Proactive Systemic Scheduler for Network Test Resource Allocation
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Solution Overview
Problem
Conventional scheduled software testing systems for wireless networks face inefficiencies due to limited physical resources, where tests often have to wait for resources, leading to skewed completion times and increased costs, especially as the number of tests and resources grows, without a feasible mathematical formula to optimize schedules within reasonable time.
Innovation Solution
A proactive systemic scheduler that automatically creates and selects schedules using genetic algorithms to minimize the overlap of test object allocation, ensuring that a given test object cannot be simultaneously allocated to more than one test, thereby optimizing resource usage and reducing idle time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional schedulers use historical completion times to determine test start times, then tests are scheduled based on past performance data, but resource overlap occurs causing tests to wait and completion times become skewed
Solution Approach 1:
The scheduler proactively allocates test objects to tests before the testing interval begins, based on predicted resource needs and historical data. This preliminary allocation prevents resource conflicts during execution, eliminating waiting time while maintaining high throughput by ensuring tests can immediately begin when scheduled.
Solution Approach 2:
The system continuously updates baseline completion times based on actual test results from previous intervals. This feedback loop refines the scheduler's predictions of resource duration and availability, enabling more accurate proactive allocation decisions that reduce both waiting time and optimize overall testing productivity.
2Reliability
If the resource manager secures all resources necessary for a test until completion, then resource allocation is simplified and conflicts are avoided, but resource utilization efficiency decreases due to idle time
Solution Approach 1:
Test objects are allocated to specific tests in advance, before the testing interval starts. This preliminary assignment ensures that each test has its resources reserved without holding them idle, as the allocation is time-bound and release-oriented. Resources are secured only for the predicted duration needed, maintaining reliability while improving utilization.
Solution Approach 2:
The resource allocation system dynamically adjusts resource holding duration based on actual test execution needs rather than static pre-allocation. Tests release resources upon completion or when no longer needed, allowing the system to adapt resource availability in real-time and reduce idle time while maintaining allocation reliability.
3Adaptability or versatility
If the number of tests and resources increases, then testing coverage improves, but scheduling complexity increases making optimization infeasible within reasonable time
Solution Approach 1:
The system transforms the complex scheduling problem into a simpler form by changing key parameters: using baseline completion times and predicted resource durations as fixed inputs, and applying a greedy allocation algorithm that processes tests in order of predicted completion. This parameter transformation reduces computational complexity from exponential to polynomial, enabling scalable scheduling for large numbers of tests and resources.
Solution Approach 2:
The scheduling process is segmented into distinct phases: baseline data collection, predicted completion time calculation, and sequential resource allocation. Each phase handles a specific aspect of the problem independently, breaking down the overall complexity into manageable segments that can be processed efficiently even as system size grows.
4Duration of action of moving object
If tests are started early in the testing interval based on historical duration, then longer tests are accommodated, but resource conflicts increase causing delays
Solution Approach 1:
The scheduler calculates predicted completion times and allocates resources in advance, before conflicts can occur. By proactively assigning test objects based on predicted duration and availability, the system ensures that tests starting early do not encounter resource conflicts, as the allocation has already been optimized to prevent overlaps.
Solution Approach 2:
The system uses feedback from actual test completion times to continuously refine predicted durations. This enables more accurate early scheduling decisions, as the baseline data becomes increasingly precise, allowing tests to be started early without causing conflicts since the predicted resource needs are more accurately forecasted.
Data Source
AI summary
An apparatus having a proactive systemic scheduler connected with a network testing system. The scheduler receives a list of tests to be run on the network test system during a testing interval, as well as available test baseline information for the tests to be run. Using a predetermined algorithm, the list of the tests, and the available test baseline information, the scheduler automatically creates a plurality of testing schedules, such that a given test object cannot be simultaneously allocated to more than one test. Each of the plurality of schedules indicates test object allocation for the testing interval. Next, the scheduler selects one of the plurality of testing schedules based on reducing an overlap of test object allocation for the testing interval. The scheduler then communicates the selected testing schedule to be implemented by the network test system.