Discovery Scheduling
Effective discovery scheduling ensures your CMDB stays current while minimizing impact on network and system resources. Tripl-i provides flexible scheduling options that adapt to your infrastructure's needs and operational windows.
Scheduling Architecture
Scheduling Engine
Schedule Types
Fixed Schedules
Fixed schedules run on a predictable, recurring basis. You can configure these common patterns:
| Schedule Name | Frequency | Targets | Scan Type | Max Duration |
|---|---|---|---|---|
| Daily Discovery | Every day at 2:00 AM | All infrastructure | Incremental | 4 hours |
| Weekly Full Scan | Every Sunday at 6:00 AM | All infrastructure | Full | 12 hours |
| Hourly Critical | Every hour | Assets tagged "critical" or "production" | Incremental | 45 minutes |
Dynamic Schedules
Dynamic schedules respond to events and changes in your environment rather than running on a fixed clock.
-
Event-Driven Triggers -- Discovery runs automatically when specific events occur:
- A new device is detected on the network
- A configuration change is recorded
- An incident is created
- A deployment completes
- After a short delay (typically 5 minutes), a targeted scan runs against only the affected items
-
Change-Based Triggers -- Discovery integrates with your change management process:
- Monitors your deployment pipeline, change calendar, and maintenance windows
- Performs a baseline scan one hour before a scheduled change
- Runs a verification scan 30 minutes after the change completes
Schedule Configuration
Basic scheduling
When creating a schedule, you configure the following key settings:
- Name and description -- Give your schedule a clear, descriptive name (for example, "Production Server Discovery") and a summary of what it covers.
- Enabled/Disabled -- Toggle the schedule on or off at any time.
- Timing
- Choose a frequency (every hour, every 4 hours, daily, weekly, and so on).
- Set the start time and timezone.
- Define blackout windows to prevent discovery during peak hours. For example, block weekday mornings from 8:00 AM to 9:00 AM to avoid scanning during peak business activity.
- Targets
- Include specific IP ranges (such as 10.1.0.0/16) or assets with certain tags (such as "production" or "critical").
- Exclude individual IPs or assets tagged for maintenance.
- Discovery options
- Select the scan type (incremental or full).
- Choose discovery methods (agent-based, WMI, SSH).
- Set a per-target timeout and the number of parallel jobs.
Advanced scheduling
Intelligent Scheduling
Tripl-i can adjust discovery frequency automatically based on several factors:
By Business Criticality
| Criticality Level | Frequency | Preferred Method |
|---|---|---|
| Critical | Real-time (continuous) | Agent only |
| High | Every 2 hours | Agent preferred |
| Medium | Every 12 hours | Agentless acceptable |
| Low | Daily | Any method |
By Device Type
| Device Type | Frequency | Notes |
|---|---|---|
| Database servers | Every hour | Preferred window: 2:00 AM -- 5:00 AM |
| Web servers | Every 4 hours | Avoid scanning during 9:00 AM -- 5:00 PM |
| Workstations | On user login | Maximum 2 scans per day |
By Change Frequency
| Change Rate | Threshold | Discovery Frequency |
|---|---|---|
| High change | More than 10 changes per week | Every 2 hours |
| Moderate change | 3 to 10 changes per week | Every 6 hours |
| Stable | Fewer than 3 changes per week | Daily |
Resource-Aware Scheduling
Set resource limits to prevent discovery from overwhelming your infrastructure:
- Global limits -- Control the maximum number of concurrent discoveries (for example, 50), total network bandwidth consumed, and CPU/memory usage thresholds.
- Per-target limits -- Restrict the number of simultaneous connections to any single target (for example, 5), cap bandwidth per target, and configure retry behavior with exponential backoff.
- Adaptive throttling -- When system load exceeds a defined threshold (for example, 80%), Tripl-i automatically reduces concurrency by half and increases scan intervals until load returns to normal.
- Priority queues -- Reserve execution slots for different priority levels:
| Priority | Reserved Slots | Maximum Wait Time |
|---|---|---|
| Critical | 20 | 5 minutes |
| High | 15 | 15 minutes |
| Normal | 10 | 1 hour |
| Low | 5 | 4 hours |
Scheduling Strategies
Infrastructure-based scheduling
Geographic Distribution
For multi-region environments, schedule discovery within each region's local off-peak window:
| Region | Discovery Window | Stagger Interval |
|---|---|---|
| US East | 2:00 AM -- 6:00 AM EST | 15 minutes between batches |
| US West | 2:00 AM -- 6:00 AM PST | 15 minutes between batches |
| Europe | 2:00 AM -- 6:00 AM CET | 20 minutes between batches |
| Asia | 2:00 AM -- 6:00 AM JST | 20 minutes between batches |
Network Topology
Align discovery frequency with the importance of each network layer:
| Network Layer | Frequency | Priority |
|---|---|---|
| Core | Every 30 minutes | Critical |
| Distribution | Every 2 hours | High |
| Access | Every 6 hours | Normal |
| Edge | Daily | Low |
Service Dependencies
Discover higher-tier services first to build dependency context for downstream tiers:
- Tier 1 services -- Discovered first, on a continuous basis
- Tier 2 services -- Discovered after Tier 1 completes, every hour
- Tier 3 services -- Discovered after Tier 2 completes, every 4 hours
Business-aligned scheduling
Maintenance Windows
Integrate with your change management system to respect scheduled blackouts:
- Import maintenance windows automatically from your change management tool.
- Run a pre-maintenance baseline scan one hour before the window opens.
- Run a post-maintenance verification scan 30 minutes after the window closes.
Business Cycles
Adjust discovery intensity around critical business periods:
| Business Period | Dates | Discovery Adjustment |
|---|---|---|
| End of month | 28th -- 31st | Reduce discovery by 75%; run priority scans only |
| Quarter end | Last week of March, June, September, December | Minimal discovery; defer non-critical scans |
| Year end / holidays | December 24 -- January 2 | Emergency scans only; manual approval required |
SLA-Driven Scheduling
Match discovery frequency to service level commitments:
| SLA Tier | Discovery Interval | Availability Requirement |
|---|---|---|
| Platinum | Every 15 minutes | 99.99% |
| Gold | Every hour | 99.9% |
| Silver | Every 4 hours | 99% |
Schedule Management
Web UI management
The Schedule Dashboard provides several views for managing your discovery schedules:
Dashboard Views
- Calendar view -- A visual timeline showing when each schedule runs
- List view -- A tabular layout with detailed schedule properties
- Gantt chart -- A resource utilization view showing overlap and capacity
- Heat map -- A density view highlighting when most discovery activity occurs
Available Actions
- Create new schedules or edit existing ones
- Enable or disable individual schedules
- Trigger a schedule to run immediately with the "Run Now" option
- Skip the next scheduled run
- View full execution history for any schedule
- Clone an existing schedule to create a similar one quickly
Monitoring at a Glance
The dashboard surfaces key information in real time:
- Next scheduled run times
- Currently running discoveries and their progress
- Success and failure rates over time
- Average scan duration
- Current resource usage
Schedule management through the platform
You can also manage schedules through the Tripl-i platform interface:
- Create a schedule by specifying a name, description, frequency (such as every 4 hours), timezone, target tags (such as "database" and "production"), scan type, timeout, parallelism, and retry behavior.
- Trigger an immediate run for any existing schedule.
- Review statistics for a schedule over a given period, including success rate and average duration.
Schedule Optimization
Performance analysis
Tripl-i continuously monitors schedule performance and generates actionable recommendations:
Key Metrics Tracked
- Completion time per schedule
- Success rate
- Resource usage during scans
- Job queue depth
- Wait time before execution
Example Optimization Recommendations
| Finding | Recommendation | Expected Impact |
|---|---|---|
| Schedules A and B overlap by 45% | Stagger the two schedules by 2 hours | Reduce resource contention by 40% |
| 2:00 AM -- 4:00 AM window is only 20% utilized | Move low-priority discoveries into this window | Better resource distribution |
| Full scan takes more than 6 hours | Split into regional sub-schedules | Reduce completion time by 60% |
Adaptive scheduling
The adaptive scheduling engine uses historical performance data to continuously improve schedule efficiency:
- Analyze -- Reviews past execution data to identify peak usage times, optimal windows, and bottlenecks.
- Adjust -- When bottlenecks are detected (such as network congestion), the system automatically reduces parallelism and caps bandwidth. If a schedule's start time falls during a peak usage period, it shifts to the next available optimal window.
- Learn -- A feedback loop tracks each execution's performance and success rate, refining future scheduling decisions over time.
Monitoring and Alerting
Schedule monitoring
Schedule Overview Dashboard
The overview dashboard includes widgets for:
- Upcoming scheduled runs
- Currently running discoveries
- Recent failures
- Resource utilization
- SLA compliance status
Performance Metrics Dashboard
The performance dashboard provides deeper analysis through:
- Completion time trends
- Success rate gauges
- Discovery coverage maps
- Queue depth charts
- Bottleneck analysis
Key Performance Indicators
| KPI | Target |
|---|---|
| Discovery coverage | Greater than 95% |
| Success rate | Greater than 98% |
| Average completion time | Less than 30 minutes |
| Resource utilization | 60% -- 80% |
| Schedule adherence | Greater than 95% |
Alert configuration
Configure alerts to stay informed about schedule health:
Schedule Failures
- Triggers when more than 2 consecutive failures occur
- Severity: High
- Notifications sent to the operations team via email and messaging channels
- Automatic actions: retry with backoff and create an incident ticket
Long-Running Discoveries
- Triggers when duration exceeds twice the expected time
- Severity: Medium
- Notifications sent to the discovery administrator
- Automatic actions: check resource usage and throttle if needed
Missed Schedules
- Triggers when any scheduled run is missed
- Severity: High
- Notifications sent to the on-call team via SMS and email
- Automatic actions: run immediately and investigate the root cause
Best Practices
1. Schedule design
- Align discovery windows with off-peak business hours
- Account for geographic distribution across time zones
- Respect maintenance windows defined in your change management process
- Plan schedule capacity for infrastructure growth
2. Resource management
- Monitor resource consumption during active discoveries
- Implement throttling to prevent network saturation
- Use priority queues to ensure critical assets are always scanned on time
- Balance load distribution across available discovery workers
3. Reliability
- Build in redundancy so that a single failure does not cascade
- Handle failures gracefully with automatic retries and backoff
- Configure retry logic with sensible limits (for example, 3 retries with exponential delay)
- Monitor success rates and investigate any downward trends promptly
4. Optimization
- Conduct regular performance reviews of all active schedules
- Adjust frequencies and windows based on collected metrics
- Eliminate redundant or overlapping schedules
- Treat scheduling as a continuous improvement process
Troubleshooting
Common issues
Schedules Not Running
If a schedule fails to execute, check the following:
- Verify that the schedule is enabled (not paused or disabled).
- Confirm that the schedule expression (frequency and timing) is valid.
- Check whether a blackout window is currently active.
- Review system resource availability -- resource limits may have been reached.
- Confirm that the scheduler service itself is running and healthy.
Common root causes include an accidentally disabled schedule, an invalid schedule configuration, an active blackout window, resource limits being exceeded, or the scheduler service being down.
Performance Degradation
If you notice increasing completion times, high resource usage, queue buildup, or timeout errors:
- Reduce the number of parallel jobs per schedule
- Increase the interval between scan runs
- Narrow the discovery scope (fewer targets per schedule)
- Add more discovery workers to distribute load
- Enable caching to avoid redundant scans of unchanged assets
Schedule analysis
Use the Schedule Dashboard to review performance over a defined period (such as the last 30 days). The analysis includes:
- Average duration per schedule
- Total number of runs
- Successful versus failed runs
- Overall success rate
Sort by lowest success rate and longest average duration to quickly identify the schedules that need attention.
Advanced Topics
Multi-site scheduling
For organizations with multiple data centers or offices, Tripl-i supports distributed scheduling:
- Local-first discovery -- Each site runs discovery locally using agents deployed on-premises.
- Central aggregation -- Results from all sites are aggregated to the central CMDB.
- Deduplication -- Overlapping results across sites are automatically deduplicated.
- Synchronization -- Data syncs to the central platform upon completion of each local scan.
Configure each site with its timezone and available bandwidth to the central system so that the scheduler can optimize timing and data transfer.
| Site | Timezone | Bandwidth to Central |
|---|---|---|
| Datacenter East | America/New_York | 1 Gbps |
| Datacenter West | America/Los_Angeles | 1 Gbps |
| Europe DC | Europe/London | 500 Mbps |
Predictive scheduling
Tripl-i uses machine learning to optimize scheduling decisions over time. The predictive engine:
- Analyzes historical discovery data including time of day, day of week, target count, and scan type
- Predicts how long a new or modified schedule will take to complete
- Recommends the optimal start time within your preferred maintenance window
- Continuously refines its predictions as more execution data becomes available
This allows you to plan schedules with confidence, knowing that the system accounts for real-world performance patterns rather than relying on static estimates.
Next Steps
- Troubleshooting - Common issues and solutions
- Best Practices - CMDB best practices
- Performance Tuning - System optimization