APM project meeting - June 13, 2012 - LBNL, Berkeley, CA

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APM project meeting - June 13, 2012 - LBNL, Berkeley, CA

  1. 1. Searching  pa,erns  in  SNMP  data   Mehmet  Balman  and  Doron  Rotem  
  2. 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. 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. 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. 5. Sunn-­‐>denv  
  6. 6. Star-­‐>wash  
  7. 7. 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  

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