Configuration - Scheduled Tasks


The scheduled tasks settings determine how the scheduled reports are generated, archiving of event log entries, how the data store is maintained and processed, the summarisation of performance data, and control how database and file data is moved off the server, either by FTP or e-mail.

Status Reports

One or more report sections can be periodically e-mailed to one or more e-mail addresses if required.

Data Summaries

It is typically necessary to store performance data for long periods of time, either for regulatory compliance or for ad-hoc analysis and reporting using ServerAssist's built-in capabilities or third-party tools. ServerAssist can periodically summarise performance data, making high-level analysis of long term performance trends easier.

Event Archiving

It may be necessary to store certain data for long periods of time, either for regulatory compliance or for ad-hoc analysis and reporting using third-party tools. ServerAssist can store all events logged to the event log in its data store, making analysis of long term performance trends easier.

Note that ServerAssist will log all events, even those logged while the Monitor service is not running, provided that no more than 24 hours has elapsed since the Monitor service was last active.

Data Store Maintenance

Not all data stored in the data store is relevant, and ServerAssist can automatically perform housekeeping on unwanted data, reducing the overall size of the data store.

IIS Logs

It can be difficult to analyse web and FTP server logs in situ on a remote publishing server, which typically is located outside of the core corporate network. ServerAssist can move log files off a remote server where they can be processed as required on a local machine.

Application Data

ServerAssist can back up file based data or any OLE DB data source, moving it off the server either by transferring it to a separate FTP server, or sending it as an e-mail attachment to an e-mail address.

Data Store Optimisation

As data is changed in the internal data store, the underlying data structures and physical files can become fragmented, and performance can suffer. Data store optimisation removes unused space, rebuilds internal indices, and defragments the physical file.