Dynamic Thread Pool Management for Cloud Integration Data Transfer
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Solution Overview
Problem
Customers using third-party cloud-based platforms like ServiceNow are unable to fully leverage the capabilities of infrastructure automation platforms like VMware Aria Automation due to resource limitations, such as concurrency and thread allocation, which are shared across various use cases.
Innovation Solution
A management node with an integration plugin that dynamically manages the number of threads allocated for data transfer between integrated products, adjusting based on the availability of idle worker threads to optimize resource utilization and improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If thread allocation for data transfer is increased to improve data transfer speed, then productivity improves, but device complexity increases due to resource management challenges
Solution Approach 1:
The patent implements dynamic thread allocation where the number of threads is adjusted based on real-time availability of worker threads in the cloud-based automation platform. The integration plugin monitors worker thread availability and dynamically modifies the thread pool size for data transfer operations, allowing the system to optimize between productivity and resource management complexity according to actual platform capacity.
2Device complexity
If fixed thread allocation is used to simplify resource management, then device complexity decreases, but productivity is limited due to inability to adapt to varying workloads
Solution Approach 1:
The integration plugin continuously monitors the availability of worker threads in the cloud-based automation platform and uses this feedback information to adjust the thread allocation for data transfer operations. This closed-loop feedback mechanism allows the system to automatically adapt thread numbers to current platform capacity, achieving both simplified management through automation and optimized productivity according to actual workload conditions.
3Productivity
If thread allocation is optimized for data transfer, then productivity improves, but reliability decreases due to potential resource exhaustion during peak loads
Solution Approach 1:
The system changes the parameter of thread allocation dynamically based on the operational state of the cloud-based automation platform. When worker thread availability indicates lower platform capacity, the integration plugin reduces the number of threads for data transfer operations. This parameter adjustment prevents resource exhaustion during peak loads while maintaining high productivity during periods of adequate resource availability, thus balancing productivity and reliability.
Data Source
AI summary
An example method may include executing, using an integration plugin installed on a first integrated product running in a first management node, a first schedule job to assess the first management node for a specified period of time or for a specified number of assessments. Further, a check is made to determine, using the integration plugin, whether a thread in a thread pool of the first management node is idle after the specified period of time or the specified number of assessments. Based on the whether the thread is idle, a number of threads allocated for data transfer between a second management node executing a second integrated product and the first management node may be altered using the integration plugin. Based on the altered number of threads, the data transfer between the second management node and the first management node may be performed using the integration plugin.