Management Of Future Communication Networks And Services

Management of Future Communication Networks and Services Miguel Ponce de Leon TSSG, Waterford Institute of Technology

Agenda
Major Trends
In-Network management
Architecture
Modelling and Knowledge Engineering
Algorithms and Processes
2 nd Workshop on IMS enabled Converged Networks New paradigms for services delivery, Paris, Sept 12, 2008

Major Trends
Technology Environment: Multitude of networked/distributed applications
Social-Economic Environment: Awareness of demographic change in an aging society (Europe and others)
“ Information” centric rather than “bit” centric at the network level

Integrated support of mobility
Communication everywhere
New services – new devices – new interactions
Complexity is largely due to heterogeneous traffic types (e.g. Data, VoIP, VoD)
Traditional management has automation but it is still manually planned and deployed
leads to high cost and error prone
2 nd Workshop on IMS enabled Converged Networks New paradigms for services delivery, Paris, Sept 12, 2008

Can In-network management, be the essence for a system to self-govern its behaviour within the constraints of the business goals the system as a whole seeks to achieve?

The TSSG is carrying out research on the Management of Communication Networks and Services
We are addressing
Architecture and Methodology
Policy Analysis and Deployment

In an communication architecture, the basic management element is a Control Loop.
This acts as manager of the resource through monitoring, analysis, and actions taken on a set of predefined system policies.

IBM: MAPE, 2004
The four functions consume and generate knowledge . The knowledge base can be seeded with known information about the system, and can grow as the autonomic manager learns more about the characteristics of the managed resources.

Motorola: FOCALE, 2005
FOCALE: Foundation, Observe, Compare, Act, Learn, rEason
Define New Device Configuration(s) Autonomic Manager Ontological Comparison Reasoning and Learning Managed Resource Analyze Data and Events Determine Actual State YES NO DEN-ng Models and Ontologies Model-Based Translation Match? Control Control Control Control Policy Manager Policies control application of intelligence Context Manager

S. Dobson et al, 2006

TSSG: AMCNS Loop 2007

Extended DEN-ng models to incorporate standardised finite state machines that model dynamic behaviour of network devices and services.
Analysed and generated model information required to
Detect changes in context
Create, enforce and monitor policy events, conditions and actions

Machine learning, reasoning and inference techniques for analysing/creating model information for policies
Coordination of Behaviour Transition (and/or Condition) T 5 States (=Behaviour) Refined Goal Hidden Design Inconsistency Nested Classifier (expected Behaviour) Sub-Goal T 7 T 8 T 6 T 9 T 10 T 1 T 4 T 2 T 3 T 5 Sub- Goal T 11 T 12 T 13 Sub- Goal Class Diagrams = Static Model = Facts State Machines = Dynamic Model = Behaviour using Facts Behavioural Pattern to refine one or more global Goals into Sub- Goals, governed by one or more Policies

Combine different Biological principles
Molecular Biology
Principles cells used to self-organise
Physiological systems used to self-manage
Translate the biological mechanisms to policy based management system
Develop policies to evaluate equilibrium alterations (e.g. link failure) and stabilise equilibrium through Autonomic Element functionality
Develop polices for cooperative self-organisation between Autonomic elements

System/Network level – Mapping from Blood Glucose Homeostasis for self-management of resources
Device/Instance – Map from Chemotaxis, Reaction Diffusion, and Hormone signalling for self-organisation of traffic QoS supported paths
Business System/ Network Device/ Instance

Blood Glucose Homeostasis under varying intensity of the body is compared to the intensity of bandwidth usage in the network
Glucose is available in other forms: Glycogen, Fat
Glycogen compared to the demand profile and Fat is compared to new or fluctuating traffic
Rules of converting from Glycogen to Fat (and vice versa) is compared to mechanism for maintaining revenue
Glucose Glycogen Fat Aerobic Anaerobic Limit allowed for respiration Glucose used < Threshold limit Glucose avail.> Threshold limit Glycogen used> Threshold limit Glycogen avail.> Threshold limit Activity Breakdown Fat Loose Fat Improve respiration Breakdown Glycogen for routine traffic Replace Glycogen Packet forwarding Primary path Spare capacity Good Revenue Bad Revenue Change in Revenue Increase in Primary traff. Decrease in Primary traff. Increase in Fluc. Traff. Decrease in Primary traff. New Traffic Refine Traffic type Threshold Discover Spare capacity Loose Spare capacity Use resource For primary path Replace Primary traff. resource

Respiration energy used for path ratio refinement
Glucose is broken down to create energy through two types of respiration (Aerobic and Anaerobic)
Aerobic respiration creates high energy, and compared to streaming traffic into allocated space
Anaerobic respiration creates low energy, and compared to streaming traffic into space allocated for different type of traffic
Multimedia Glycogen P1 P2 Demand profile paths Data Glycogen Fat F1 Energy

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Management Of Future Communication Networks And Services

by Miguel Ponce de Leon @ TSSG / Waterford Institute of Technology on Sep 17, 2008

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