Labor management timesheet sync fails between on-premise workers and cloud with SSL timeout

MES AVEVA MES
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1

We’re running AVEVA MES 2021.2 in a hybrid deployment where shop floor terminals are on-premise but the labor-mgmt module runs in Azure cloud. Timesheet sync is failing intermittently with SSL handshake timeouts after 50 seconds.

The error appears in logs:


SSLHandshakeException: Remote host closed connection
at TimesheetSyncService.pushData(line 234)
Retry attempt 3/3 failed - abandoning sync

We’ve tried increasing timeout values but that didn’t help. The message queue seems to be dropping timesheet entries during peak shift changes when 200+ workers clock in simultaneously. We need offline caching and proper retry logic, but not sure how to implement it with the cloud architecture. Labor cost tracking is now 15% inaccurate due to missing timesheet data.

2

Check your firewall rules between on-premise and Azure. We had identical symptoms and discovered our corporate proxy was terminating SSL connections after 60 seconds during high traffic periods. Adding the AVEVA MES cloud endpoints to the proxy whitelist and enabling SSL passthrough resolved it immediately. Also verify your Azure NSG rules allow persistent connections.

3

I’ve seen similar SSL timeout issues in hybrid deployments. The 50-second timeout suggests your network retry logic isn’t configured properly. Have you checked if the message queue has proper acknowledgment settings? When 200+ workers hit the system simultaneously, you need batch processing rather than individual sync calls.

4

The SSL certificate validation is likely timing out because your on-premise terminals can’t reach the certificate authority during peak loads. I’d recommend implementing a local certificate cache and adjusting your retry strategy. Instead of 3 retries with fixed intervals, use exponential backoff starting at 5 seconds. Also, your message queue should have a dead letter exchange configured to capture failed syncs for later processing. This way you don’t lose timesheet data even when cloud connectivity drops completely.

5

Your architecture needs an offline-first approach. Implement local SQLite caching on the shop floor terminals to store timesheet entries when cloud sync fails. The sync service should poll this cache every 2 minutes and attempt batch uploads. For the SSL issues, verify your Azure Application Gateway has proper health probe configuration and that your backend pool timeout matches your application timeout settings. We had success setting both to 180 seconds in similar scenarios.

6

Here’s a comprehensive solution that addresses all the architectural issues:

1. Message Queue Implementation Configure RabbitMQ with durable queues and persistent messages. Set prefetch count to 50 to handle batch processing:


channel.basicQos(50);
queue.setDurable(true);
message.setDeliveryMode(2);

2. Offline Caching Strategy Implement a three-tier caching approach: Terminal → Edge Gateway → Cloud. The edge gateway should run a lightweight container with Redis cache that aggregates timesheet entries and syncs every 5 minutes or when 100 entries accumulate, whichever comes first.

3. Network Retry Logic Replace your fixed retry attempts with exponential backoff and circuit breaker pattern:


retryPolicy.maxAttempts(10)
  .backoff(5000, 120000, 2.0)
  .circuitBreaker(failureThreshold=5, timeout=60s)

4. SSL Certificate Validation The root cause is likely certificate chain validation timing out. Implement local certificate caching on your edge gateway and configure certificate pinning to avoid repeated CA lookups. Update your SSL context to cache sessions:


SSLContext.setSessionTimeout(7200);
SSLContext.setSessionCacheSize(100);

Architecture Flow:

  • Shop floor terminals write to local SQLite immediately (sub-100ms response)
  • Edge gateway polls terminals every 30 seconds, aggregates data
  • Gateway pushes batches to Azure Service Bus (not direct REST calls)
  • Cloud labor-mgmt module consumes from Service Bus with competing consumers pattern
  • Failed messages route to dead letter queue for manual reconciliation

Additional Configuration:

  • Set Azure Application Gateway backend timeout to 180 seconds
  • Enable Connection Draining with 120 second drain period
  • Configure health probes every 30 seconds with 3 retry attempts
  • Implement Azure Front Door for SSL termination and caching

Monitoring: Add Application Insights custom metrics for sync latency, queue depth, and cache hit rates. Set alerts for queue depth > 500 or sync latency > 60 seconds.

This architecture handles 500+ simultaneous workers and maintains 99.9% timesheet accuracy even during complete cloud outages up to 4 hours. The offline cache automatically reconciles when connectivity restores.

7

Thanks for the suggestions. We implemented the local SQLite cache and adjusted firewall rules. The SSL passthrough helped but we’re still seeing occasional failures during shift changes. The offline caching is working now at least.

8

We haven’t configured batch processing yet. The message queue is using default RabbitMQ settings. Should we be looking at implementing an edge gateway on-premise to handle the caching locally before syncing to cloud?