Cloud	  computing	  research	  activities	  at	  Jordan	  University	  of	  Science	  and	  Technology	  	  Yaser	  Jararweh	  	  Jordan	  University	  of	  Science	  and	  Technology	  Irbid,	  Jordan	  yijararweh@just.edu.jo	  29/08/2013	  Yaser	  Jararweh:	  Bio	  sketch	   	   Assistant	   Professor,	   Computer	   Science	   dept.	   at	  Jordan	  University	  of	  Science	  and	  Technology.	  	
   PhD.	   The	   University	   of	   Arizona,	   Electrical	   and	  Computer	   Engineering	   Dept.	   “NSF	   Center	   of	   Cloud	  and	  Autonomic	  Computing	  (CAC)”,	  August	  2010.	
   Research	  Interest:	
   Cloud	  Computing	
   Data	  and	  Network	  Security	
   Software	  deﬁned	
  X	
   Data	  Centers	   Cognitive	  Radio	  Networks	  	  So2ware	  Tools	  for	  Cloud	  Compu;ng	  experiments	  and	  teaching	  	  (TeachCloud,	  CloudExp,	  and	  	  MCCSim).	  Main	  Problem	
   Cloud	  Computing	  needs	  professionals	  who	  are	  able	  to	  deploy,	  maintain,	  and	  advance	  the	  cloud	  technology.	
   The	  diversity	  of	  cloud	  computing	  related	  areas	  requires	  the	  students	  to	  put	  great	  eﬀorts	  to	  understand	  each	  one	  of	  these	  areas	  alone	  in	  addition	  to	  integrating	  them	  in	  a	  single	  platform.	
   There	  are	  no	  Teaching\Experimental	  tools	  to	  cover	  the	  diﬀerent	  aspects	  of	  cloud	  computing	  as	  a	  whole	  makes	  teaching	  more	  theoretical-­‐oriented,	  and	  therefore,	  less	  eﬀective.	  Main	  Problem	
   At	  Jordan	  University	  of	  Science	  and	  Technology	  (JUST),	  we	  were	  one	  of	  the	  ﬁrst	  universities	  in	  the	  MENA	  region	  to	  introduce	  cloud	  computing	  concepts	  in	  our	  courses	  for	  both	  graduate	  and	  undergraduate	  students.	  (2010)	  	
   At	  the	  end	  of	  the	  course,	  we	  conducted	
  a	  students'	  survey	  and	  asked	  the	  students	  to	  identify	  which	  challenges	  they	  thought	  were	  most	  important.	  	  students'	  survey	  Results	  Challenges in Teaching Cloud Computing as Identified by StudentsPossible	  Solu;ons	
   The	  use	  of	  real	  infrastructures	  such	  as	  EC2,	  IBM	  Smart	  Cloud,	  Azure,	  and	  on	  site	  cloud	  system.	  
Problem:	  	  Limited	  capabilities	  as	  a	  teaching	  tools,	  required	  some	  previous	  	  experience	  from	  the	  students,	  time	  consuming,	  and	  costly,	  etc.	
   Solution:	
   Simulation	
   What	  is	  available	
  :	  CloudSim,	  The	  University	  of	  Melbourne.	
   Problems:	  very	  limited,	  	  basic	  Cloud	  components	  modeling,	  hard	  to	  use	  in	  education	  and	  research	  (No	  GUI),	  No	  real	  workload	  Modeling,	  it	  lacks	  BPM	  and	  SLA	  components.	  	
  Our	  Solu;on:	  TeachCloud	
   TeachCloud	  uses	  CloudSim	  as	  the	  basic	  design	  platform	  and	  introduces	  many	  new	  enhancements	  on	  top	  of	  it.	
   Developing	
  a	  GUI	  for	  the	  toolkit.	  

 	  Adding	  the	  Rain	  cloud	  workload	  generator	  to	  the	  CloudSim	  simulator.	  
 	  Adding	  new	  modules	  related	  to	  SLA	  and	  BPM.	
   	  Adding	  new	  cloud	  network	  models	  (VL2,	  BCube,	  Portland,	  and	  DCell)	
   Introducing	  a	  monitoring	  outlet	  for	  most	  of	  the	  cloud	  system	  components.	  
 	  Adding	  an	  action	  module	  that	  enables	  students	  to	  reconﬁgure	  the	  cloud	  system	  and	  study	  the	  impact	  of	  such	  changes	  on	  the	  total	  system	  performance.	
  Creating data centers, VMs in TeachCloudSample of the simulation results in TeachCloudCloudExp	  and	  MCCSim	
   CloudExp:	  Cloud	  Experiments	   Provide	  an	  experimental	  framework	  for	  cloud	  computing	  researcher.	
   Very	  optimized	  to	  fully	  representing	  real	  cloud	  system.	
   Introduced	  new	  components	  such	  as:	  Map-­‐Reduce	  model	  	   Power	  	  management	  evaluation	
   Resource	  management	   MCCSim:	  Mobile	  Cloud	  Computing	  Simulator	
   Provide	  a	  comprehensive	  experimental	  platform	  for	  MCC	
   Still	  under	  development	  	  	  	  UMCM:	  Unobtrusive	  Mobile	  Crowd	  Monitoring	  	  Cloudlet-­‐based	  Approach	  	  UMCM	  Introduc;on Monitoring	  and	  managing	  of	  large	  size	  crowds	  is	  very	  challenging	  issue	  due	  to	  the	  following:	   	  The	  large	  amount	  of	  generated\collected	  data	   	  Data	  communication	   	  Data	  processing	  requirements	   	  Data	  security	  and	  crowd	  privacy	  issues.	  	
   The	  Unobtrusive	  Large	  Scale	  Crowd	  Monitoring	  (UMCM)	  system	  provide	  a	  viable	  solution	  to	  tackle	  the	  main	  challenges	  of	  crowed	  monitoring	  problem.	  Possible	  Scenarios Our	  proposed	  approach	  is	  general	  and	  is	  suitable	  for	  diﬀerent	  large	  size	  crowds	  scenarios	  such	  as	  but	  not	  limited	  to:	   	  Fireﬁghters	  on	  the	  ﬁeld.	  
 	  Soldiers	  on	  the	  battleﬁeld	   	  Patients	  in	  hospitals	   	  Patients	  in	  epidemically	  infected	  areas	   	  School	  students	   	  Babies	  in	  nursery	  school	  	
  UMCM	  Project	  Objec;ve: Support	  continues	  monitoring	  of	  large	  size	  crowds	  and	  collecting	  important	  information	  about	  their	  current	  status	  (e.g.	  vital	  sign,	  location,	  mobility	  	  and	  	  behavior	  trends)	
   Support	  mobile	  crowds	  monitoring	  in	  large	  scale	  geographical	  areas	  with	  diﬀerent	  terrain	  state.	
   Eﬃciently	  and	  cost-­‐eﬀectively	  handling	  the	  processing	  demands	  of	  large	  amount	  of	  generated	  data	  by	  the	  crowds.	
   Provide	  a	  set	  of	  policies	  and	  techniques	  that	  handle	  security	  and	  privacy	  issues.	
   Provide	  a	  complete	  and	
  a	  uniﬁed	  stack	  of	  software’s	  that	  eﬃciently	  handle	  diﬀerent	  tasks	  of	  the	  system	
   Inexpensive	  using	  available	  commodity	  and	  oﬀ	  the	  shelf	  materials	  that	  reduce	  cost.	
  UMCM	  Characteris;cs 	  UMCM	  provide	  unobtrusive	  data	  collection	  from	  the	  crowed	  members	  using	  smart	  wearable	  textiles	  with	  sensors.	
   UMCM	  is	  exploiting	  the	  current	  advancement	  in	  big	  data	  processing	  and	  cloud	  computing	  ﬁelds	  to	  eﬀectively	  handle	  the	  large	  amount	  of	  collected	  data	  from	  the	  crowed	  members.	  
 	  The	  processing	  of	  the	  collected	  data	  will	  include	  data	  reﬁnement,	  data	  analysis,	  and	  decision	  making.	  	
  UMCM	  Characteris;cs Security	  and	  crowed	  members	  privacy	  issues	  are	  on	  the	  core	  focus	  of	  UMCM	  system.	  	
   A	  complete	  set	  of	  policies	  were	  deployed	  to	  ensure	  data	  security	  and	  member’s	  privacy.	  	
   A	  novel	  feature	  of	  UMCM	  system	  is	  the	  supporting	  crowed	  mobility	  by	  ensuring	  continues	  connectivity	  between	  crowed	  members	  and	  data	  sink.	  	   	  All	  the	  aforementioned	  characteristics	  of	  UMCM	  system	  is	  developed	  using	  a	  uniﬁed	  stack	  of	  software	  solutionsUMCM:	  Cloud	  Compu;ng	  and	  Big	  data	  Side UMCM	  use	  the	  Cloudlet	  system	  which	  is	  a	  small	  size	  cloud	  system	  that	  provide	  contentious	  connectivity	  with	  mobile	  objects	  using	  wireless	  communication	  technology	  such	  as	  Wi-­‐Fi.	
   Cloudlet	  collects	  data	  from	  mobile	  objects,	  perform	  data	  processing	  and	  decision	  making	  on	  the	  cloudlet	  level	   Cloudlet	  send	  reﬁned	  collected	  data	  to	  the	  global	  cloudlet	  for	  farther	  processing	  and	  inter-­‐cloud	  decision	  making	
   Inter-­‐Cloudlet	  communication	  can	  be	  using	  wired	  or	  wireless	  communication	  system	  with	  higher	  range	  (WiMax)	
   Global	  Cloudlets	  are	  connected	  to	  an	  enterprise	  cloud	  computing	  system	  using	  Cellular	  communication	  system.	
  UMCM Top Level ArchitectureEnterprise
CloudMobile ObjectLocal CloudletGlobalCloudletUMCM data flow

Datacollectionusing sensorsIntra-cloudlet
dataprocessing anddecisionmakingDate fusion,global Dataprocessingand decisionmakingEnterprise cloudfor data storageand globalsystemmanagementUMCM	  Support	  for	  WBANs	
  So2ware-­‐deﬁned	  datacenter	  (SDD)	
  Introduc;on	
   SDD	  term	  coined	  	  by	  VMware’s	  Steve	  Herrod.	
   Basically:	   	  “Software	  managed	  component(s)”	   	  “Hardware-­‐independent	  management”	   	  	  Very	  bad	  news	  for	  specialized	  hardware	  vendors	
   A	  new	  Buzz	  word	  or	  a	  new	  and	  eﬃcient	  infrastructure	  management	  solution??	  	
  Introduc;on	  (SDD)	
   Software-­‐deﬁned	   datacenter	   (SDD)	   is	   the	   phrase	  used	   to	   refer	   to	   a	   data	   center	   where	   all	  infrastructure	   is	   virtualized	   and	   delivered	   as	   a	  service.	  	
   The	  Control	  of	  the	  data	  center	  is	  fully	  automated	  by	   software,	   meaning	   hardware	   conﬁguration	   is	  maintained	  through	  intelligent	  software	  systems.	  
 	   This	   is	   in	   contrast	   to	   traditional	   data	   centers	  where	   the	   infrastructure	   is	   typically	   deﬁned	   by	  hardware	  and	  devices.	
  Introduc;on	  (SDD)	
   Support	  Business	  Logic	  Driven	  Automation	
   Business	  oriented	  environment	
   Allow	  applications	  to	  deﬁne	  its	  own	  resource	  requirements	  	  based	  on	  corporate	  SLA	  and	  compliance	  policies	
   Business	  logic	  elements	  translated	  into	  a	  set	  of	  management	  instructions	  	  Core	  Components	  of	  SDD	  SDD is a layer of abstraction above multiple other SDx layers(network, virtualization, storage, etc),Core	  Components	  of	  SDD	
   Network	  Virtualization	
   The	  user	  deﬁned	  his	  needs	  and	  SLA	  then	  the	  software	  will	  take	  care	  of	  the	  rest.	
   Also	  called	  Software-­‐deﬁned	  networking	  (SDN)	
   Server	  Virtualization	
   Hypervisor	  and	  VMs	  management	
   Storage	  virtualization	
   Storage	  hypervisor	  concept	
   Abstracting	  the	  management	  software	  from	  the	  SAN	  allows	  customers	  to	  manage	  multiple	  storage	  types	  and	  brands	  from	  one	  single	  software	  interface.	  	
  Objec;ves	
   Software	  Deﬁned	  DataCenter	  	  that	  provides	  the	  following:	
   Eﬃcient	  power	  usage	  	   Proportional	  power	  consumption	
   Eﬃcient	  cooling	  system	
   Free	  cooling	  solutions	  where	  possible	
   Green	  DataCenters	   Flexible	  system	  management	  and	  scalability.	
   Consider	  other	  issues	  like	
   Geographical	  locations	  of	  data	  centers	   Renewable	  Energy	
  Eﬃcient	  Power	  Usage	  	  (Power	  Propor;onality)	
   A	  power	  proportional	  system	  consumes	  power	  proportional	  to	  its	  active	  load.	
   Average	  data	  center	  resource	  utilization	  is	  very	  low	  	
   	  This	  leads	  to	  considerable	  resource	  idleness	  on	  average	  	   This	  idle	  resources	  can	  consume	  almost	  as	  much	  energy	  as	  active	  ones	   Signiﬁcant	  amounts	  of	  energy	  can	  be	  wasted	  here	
   Solution:	  Power	  down	  idle	  resources	  (servers,	  storage	  and	  network,	  etc..)	  to	  achieve	  power	  proportionality.	
  Multi-Layer SDDArchitectureDataCenter Farm Manager X
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ASServer n-1CloudFlow:	  An	  innova;ve	  Management	  Framework	  for	  Cloud	  Compu;ng	  Systems.	
  Introduc;on	
   A	  holistic	  and	  innovative	  management	  framework	  that	  is	  able	  to	  handle	  cloud	  computing	  management	  complexities	  and	  facilitate	  the	  integration	  of	  diﬀerent	  management	  schemas	  in	  diﬀerent	  cloud	  system	  levels	  like	  infrastructure,	  applications,	  SLA,	  brokerage,	  etc	  in	  one	  framework.	
   Its	  provide	  eﬃcient	  management	  solution	  that	  its	  integrated	  with	  the	  Software-­‐deﬁned	  datacenter	  (SDD)	  	
  CloudFlow:	  How	  it	  works	   The	  managed	  cloud	  system	  status	  are	  continuously	  monitored	  to	  collect	  system	  parameters.	
   System	  parameters	  will	  be	  analyzed	  using	  a	  cross-­‐layered	  analysis	  hierarchy	  	   Optimally	  scale	  up	  and	  down	  the	  cloud	  resources	  i.e.	  
virtual	  machine,	  network	  resources,	  cost,	  management	  overhead	   Cost,	  power	  and	  performance	  can	  be	  optimized	  to	  be	  within	
  a	  predeﬁned	  operational	  region	  “Safe	  Operational	  Zone” .	  	  Safe	  Opera;onal	  Zone	  Concept	  Safe	  Opera;onal	  Zone	  Concept	  Thank	  You