Intelligent  Placement  of  Datacenters  for  Internet  Services  EEDC-­‐34330  HW  #6  Faik  Aras  Tarhan  faik.aras.tarhan@ac.upc.edu  

Introduction  •  Popular  Internet  companies,  such  as  Google,  Yahoo,  and  MicrosoJ  offer  a  range  of  services  hosted:    •  in  datacenters  containing  thousands  of  servers  and  infrastructures  

•  by  mulPple  geographically  distributed  datacenters                                  

Apple  Data  Center,  North  Carolina    

Introduction  •  The  locaPon  of  datacenters  has  a  direct  impact  on:  •  the  services’  response  Pmes  •  capital    •  operaPonal  costs  •  (indirect)  carbon  dioxide  emissions  

Introduction  •  SelecPng  a  locaPon  involves  many  important  consideraPons  such  as:    •  its  proximity  to  populaPon  centers  •  power  plants  •  network  backbones  •  the  source  of  the  electricity  in  the  region  •  the  electricity,  land,  and  water  prices  at  the  locaPon  •  the  average  temperatures  at  the  locaPon  

Paper’s  Contribution  •  a  framework  and  opPmizaPon  problem  for  selecPng  datacenter  locaPons  

•  soluPon  approaches  for  the  problem  •  characterize  areas  across  the  US  as  potenPal  locaPons  for  datacenters  

Framework  Variables  •  Costs  •  CAPEX  •  OPEX  

•  Response  Pme  •  it  is  criPcal  to  model  the  network  latency  between  the  service’s  potenPal  users  and  the  potenPal  locaPons  

•  Consistency  delay  •  the  Pme  required  for  state  changes  to  reach  all  mirrors  

•  Availability  •  redundant  components  in  each  path  increases  availability  

Formulation  •  Minimize  total  cost  subject  to;  •  no  user  should  experience  higher  latency  than  MAXLAT  •  consistency  should  take  no  longer  than  MAXDELAY  •  availability  must  be  at  least  MINAVAIL  •  the  total  number  of  servers  is  no  greater  than  MaxS  •  must  provision  enough  servers  for  every  populaPon  center  •  no  datacenter  can  host  more  servers  than  its  max  capacity  

Solution  Approaches  •  the  problem  is  non-­‐linear    •  it  is  not  directly  solvable  by  linear  prog.  (LP)  solvers  •  approaches  that  use  LP  to  different  extents  were  discussed  in  the  paper  

•  Simple  linear  programming  (LP0)  •  Due  to  its  simplificaPons  and  restricPveness,  it  may  produce  higher  total  cost  for  a  datacenter  network    

•  Pre-­‐set  linear  programming  (LP1)  •  As  it  requires  a  previously  selected  set  of  datacenters,  this  approach  cannot  be  used  by  itself  

Solution  Approaches  •  Brute  force  (Brute)  •  tests  each  of  possible  combinaPon  using  the  LP1  approach  and  returns  the  best  one  

•  Heuris;c  based  on  LP  (Heuris;c)  •  selects  the  most  popular  locaPons  from  the  shorter  ranked  list  of  configuraPons  and  runs  brute  force  on  them  

•  Simulated  annealing  plus  LP1  (SA+LP1)  •  SA  is  a  generic  probabilisPc  meta-­‐heurisPc  for  non-­‐linear  opPmizaPon  problems  

•  Op;mized  SA+LP1  (OSA+LP1)  •  speeds  up  the  opPmizaPon  process  because  it  drives  toward  the  lowest  cost  configuraPon  faster  

Input  Data  •  They  are  received  from  ISP  backbones,  the  Department  of  Energy  (DOE)  ,  some  insPtuPons  an  web  sites  

•  In  the  case  of  missing  data  the  tool  uses  informaPon  from  the  closest  neighboring  locaPon  for  which  it  has  the  needed  data  

Evaluations  •  OSA+LP1  finds  the  opPmal  soluPon  in  all  cases  •  LP0  exhibits  the  worst  behavior  •  Brute  exhibits  very  high  running  Pmes  •  OSA+LP1  provides  the  best  tradeoff  between  running  Pme  and  search  quality  

•  It  achieves  opPmal  results  with  the  second  lowest  execuPon  Pmes  

Evaluations  

Conclusions  •  The  automaPc  placement  of  datacenters  for  Internet  services  was  considered.    

•  An  opPmizaPon  framework  for  the  problem  and  many  soluPons  approaches  were  proposed.  

•  Different  US  regions  as  potenPal  locaPons  for  datacenters.    •  Compared  the  soluPon  approaches  illustrated  many  tradeoffs.    •  The  intelligent  placement  of  datacenters  can  save  millions  of  dollars