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BOF Discussion: Uploading IEPM-BW data to MonALISA

BOF Discussion: Uploading IEPM-BW data to MonALISA. Connie Logg SLAC Winter 2006 ESCC / Internet2 Joint Techs Workshop February 4-9, 2006. Overview. Several discussions about uploading IEPM-BW data and alerts to MonALISA Consider: What parts of data (varies with probe type)

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BOF Discussion: Uploading IEPM-BW data to MonALISA

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  1. BOF Discussion: Uploading IEPM-BW data to MonALISA Connie Logg SLAC Winter 2006 ESCC/Internet2 Joint Techs Workshop February 4-9, 2006

  2. Overview Several discussions about uploading IEPM-BW data and alerts to MonALISA Consider: • What parts of data (varies with probe type) • Mechanism (every n minutes or as data is taken) • What parts of ALERTS?

  3. Data Fields Vary with type of probe • PING – RTT (min, avg, max), Packets sent, Packets Received, Packet order (probably not) • ? Traceroute – list of IP addresses in the traceroute

  4. Data Fields - Continued • IPERF - #streams - Window size, throughput achieved. With incremental reporting are the thruput min, max, avg and std dev of incremental values of interest ? (I do not think so)

  5. Data Fields - Continued • THRULAY TCP: total thruput; RTT min, max, and avg. With incremental reporting are the thruput min, max, avg and std dev of incremental values of interest? (I do not think so) • PATHCHIRP: achievable bandwidth; min, avg, max of chirping steps - NO

  6. Data Fields - Continued • PATHLOAD – min and max of thruput range ? CAVEATS: Only upload data from relevant and quality probes – for example Pathchirp works well on some paths, and not so well on others.

  7. ALERTS IEPM-BW has an algorithm for auto detection of bandwidth drops. Need to choose only alerts on relevant and quality probes Do not flood MonALISA with every alert that is detected.

  8. ALERT Mechanism • Baseline data – in ‘history’ buffer – has a mean and std dev • Data less than (mean – 2 std dev) is put in ‘trigger’ buffer for examination • Trigger buffer also has mean and std dev. • When trigger buffer is full and (history_mean – trigger_mean)/history_mean meets or exceeds the drop threshold, an ALERT is generated.

  9. ALERTS – Info to Upload • Baseline mean and std dev of ‘history’ buffer before drop • Date and time when drop is detected in ‘trigger’ buffer • Percentage of drop from history mean to trigger mean • Mean and std dev of ‘trigger’ buffer • Values in Trigger buffer (probably not)

  10. Guidance/Input Needed • What probes, how frequent, and length of history and trigger buffers • What probe data to upload to MonALISA • What alert information to upload to MonALISA • How to upload information to MonALISA • Other thoughts, comments and suggestions

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