A Complex Adaptive System Approach to QoS Assurance and Stateful Resource Management for Dependable Information Infrastructure (CIP Project)

1
A Complex Adaptive System Approach toQoS
Assurance and Stateful Resource Management for
Dependable Information Infrastructure(CIP
Project)

• Nong Ye (PI)
• Professor of Industrial Engineering, Affiliated
  Professor of Computer Science and Engineering
• Ying-Cheng Lai (co-PI)
• Professor of Electrical Engineering and
  Mathematics
• Partha Dasgupta (co-PI)
• Associate Professor of Computer Science and
  Engineering
• Collaborators AFRL (John Faust and Pat Hurley)
• October 18, 2002

2
Presentation Outline

• Project overview
• Year 1 work
• QoS requirements Nong Ye
• local-level QoS models (router and web server)
  Nong Ye
• Simulation model of Internet Nong Ye
• Mathematical theories on networks and attacks
  Ying-Cheng Lai
• Trust and security models of networks Partha
  Dasgupta
• Year 2 work and plan
• Regional-level QoS models Nong Ye
• Detection of emergent network states Nong Ye
• Mathematical theory on phase transition in
  networks Ying-Cheng Lai
• Trust and security model of networks Partha
  Dasgupta

3
Project Overview

• Goal
• Develop the bottom-up self-synchronization of
  QoS-centric stateful resource management,
  according to a Complex Adaptive Systems approach,
  for a dependable information infrastructure that
  will be used to host network-centric information
  operations
• Objectives
• Investigate, implement and test two enabling
  elements of the dependable information
  infrastructure
• Control strategies to enable the bottom-up
  self-synchronization of QoS-centric stateful
  resource management
• Control and communication protocols to embed the
  control strategies of self-synchronization into
  the existing information infrastructure for
  making it dependable at affordable costs
• Year 1 research local-level QoS and security
• Year 2-3 research regional-level QoS and
  security
• Year 4-5 research global-level QoS and security

4
QoS Requirements

• Without QoS requirements, any QoS level is
  acceptable
• Sensitivity of various traffic data on computer
  networks
• QoS Attributes
• Timeliness
• Precision
• Accuracy

5
QoS Requirements

• Traffic data classification
• Technology properties
• Time dependency
• Real Time (RT) hard constraints on delay and
  jitter
• Non Real Time (NRT) soft constraints mostly on
  delay
• Symmetry of Interaction
• Symmetric requests and responses consume
  comparable amounts of resources
• Asymmetric requests are less resource-consuming
  than responses
• Human factor properties
• Data on delay
• Conventional text and data lt 2-5 sec. tolerable
  gt 5 sec. unacceptable
• Audio lt 0.1-0.5 sec. for real time impression in
  virtual reality (VR)
• Video less sensitive than audio, lt 100 ms for
  audio and video synchronization
• Data on jitter
• Audio lt 20-30 sec. for VR, lt 100 ms for CD
  sound, lt 400 ms for telephone speech
• Video lt 50 ms for HDTV, lt100 ms for broadcast
  TV, lt400 ms for video-conference
• Data on bit error rate
• Audio lt10-2 for telephone, lt10-3 for
  uncompressed CD, lt10-4 for compressed CD
• Video 10-6 for HDTV, 10-5 for broadcast TV, 10-4
  for videoconference

6
QoS Requirements

• Traffic data classification

7
QoS Requirements

• Standards of QoS requirements for each traffic
  class
• Voice over IP

8
QoS Requirements

• Standards of QoS requirements for each traffic
  class
• Video on demand

9
Local-Level QoS Models

• Existing models
• Best effort (BE) current Internet, FIFO, no
  resource reservation, no service service
  differentiation, no service guarantee
• Differential service (DS) DiffServ, RFC2475,
  per-hop service control, coarse granularity of
  service differentiation through traffic
  classification, conditioning, priority queuing,
  bandwidth allocation by service class, weak
  service guarantee, stateless
• Integrated service (IS) InteServ, RFC1633,
  end-to-end bandwidth reservation through RSVP,
  queuing to enforce bandwidth allocation, firm
  end-to-end per-flow service guarantee, problems
  in scalability and flexibility
• Goals
• Minimize execution time
• Maximize resource utilization
• Maximize throughput

10
Local-Level QoS Models

• QoS principles
• Resource agents cannot provide end-to-end service
  guarantee to user agents
• Process agents need to be proactive in seeking
  right resource agents to meet their end-to-end
  QoS requirements
• QoS goal of local-level resource agents
• Performance stability and thus predictability
  through bounded or least variable performance
• Service differentiation
• Guaranteed if admitted

11
Local-Level QoS Models

• QoS model of router
• QoS model based on feedback control (FB) versus a
  DS model
• Goal bounded delay of high-priority packets
• State monitored high-priority queue length
• PID feedback control of high-priority admission
  rate (r)
• Root locus method for optimal control parameters

12
Local-Level QoS Models

• QoS model of router
• QoS model based on adjusted WSPT (A-WSPT) versus
  a best-effort model
• Goal minimize and stabilize delay of
  high-priority packets
• A-WSPT scheduling rule
• Markov decision process for optimal scheduling
  and admission control

13
Local-Level QoS Models

• QoS models of router
• OPNET simulation experiments
• Parameters of router models
• BE FIFO queuing, no admission control
• WSPT and A-WSPT WSPT and A-WSPT queuing, no
  admission control, W5 for high-priority packets,
  W2 for low-priority packets
• DS token rate400,000 bits/sec, bucket
  depth100,000 bits, high-priority queue100,000
  bits, low-priority queue450,000 bits
• FB Kp 1.0, Ki 0.2, Ki 0.2, Control bound
  value 80,000 bits, other configurations are
  same as those for DS
• Experiment set-up
• Each source generates either high-priority
  packets or low-priority packets, NOT both
• Inter-arrival time exponential distribution
• Packet size normal distribution, mean10,000
  bits, standard deviation2,000 bits
• One output interface Service rate 640,000
  bits/sec
• Total output queue space 550,000 bits
• Two types of packet High priority ToS value7,
  Low priority ToS value0
• Simulation duration 180 seconds

14
Local-Level QoS Models

• QoS models of router
• OPNET simulation experiments
• Experimental set-up

15
Local-Level QoS Models

• QoS models of router
• Simulation results for high priority packets in
  the heavy traffic condition

16
Local-Level QoS Models

• QoS models of router
• Simulation results for high priority packets in
  the heavy traffic condition

17
Local-Level QoS Models

• QoS models of router
• Overall simulation results
• For the heavy traffic condition
• Feedback control
• Shortest time-in-system for high-priority packets
  with low variation
• Lowest packet loss for high-priority packets
• High throughput for high-priority packets
• DiffServ
• Generally similar performance to FB
• Higher loss of high-priority packets at the
  output queue
• Slightly better throughput of high-priority
• WSPT
• Highest throughput for high-priority traffic.
• Variable time-in-system, because WSPT allows
  newly arriving packets to push back
  lower-priority packets
• A-WSPT
• Comparable to WSPT but with more stable
  time-in-system
• Best effort
• Similar performance for high and low priority
  packets
• For the light traffic condition

18
Local-Level QoS Models

• QoS models of web server
• Web requests with due time
• Admission control if completion time gt due time,
  reject
• QoS models based on production planning for
  single machine, parallel machines (cluster of web
  servers) and serial-machines (multiple steps)
• WSPT schedule by Wj/Pj
• ATC combine WSPT with minimum slack time,
• EDD schedule by the earliest due date

19
Local-Level QoS Models

• QoS models of web server
• OPNET simulation experiments
• Five models BE, DS, WSPT, ATC, EDD
• Three scenarios
• Heavy traffic
• Traffic Generation
• Weight 1,2,3,6
• Packet inter-arrival time distribution
  exponential (0.04) for W1, W2, W3,
• and exponential (0.2) for W6
• Packet size distribution Normal(6000,1000)
  bits
• Traffic generated 480,000 bits per second in
  average
• Due date distribution Normal(0.8,0.08)
• Queue
• Service Rate 240,000 bits per second
• Capacity 512,000 bits. For DS, capacity of
  high-priority queue is 32,000
• capacity of low-priority queue is 480,000
• K value for ATC 1000
• Longer due time
• Traffic Generation due date distribution of
  Normal(2,0.2)

20
Local-Level QoS Models

• QoS models of web server
• Simulation results for the heavy traffic condition

21
Local-Level QoS Models

• QoS models of web server
• Overall simulation results
• Effects of due time and admission control less
  drop at the queue
• Effects of longer due time longer queue length
• Effects of less queue capacity
• Smaller lateness of all traffic for all five
  models, W6, W3 and W1, because of a smaller queue
• DS drops more W6
• Production planning admission control keeps the
  lateness of all requests lt 0
• For W6 requests WSPT/ATC is similar to DS in
  producing the best performance
• For W3 and W1 requests WSPT/ATC is better than DS

22
Simulation Model of Internet

• Goals
• Build a simulation model of Internet using
  scale-free model of Internet
• Discover data collection points, metrics and
  analytical techniques to detect emergent network
  states
• Research stages

23
Simulation Model of Internet

• Research stages
• Stage 1
• Write program which implements the scale-free
  algorithm to build up internet topology
•   max of nodes n 5,000
• of connections m 1
• Initial of nodes n0 m
• Stage 2
• Classify devices as follows
• For all nodes with connectivity 1, assign
  workstation model to 70 of nodes, server model
  to 30
• Within server nodes, assign types 40 HTTP, 40
  E-mail, 10 FTP, 10 Telnet
• For all nodes with connectivity gt 16, assume ISP
  assign ISP Router model (black box ISP).
• For all remaining nodes, assign switch model
• For each ISP Router, recursively define
  sub-network of all nodes connected to this router
  and its children, etc.
• Define top network as all sub-networks and the
  links connecting them (these are router to router
  links).

24
Simulation Model of Internet

• Research stages
• Stage 3
• Generate java classes of Modeler Document Data
  Type using Oracles XML Class Generator for Java
• Use classes to generate XML document of internet
  topology
• Import XML document to OPNET and verify links
• Stage 4
• Create probe models to collect metrics
• Collect baseline system metrics
• Stage 5
• Create scenarios with random failure
• Create scenarios with planned attack
• Collect metrics
• Stage 6
• Detect emergent network states using analytical
  techniques

25
Simulation Model of Internet

• Topology
• 5,000 devices
• 32 ISP routers
• 1006 servers (30)
• Min subnet 38 devices
• Max subnet 441 devices

26
Simulation Model of Internet

• Topology

27
Simulation Model of Internet

• Simulation set-up
• Simulations run for 6 minutes each
• All workstations initialize between 30 seconds
  and 4.5 minutes
• ISP routers
• Each ISP router has a number of interfaces, each
  of which represents a point of access into the
  ISP
• Min (max) number of interfaces on a router 17
  (77)
• Total number of interfaces on the network 1,027
• RIP Routing protocol is implemented one each
  interface
• RIP creates dynamic routing tables with all
  routes to destination
• Routing uses a FIFO queuing scheme
• Buffer size 1 KB, reduced for attack/failure
• Packets are dropped when the buffer is full

28
Simulation Model of Internet

• Simulation set-up
• Workstations

29
Simulation Model of Internet

• Simulation set-up
• Servers

30
Simulation Model of Internet

• Experimental conditions
• Independent variables
• Under attack, a device operates at a reduced
  service rate
• Under failure, a device ceases to process traffic

31
Simulation Model of Internet

• Experimental conditions
• Dependent variables

32
Simulation Model of Internet

• Data collection

33
Simulation Model of Internet

• Progress

34
Simulation Model of Internet

• Some traffic data collected
• Baseline traffic

35
Simulation Model of Internet

• Some traffic data collected
• Global metric IP packets dropped

36
Simulation Model of Internet

• Some traffic data collected
• Regional metric IP packets received at ISP

37
Simulation Model of Internet

• Some traffic data collected
• Local metric traffic received at interface

38
Simulation Model of Internet

• Some traffic data collected
• Local metric traffic dropped at interface

39
Simulation Model of Internet

• Some traffic data collected
• Regional metric processing delay at ISP

40
Detection of Emergent Network States

• Multivariate statistical process control
  techniques to detect anomalies
• Chi-square disatnce test
• MEWMA
• Multivariate factor analysis to identify
  significant factors
• ANOVA
• Nonlinear time-series analysis techniques to
  detect emergent behavior
• Embedded coordinate technique find correlation
  dimension, identify system dimensionality,
  requires a deterministic system present in model
• Multivariate Autoregressive (MVAR) models
  determine coupling strengths between regions
• Synchronization technique "spike synchronization
  detection" or "unitary events detection, tells
  whether there is a synchronization between two
  time series that consist of spikes at random
  times
• Hilbert space technique works for stochastic
  models

41
Regional-Level QoS Models

• Regional-level systems
• Local area networks
• Administrative domains
• Existing work
• Centralized optimization e.g., computational
  grids
• Allocation and scheduling are fundamental to
  performance
• Allocation of data and computation in space
• Select available resources for processes
• Assign processes to resources
• Distribute processes and data
• Scheduling data and computation over time
• Order processes on resources
• Order communications between processes
• Objectives
• Promote the performance of the SYSTEM
• Job schedulers maximize throughput, minimize
  communication cost
• Resource schedulers maximize resource
  utilization
• Promote the performance of the INDIVIDUAL
  APPLICATIONS
• Application schedulers optimize performance,
  e.g., execution time, resolution, speed, cost,
  etc.

42
Regional-Level QoS Models

• Existing work
• High performance schedulers
• MPP (Massive Parallel Processors) produce poor
  performance for computational grids

43
Regional-Level QoS Models

• Existing work
• High performance schedulers
• Grid schedulers

44
Regional-Level QoS Models

• Existing work
• High performance schedulers
• Grid schedulers
• Program model
• Represent programs in terms of their resource
  requirements
• Build a program dependency graph of phased tasks
• Performance model
• Use the program dependency graph parameterized
  during execution as performance model to predict
  execution time
• Use a generic model, e.g., execution time
  computation communication
• Input the data-flow program graph to expert
  system
• Scheduling policy
• Choose the best among candidate schedules based
  on performance criteria
• Centralized, FCFS
• Load balancing

45
Regional-Level QoS Models

• Existing work
• High performance schedulers
• Grid schedulers
• Example AppLes
• Framework and a testbed

46
Regional-Level QoS Models

• Existing work
• High performance schedulers
• Grid schedulers
• Example AppLes
• Strategy to develop a schedule

47
Regional-Level QoS Models

• Existing work
• High performance schedulers
• Grid schedulers
• Example AppLes
• Cost model to evaluate strip decomposition

48
Regional-Level QoS Models

• Existing work
• High performance schedulers
• Grid schedulers
• Example AppLes
• Methods of strip decomposition

49
Regional-Level QoS Models

• Existing work
• High performance schedulers
• Grid schedulers
• Example AppLes
• Performance results

50
Regional-Level QoS Models

• Existing work
• High performance schedulers
• Grid schedulers
• Challenges
• Complexity of scheduling problem
• Variations in deliverable resource performance
  due to resource sharing
• Prediction of programs resource requirements
• Hardware and software heterogeneity

51
Regional-Level QoS Models

• Principles for our regional-level QoS models
• Simplify the scheduling problem through resource
  standardization, i.e., stabilizing performance of
  resources to make them standard parts
• Develop new scheduling and control strategies to
  achieve the objective of performance stability
• Call on reserved, redundant resources to achieve
  performance stability under failure/attack
• Make dynamic resource state available to process
  agents
• Process agents plan ahead to achieve performance
  objectivesa distributed decomposition of the
  scheduling problem complexity
• Make network policies accordingly