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Autonomic Computing and Networking. Pieter Simoens , Steven Latré Filip De Turck , Bart Dhoedt Future Internet Department 17/05/2011 Gent. Outline. Research Context Thin/Smart client computing Autonomic Communications Introduction to Demo’s. Why autonomic systems ?.

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autonomic computing and networking

Autonomic Computing and Networking

Pieter Simoens, Steven Latré

Filip De Turck, Bart Dhoedt Future Internet Department

17/05/2011 Gent

outline
Outline
  • Research Context
  • Thin/Smart client computing
  • Autonomic Communications
  • Introduction to Demo’s
why autonomic systems
Why autonomic systems ?

Autonomic systems :

Managing complex things is difficult

autonomic systems
Autonomic Systems

Observation

Complexity of ICT-systems is growing

Issues

  • Management gets complex (high opex)
  • System configuration error-prone and sub-optimal
  • Difficult to recover from unforeseen situations
autonomic systems1
Autonomic Systems

Inspiration : The Human Body

  • Distributed responsibilities
    • Collaborating control systems
    • Each system: optimised for specific task
    • Under control of central system
  • Learns and adapts online
  • Governed by high-level goal: Stay Alive
autonomic systems2
Autonomic Systems

Autonomic systems decrease management complexity by performing low-level configurations themselves

  • The system adapts its behavior to changes in
    • The environment
    • End-user needs
    • Service requirements
  • It is governed by high-level policies
    • Representing business goals
    • Defined and managed by human operators
autonomic computing
Autonomic Computing

"Civilization advances by extending the number

of important operations which we can perform without

thinking about them.” - Alfred North Whitehead

  • MAPE control loop (IBM 2001)
  • Knows itself and its context
  • Configures, reconfigures, heals and protects itself
  • Optimizes continuously
  • Can interact with outside world
  • Anticipates to balance resources and needs, without involving users
acn @ future internet dpt
ACN @ Future Internet Dpt.
  • 1. Autonomic Technologies
  • - Automatic policy translation
  • - Autonomic adaptation
  • - Scalability and multi-agent management
  • - Learning
  • - Design and implementation of an autonomic service platform
  • 2. Autonomic Communication
  • 3. Autonomic Distributed Computing
  • 4. Integrated infrastructures
  • 5. Smart Client Computing
  • 6. Autonomics for IoT
    • - Sensor networks
    • - ICT for Green
outline1
Outline
  • Research Context
  • Thin/Smart client computing
  • Autonomic Communications
  • Introduction to Demo’s
introduction
Introduction
  • Thin client ?
    • ideallylimited to I/O functions (display, network)
    • CPU and storagehosted in the network
  • Rationale :
    • Enhanced software life cycle management
    • Data security, privacy and integrity
    • Increased terminal lifetime
    • Data is available

optimized for wired LAN environments,

non I/O intensive applications

objectives
Objectives

mobile

multimedia

QoE

energy-efficient

intelligent

  • X-layer optimization for better performance
    • wireless link optimizations
    • image transmission optimizations
    • optimized management(profiling, migration, reservations, ...)

public hotspot

core network

access network

mobithin
MobiThin
  • FP7-STREP (call 1, Challenge 1.1 “Future Internet”)
  • Time frame
    • start : Jan 1st, 2008
    • end : June 30th, 2010
mobithin system
MobiThin system

Build a mobilethin client service in

wirelessenvironment for heterogeneousapplications

project highlights integrated system
Project Highlights - Integrated System

Management Server SLM

Thin Client Server SLM (physical host)

User Session SLM (VM that runs apps)

Channel server side SLM

Channel clientside SLM

Mobile Device SLM

  • Fully functional E2E system has been built, based on requirements analyzed at the start of the project
  • Cross-layer optimizations = the core business of the project 1) wireless X-layer mechanisms (thin client protocol - PHY-MAC) 2) thin client protocol optimizations
      • scheduled updates
      • event buffering
  • 3) self-management of the service
      • VM migration supporting QoS, peak load avoidance, …
      • server consolidation for green computing
  • 4) SLM framework spanning the complete system developed
possible actions per level

Relocate session to other server, start/stop extra server

Redistribution of resources to certain session, compensating over-spenders by under-spenders

Choice of channel (= image transmission protocol)

Tuning of channel parameters: color depth, UDP/TCP, user event buffering, scheduled updates, streaming

(Semi-) Physical changes: display brightness, wireless interface sleep time

Possible actions per level

Management Server SLM

Thin Client Server SLM (physical host)

User Session SLM (VM that runs apps)

Channel serverside SLM

Channel clientside SLM

Mobile Device SLM

server consolidation
Server Consolidation

System load

  • When there is low work load on the system, energy can be saved by shutting down redundant thin client servers.
  • When the work load raises, extra thin client servers should be powered on.

t

Server Consolidation Algorithm

Decide how many servers are needed in the (near) future based on the system load in a previous time frame

server consolidation1
Server Consolidation

P  CPU  #online servers

Max. Energy Savings: 45%

mobithin gains
MobiThin Gains
  • Successful project, rated “Excellent” by EU
  • Strong partnership, good prospects for future collaborations
  • Foundation laid for innovative research ideas
  • Good output in publications
    • > 20 accepted publications
    • Best paper award
  • Standardisation through ETSI (ISG-MTC)
    • 2 work items completed
from thin to smart
From Thin to Smart

Thin client : Run the whole application on a server

Problems

Constant and high bandwidth needed

Always extra latency introduced

Doesn\'t work well with some multimedia applications (e.g. augmented reality)

smart client
Smart client

Only offload parts of the software

smart client1
Only offload parts of the software

Adapt the deployment to the changing context and the changing optimization goal

Smart client
outline2
Outline
  • Research Context
  • Thin/Smart client computing
  • Autonomic Communications
  • Introduction to Demo’s
the goal of autonomic communications
The goal of autonomic communications

Optimize the Quality of Experience, maximize the revenue … and do it fast!

From high-level goals

To low-level device configurations

  • Router> enable
  • Router# configure terminal
  • Router(config)# interface ethernet 1/1
  • Router(config-if)# ethernet
  • Router(config-line)# exit
  • Router(config)# end
  • Router#
computing vs communications
Computing vs. Communications

Autonomic Computing

Autonomic Communications

Extension to IBM’s model

Heterogeneous devices

Networked system

More complex control loops

Model-based translation

Semantically enriched

Reasoning & learning

Policy-based management

  • Presented by IBM in 2001
  • Homogeneous components
  • 1 computing environment
  • MAPE control loop
    • Monitor
    • Analyze
    • Plan
    • Execute
complexity
Complexity
  • Manage complexity of an Operations Support System
  • Real-time dynamic management
  • Per service or per subscriber management

Will we ever be able to tackle such complexity?

  • Parallel with robotics
  • Millions of interactions
  • Trying to “mimic” human behavior
  • Still in early stages
introducing intelligence into the network
Introducing intelligence into the network

Privacy

Scalable

HOW?

Trustworthy

Intelligent

Human-governed

Secure

a federation of autonomic elements ae
A federation of autonomic elements (AE)

distributed reasoning

service

discovery

context

exchange

contract

negotiation

AE

AE

AE

AE

AE

AE

AE

AE

AE

AE

AE

AE

AE

AE

AE

research focus
Research focus

Design and implementation of architectural components for federated management of future networks and services

policy driven

loosely coupled management components

end-to-end federation of management domains

semantic communication and collaboration

research directions
Research directions

automated policy translation

control loop design

semantic inter-domain contract negotiation

autonomic cloud management

fp7 ecode
FP7 ECODE
  • Introducing autonomic behaviour in today’s routers
  • FP7 Strep (Call 1.6 “New paradigms and experimental facilities”)
  • Timeframe
    • Start: September 2008
    • End: December 2011
fp7 ecode1
FP7 ECODE

Experimental COgnitive Distributed Engine

Cognitive engine on top of an existing router

integration of learning capability into self adaptive closed loop control process

Router

Router

Learning

Weak coupling

Routing

Routing

Routing + Learning

Strong Coupling

Forwarding

Forwarding

Forward + Learning

Today

Step 1: overlay

Step 2: integrated

Integration of learning capability into self-adaptive closed-loop control process

Communication systems autonomously interrelated and controlled, dynamically adapting to changing environments

Role of learning

  • How to diagnose their own state, own activity/behavior, and environment over time (thus detect, identify, & analyze problems)
  • How (cost-effective) and when (timely) to adapt decisions and to tune react/execute (and thus capable to increase their functionality and performance)
  • When to operate autonomously and to cooperate

Augment control paradigm of pre-defined decision making process, and pre-determined execution, with learning component

ecode machine learning in practice
ECODE machine learning in practice

Different TCP stacks cause different levels of fairness

Highspeed

Cubic

Cubic

Reno

Vegas

Cubic

Vegas

Reno

Highspeed

Vegas

ecode machine learning in practice1
ECODE machine learning in practice
  • Different TCP stacks  different responsiveness
  • Variations due to
    • Different TCP dialects
    • Defective stacks: ignores congestion warnings
  • Profile Based Accountabilityholding subscribers (i.e. stacks) accountable for their behaviour

reward stacks

in the good zone

Good

zone

responsiveness

punish stacks

in the bad zone

aggressiveness

outline3
Outline
  • Research Context
  • Thin/Smart client computing
  • Autonomic Communications
  • Introduction to Demo’s
demo 1 hybrid remote display
Demo 1 – hybrid remote display

Motivation: graphical content diversity

multimedia application

video streaming, 3D game

office applicationtext editor, spreadsheet, e-mail client

  • large areas of solid color
  • few colors
  • updates cover small part of screen
  • low update frequency
  • no homogeneous areas
  • fine-grained complex color patterns
  • updates cover whole screen
  • high update frequency

Encode through remote display protocol (VNC)

Encode through video codec(H.264)

dynamically switching between protocols
Dynamically switching between protocols

Decision on output encoding format based on amount of motion between subsequent frames

  • inefficient transport of multimedia data via a thin client protocol
    • high bandwidth
    • irresponsive user interface
  • video codecs are designed for transport of video
    • minimal bandwidth requirements for a given amount of motion
    • higher client CPU load due to decoding
demo 2 slrg inferencing
Demo 2 – SLRG inferencing
  • Identification of Shared Link Resource Groups

Shared Link Resource Group

demo 2 slrg inferencing1
Demo 2 – SLRG inferencing
  • Goal: improve recovery time of link failures by learning.
  • OSPF area
  • One node is enabled with SLRG inference
    • Learns
demonstration ilab t setup
Demonstration – iLab.t setup
  • Using three nodes

ctl

vhost-0

vid

OSPF area

n1

n2

n3

n4

n5

n6

n7

n8

Demo control

Video streaming

video output

n9

n10

demonstration video screen
Demonstration – video screen
  • Showing three video streams
demonstration video screen1
Demonstration – video screen
  • What to look for?
    • Video interruptions;
    • standard OSPF (left side) and SRG inference enabled OPSF (right side).
  • For learned SRGs
    • compare left and right parts of astream;
    • compare streams;
    • compare local andremote link failures.
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