approach 1:
The patent changes the provisioning parameter from simple cluster availability to geographic distance metric. By calculating and using distance as the primary selection criterion, the system reduces task execution latency while keeping the provisioning process relatively simple. The distance calculation is a straightforward mathematical operation that adds minimal complexity while delivering significant latency reduction benefits.
Data Source
AI summary
Systems and methods for multi-cluster worker management for speed and proximity use cases. A method includes providing a plurality of tasks to a priority-based backlog queue and provisioning each of the plurality of tasks to one of a plurality of clusters. Provisioning each of the plurality of tasks comprises provisioning based on a proximity-based allocation process. The proximity-based allocation process includes identifying a network element location associated with each of the plurality of tasks, identifying a geographic location for each of the plurality of clusters, and prioritizing a nearest cluster of the plurality of clusters.