Adaptive Discovery Process Scheduling for ITOM
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Solution Overview
Problem
Existing discovery processes in ITOM environments often lead to performance degradation and increased costs due to resource overutilization, as they are not adaptively scheduled based on current resource usage and requirements.
Innovation Solution
The implementation of adaptive discovery process scheduling, which involves monitoring network infrastructure and device metrics in real-time or at configured intervals, analyzing the impact of immediate execution, and determining optimal scheduling parameters using proportional, integral, and derivative factors, along with an increasing decay factor to prevent infinite rescheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If discovery processes are executed continuously without adaptive scheduling, then discovery completeness is improved, but system performance degrades and resource utilization becomes inefficient
Solution Approach 1:
The discovery process scheduling is made dynamic by continuously monitoring system metrics (CPU usage, memory availability, network load) and adjusting the execution timing of discovery processes based on real-time resource availability. This allows the system to maintain discovery completeness while adapting to changing system conditions to avoid performance degradation.
Solution Approach 2:
The system implements feedback mechanisms where discovered information about configuration items and their statuses is continuously fed back into the scheduling decision process. The scheduler uses this feedback to determine when to execute discovery processes, ensuring both completeness and optimal performance by leveraging actual system state information.
2Productivity
If discovery processes are delayed to reduce resource impact, then system performance is improved, but discovery timeliness deteriorates
Solution Approach 1:
The system changes scheduling parameters dynamically based on system conditions. When resources are abundant, discovery processes are scheduled immediately; when resources are constrained, processes are delayed. This parameter adaptation allows the system to balance performance and timeliness by adjusting execution timing based on real-time resource availability and discovery priorities.
3Productivity
If discovery processes are scheduled frequently, then discovery frequency is improved, but resource consumption increases
Solution Approach 1:
The system applies partial action by scheduling discovery processes selectively rather than continuously. Based on system metrics and discovered information, the scheduler determines the minimum necessary discovery frequency to maintain accuracy, avoiding excessive resource consumption while still providing sufficient discovery updates. This allows the system to achieve adequate discovery frequency without proportionally increasing resource usage.
Data Source
AI summary
Metrics that characterize one or more computing devices are received. A value associated with a performance of the one or more computing devices based on the received metrics is determined. A first scheduling parameter based on the value is determined, wherein the first scheduling parameter is associated with a first discovery process that is associated with at least a portion of the one or more computing devices. Execution of the first discovery process is directed according to the first scheduling parameter.