APM project meeting - June 13, 2012 - LBNL, Berkeley, CA
- 1. Searching pa,erns in SNMP data Mehmet Balman and Doron Rotem
- 2. Searching pa,erns in SNMP data • Looked at two busy links: – star-‐sdn1/interface/xe-‐7_3_0 – denv-‐cr2/interface/xe-‐1_1_0 -‐Generated graphs for the last 6 months (including graphs per month, per week ) -‐ Visually inspected whether there is any Ime related specific pa,ern in bandwidth usage
- 3. Searching pa,erns in SNMP data – star-‐sdn1/interface/xe-‐7_3_0 star-‐>wash h6ps://sdm.lbl.gov/~balman/temp/1-‐a/ – denv-‐cr2/interface/xe-‐1_1_0 sunn-‐>denv h6ps://sdm.lbl.gov/~balman/temp/1/
- 4. Searching pa,erns in SNMP data • Collected data for those two links (one year long) and tried to analyze the data with a machine learning soMware • Converted data into arff format • Used Weka • Evaluated the bandwidth vs. Ime data (Ime series analysis) to see whether day of the week, PM or AM, day of the year, etc. have any visible effect on bandwidth usage
- 5. Sunn-‐>denv
- 6. Star-‐>wash
- 8. Searching pa,erns in SNMP data • Our iniIal results on Ime series predicIon gave 40-‐50% error rate. • By using some other techniques, we were able to achieve 30-‐40 % error rate. • At this moment, taking average link usage may be a reasonable way to start with. • Further study is required to make useful predicIons – Gretl is also another alternaIve – Using R instead of Weka