2008’ Future Internet
Autonomic Network Management
May 2008
Joon-Myung Kang
Distributed Processing & Network Management Lab.
Dept. of Computer Science and Engineering
POSTECH, Korea
eliot@postech.ac.kr
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Contents
1. Introduction
2. Autonomic Computing vs. Autonomic Networking
3. Autonomic Computing Environment
4. Research Challenges
5. Conclusions
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The Problem of Complexity
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Moving
Early
majority
CCS
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Network Environment
Billing VHE
ISP
SIP Proxy
Server
Signalling
Gateway
WAPAccounting
The
Internet
Context-aware information
Centre
Satellite
Core Network
Broadcast
Networks
(DAB, DVB-T)
CDMA, GSM,
GPRS
4G
IP-based
micro-mobility
Bluetooth
Zigbee
WiBro,
HSDPA
Wireless
LANs
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A Traditional OSS/BSS
Autonomic Network
Management
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The Problem – Managing Complexity
 The Complexity of system design and management keeps increasing
– Stovepipe systems: best-of-breed functionality but integration nightmares
– Increased technology overwhelms users and administrators
• Different devices have different programming models and interaction models
• Different management tasks and integration types require different skill levels
 The complexity of business is also increasing
– People are demanding a pervasive presence
– Many types of business LOSE MONEY if they can’t react fast enough
– Varieties of threats, problems, and non-optimized behavior keeps increasing
 Behavioral complexity is also increasing
– Everything is interconnected, requiring different policies and functions
– Too complex to predict, needs too high a skill level, not enough people!
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Shortcomings - Infrastructural
 Architectural issues
–
–
–
–
 Integration issues
Data redundancy
Synchronization problems
Application authorization issues
Vendor and Application “lock in”
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– Isolated Data Silos
– Administrative nightmare
– Integration/customization
nightmare
– Transition from legacy systems to
a new OSS
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Stovepipes are Everywhere!
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Some Effects of Complexity
 Expensive
– Cost of management by administrators is increasing (CAPEX, OPEX)
CAPEX(Capital Expenditure): expenditures creating future benefits.
OPEX (Operational Expenditure): on-going cost for running a product, business, or system
 Fragile
– Complex interdependencies make it hard to diagnose and fix problems
– More prone to human error (additional cost)
– Upgrades, performance tuning, re-purposing all suffer
 Inflexible
– Reluctance to change infrastructure once it is working
– Does not support agile business (new software, business processes)
 Worsening
– Technology innovations typically exacerbate the problem, preventing product
innovations from being deployed
Solution: Self-managing systems
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More Effects – Constituency Separation
 Different constituencies have different terms, grammars, and needs
– Service Level Agreement meaning changes
– Business “speak” vs. networking commands
– Different representations (e.g., use of UML)
 Relating network services and resources to business needs
– Not reflected in EMS and NMS design
– Lack of policy controlling allocation
– Lack of ability to
• Incorporate new knowledge
• React in a timely manner to changes
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Motivation of Autonomic Management
 Complexity is growing
–
–
–
–
Telecommunications industry has changed dramatically
Explosive growth of the Internet
The proliferation of mobile technologies
Fixed mobile convergence
Autonomic Network Management
– Simplify network management process by automating and
distributing the decision making processes involved in optimizing
network operation
– Enable expensive human attention to focus more on business logic
and less on low level device configuration processes
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What is Autonomic Computing?
 Autonomic
– Pertaining to operating system that responds automatically to problems or
system failures.
 Autonomic Nervous System
– handles many crucial (but what we'd consider mundane) functions without
requiring any conscious though on our part.
– When we run, it increases our heart and breathing rates. If we get too hot, it
redirects blood flow to "cool us". If we turn a light on or off, it adjusts our pupils
for maximum visual activity.
– This leads us to describe an approach we call "autonomic computing.“
 Autonomic computing
– A computing environment with the ability to manage itself and dynamically adapt
to change in accordance with business policies and objectives.
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Future Vision of Autonomic Computing?
 Machines will take over all management tasks,
rendering humans superfluous.
Wrong
 Machines will free system administrators to manage system at a
higher level.
Right
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A Misconception About Autonomics
 Autonomics is not the four (in)famous self-functions (self-configure, protect, -heal, and –optimize)
– These do not define an autonomic system
– These are benefits resulting from an autonomic system
 Autonomics is rooted in the following capabilities
– Self-knowledge
• We can’t configure what we don’t know!
– Ability to understand what is happening to our surroundings
• Learn from and reason about sensed data
– Inspiration from biology, sociology, economics, …
• New ways to build and organize management functionality
• Notion of maximization of “social welfare” of network service
– Link to business rules
• Network services and resources adapt to change
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Autonomic Computing Attributes
Increased
Responsiveness
Business Resiliency
Discover, diagnose,
and act to prevent
disruptions
Adapt to dynamically
changing
environments
Operational
Efficiency
Secure
Information
and Resources
Tune resources and
balance workloads
to maximize use of
IT resources
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Anticipate, detect,
identify, and
protect against
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Self-Configuration
 Configuration is governed by high-level policies
– BUSINESS objectives that specify WHAT is to be accomplished, but not HOW
 New way of component interaction
– New component adapts itself to how other components in the environment are
working
– Existing components adapt to the presence of the new component
– This cannot be done unless the component “knows itself” and its environment
 DEN-ng models this interaction using the concepts of capabilities and
constraints
– DEN-ng: new version of Directory Enabled Networks (DEN)
– Common information model to translate business rules into device configuration
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Self-Healing
 Error detection and correction is HARD
– Network management is a good (well, bad) example of how NOT to do this
– Predictive failure analysis is still magic. Why? Because there is no selfknowledge!
• Multiple incompatible knowledge sources
• Self-healing affects ALL phases of the control loop
 Autonomic computing detects errors
– Based on self-knowledge (critical role of DEN-ng)
– Once an error is known, it can be repaired
– or can it? (critical role of DEN-ng)
• The problem is more difficult than this – component and system behavior can
change
• How do we know if the change is desired?
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Self-Optimization
 Current research efforts are
– Oriented mostly on optimizing system performance
– Just the aspect of tuning is complex
• Hardware and software dependencies, backwards compatibility, unclear semantics,
etc.
• Tuning one component can adversely affect others
 It’s also about
–
–
–
–
Using the right resources for a given task
Ensuring that tasks with higher business importance get the resources they need
Adapting to recognized environmental and service usage patterns
Learning through action
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Self-Protecting
 Current problems that need to be addressed
– Detecting threats and malicious operations
– Prevent the cascading of uncorrected errors
– Transitioning from reactive to proactive systems
 It can NOT be done without using an information model to represent
the behavior
– Capabilities and constraints
– Patterns and roles
 Interaction between hardware and software to protect a system
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Any Self-Function Can Change
 The system must have models pre-defined
– Characteristics and behavior of itself
– Needs of the user
– Environmental constraints
 Self-configuration can change
– Based on different user needs
– Based on environmental conditions
 Decision-making requires policy to govern the system and its
interactions
– Reasoning enables deduction of cause and effect
– Learning functions enable the system to improve
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Just Add Water and Stir
 The integration of self-configuration, -healing, -protection, and –
optimization is critical
– If that happens, these separate concepts will merge into more powerful concepts
– For example, self-maintenance is the holistic combination of all four of these
principles
• Using anti-virus software as an example, the system will proactively and upgrade its
functionality
• Adjustment of workload in response to changing conditions (e.g., component
failures) and environment (e.g., new users running new apps)
 Self-management is more than the sum of the self-management of its
individual components
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Autonomic computing architecture concepts
 Autonomic computing
system
– A computing system
that senses its operating
environment,
– models its behavior in
that environment,
– and takes action to
change the environment
or its behavior.
- Autonomic Computing reference architecture Autonomic Network Management
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Autonomic computing architecture concepts
 Managed resource
– An entity that exists in the run-time environment of an I/T system and that
can be managed.
 Touchpoint
– The interface to an instance of a managed resource, such as an
operating system or a server.
– A touchpoint implements sensor and effector behavior for the managed
resource.
– And it maps to the sensor and effector interfaces to existing interfaces
 Touchpoint autonomic managers
– An autonomic manager that works with managed resources through their
touchpoints.
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Autonomic computing architecture concepts
 Orchestrating autonomic managers
– An autonomic manager that works with other autonomic managers to
provide coordination functions.
 Integrated solutions console
– A technology that provides common, consistent user interface, based on
industry standards and component reuse, and can host common system
administrative functions.
– The IBM Integrated Solutions Console is a core technology of the IBM
Autonomic Computing initiative that uses a portal-based interface.
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Autonomic computing architecture details
 Autonomic manager internal structure
– Knowledge
• Standard data shared among the monitor, analyze, plan and execute
functions of an autonomic manager, such as symptoms and policies.
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Autonomic computing architecture details
 Knowledge types
Solution
Topology
Knowledge
• Captures knowledge about components and constructions and
configuration for business system.
• For AM to install or configure components, installation and
configuration knowledge is captured in a common installable unit.
Policy
Knowledge
• A policy is a knowledge that is consulted to determine whether or
not changes need to be made in the system.
• Autonomic computing system requires a uniform method for
defining the policies that govern decision-making for autonomic
managers
Problem
Determination
Knowledge
• It includes monitored data, symptoms and decision trees.
• As the system responds to actions, newly learned knowledge can
be collected within AM.
• Autonomic Computing System requires a uniform method for
representing problem.
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Autonomic computing architecture details
 Autonomic manager is a component that implements the control loop.
– Monitor Function
• the function that collects, aggregates, filters and reports details (e.g.
metrics, topologies)
– Analyze Function
• the function that models complex situations to understand current system
state.
– Plan Function
• the function that structures the actions needed to achieve goals and
objectives.
– Execute Function
• the function that changes the behavior of the managed resource using
effectors.
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Autonomic computing architecture details
 Managed Resource
– A controlled system component.
ex) a server, a router, a cluster or business application etc.
 Manageability Interface
– A service of the managed resource that includes the Sensor and the
Effector used by an autonomic manager.
– This is for autonomic manager to monitor and control the managed
resource.
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Autonomic computing architecture details
 Sensor
– A set of “get” operations that retrieve
information about the current state of a
managed resource.
– A set of management events
(unsolicited, asynchronous messages
or notifications) that can occur.
 Effector
– A collection of “set” operations that
allow the state of the managed resource
to be changed in some important way.
– The operations that managed resource
can use to make request.
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Autonomic computing architecture details
 Evolving towards Autonomic Computing Systems
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Autonomic computing architecture details
 An evolution, not a revolution
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Current Network Management Deficiencies
 Aggregates of elements may exhibit behavior not predictable from
knowledge of individual behaviors
– “The whole may be greater than the sum”
 Causal determinacy still limited by simple statistical analysis and
rudimentary correlation approaches
– Precompiled diagnostic processes required to guide approaches
 No ability of the system to “go beyond” precompiled knowledge and
procedures
– “Human-in-the-loop” is still the order of the day
 All current techniques require “human-in-the-loop” back-end analysis
– Extensive system, deployment, and technology knowledge
– Drives us CAPEX and OPEX
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Autonomic Networking
 Biology, Sociology, and Economics can Inspire Better Networks!
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Technical complexity: human body  technology, devices
Business complexity: macro-economics  e- and m-Commerce
Behavioral complexity: social interaction  service composition
Operational complexity: healing  anti-virus, configuration management
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Business to System Interactions
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Autonomic Control Loop
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Autonomic Management System
 Conceptual Representation
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Autonomic Management System
 The control loop
– Controlled by an autonomic manager that influences the deployment of the
policies that effect decision making within the loop
 DEN-ng (Directory Enabled Network new generation)
– Finite state machines to model behavior and augmenting with ontological models
that embody semantic information that cannot be represented in the UML
– A comprehensive information model for telecommunications, capturing
everything from business concepts (products, service level agreements, and
customers) to low-level device functionality (packet marking, forwarding, and
queuing)
 Model-based policy processing component
– Incorporate policy conflict analysis algorithms that (1) elaborate newly
defined/modified policies so that conflicts are easier to detect, (2) detect sets of
policies that will or potentially could conflict, given certain network context, and
(3) resolve conflicts by modifying or removing policies based on separate
resolution policies or by referring back to the appropriate policy author for a
decision
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Autonomic Management System
 Autonomic Management Architecture
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Autonomic Management System
 FOCALE
– Foundation Observation Comparison Action Learn rEason
– Based on the observation that business objectives, user requirements, and
environmental context all change dynamically
– Two control loop
• Maintenance control-loop
– Used when no anomalies are found
• Adjustment control loop
– Used when one or more policy reconfiguration actions must be performed, and/or new
policies must be codified and deployed
– It is unreasonable to assume that a single entity can maintain all the information
required to realize the FOCALE control loops for large scale networks containing
large numbers of heterogeneous devices
– FOCALE must be a distributed architecture, to the degree that even individual
network devices may incorporate autonomic management software,
implementing the maintenance and adjustment control loops
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FOCALE Autonomic Network Management Architecture
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FOCALE Autonomic Network Management Architecture
 Main functional components
– AM
• Independent of the vendor-specific functionality/data of the underlying managed
resource(s), which facilitates easier communication between AMEs for coordination
of management decision making
– MBTL
• Indepth knowledge of the managed resource(s) to enable it to translate normalized
vendor-specific data gathered from the managed resource(s) into DEN-ng compliant
vendor-neutral data to pass to the policy analyer/PDP and vice versa for
configuration commands
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Basis of MBTL
 Use of ontologies to identify cognitive equivalence across
heterogeneous data models
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Prototype Implementation
 Implementation
– Single FOCALE AME that targets aspects of traffic conditioning in
a simulated IP-based network of an ISP, over which customers
are offered a small number of communications services
– Simulated network is very loosely coupled to the AME
implementation
– Plan to replaced the simulation with real routers that will be
configured by CLI commands generated by the AME and that will
provide context information to the AME via SNMP
– OPNET based simulation is configured to emit information relating
to network events and read and apply new router configurations
generated by the AME
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Prototype Implementation
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Autonomic Networking Scenario
 Different networks, technologies, and business rules
 Conflicting resource and service requirements
 Different capabilities
Maximize network
services according
to business rules
across all customers
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Autonomic Computing and Autonomic Networking
 Autonomic Computing
– Coined by IBM as an analogy to the autonomic nervous system
– Attempts to manage the operation of individual pieces of IT infrastructure through the
introduction of an autonomic manager that implements an autonomic control loop in
which the managed element and the environment in which it operates is monitored
– Autonomic control loop: monitor, analyze, plan, and execute components
 The vision of autonomic computing
– Self-managing IT infrastructure
• Self-configure, self-optimize, self-heal, self-protect (self-* behavior)
 Autonomic Networking
– Burgeoning research area that seeks to integrate results from disciplines ranging from
telecommunications network management to artificial intelligence and from biology to
sociology
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Autonomic Computing and Autonomic Networking
 Focus of research in autonomic network management
– On the development of highly distributed algorithms that seek to optimize one or
more aspects of network operation and/or performance
– Investigating the potential use of biologically-inspired algorithms and processes
– Although work on the development of decentralized, self-management
algorithms is crucial, the deployment of these algorithms will not be sufficient
– Equally important will be the flexible specification and enforcement of the goals
these algorithms collectively seek to achieve
 Policy-based Network Management
– Appropriate management paradigm to facilitate higher-level, human-specified
cognitive decision making
 Little work has been done to date on integrating distributed selfmanagement algorithms with policy-based management
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Autonomic Computing/Networking
 People express at a high level what they want the system to achieve
– Level could be business, or IT
 The system strives to manage its own behavior to optimally satisfy
these multiple criteria, given resource constraints
– Resources: Hardware, software, cost
– Tradeoffs among multiple criteria must be clear
– Self-{configuration, healing, optimization, protection, …} are general classes of
behavioral criteria, but don’t define Autonomic Computing
– The AC challenge is to develop the right technologies and architecture
 People and self-managing systems will work together iteratively, in
partnership with one another
– People will do what they’re best at
– Systems will gradually assume more management burden
• As they become more competent to do so
• As people become more comfortable with this
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Autonomic Computing Element (ACE)
An ACE is an abstraction
that enables an autonomic
system to manage the
functionality of a new or
legacy managed resource
ACEs manage their own
behavior in a standard way
Does not have autonomic
capabilities
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How to Build an Autonomic Element
AC toolkit: Autonomic Integrated Development Environment (AIDE)
 AIDE is part of IBM “Build To
Manage” initiative
– WSDM (Manageability) support
 Eclipse tool generates java ‘stubs’
for multiple runtimes
– Apache Muse (default)
– OSGi, Eclipse 3.1
– WebSphere App Server
 Currently available at
– http://www.alphaworks.ibm.com
 In use by several IBM business
partners and customers
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IBM’s Autonomic Control Loop
 ACEs are building blocks that are arranged to provide higher-level system behavior
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Autonomic Networking in the ACF
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Autonomic Computing Element (ACE)
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IBM Touchpoint vs. FOCALE MBTL
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Autonomic Computing Scalability
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Motorola Labs Autonomic Element
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Scalability in an Autonomic Network
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Autonomic Computing vs. Autonomic Networking
 Autonomic COMPUTING
–
–
–
–
–
–
Assumes homogeneous elements
Control loop is straightforward
System adaptation based on ITIL (or eTOM)
Focused on self-config, -heal, -optimize, -protect
Policies determine what actions to take
Goal is to orchestrate static behavior
 Autonomic NETWORKING
–
–
–
–
Assumes heterogeneous elements
Control loop adapts based on network, task, …
System adaptation based on DEN-ng & ontologies
Focused on self-governance and –knowledge, which drive all other self-*
functions
– Policies determine overall behavior of the ACE
– Goal is to orchestrate dynamic behavior
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Research Challenges
 Key Challenges
– Providing self-* functionality
• Mapping management information and commands in the legacy programming
models to an autonomic programming model
• Orchestrate behaviour in a way that legacy devices can understand
– The complexity of applying various techniques may preclude their use in certain
platforms
•
•
•
•
Model-Driven Engineering
Aspect-Oriented Software Development
Reverse Engineering
Generative Programming
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Challenge: Architecture
 AE: How to coordinate multiple threads of activity?
– AE’s live in complex environments
– Multiple task instances and types
Define set of fundamental architectural
principles from which self-* emerges
S
– concurrent, asynchronous
E
Autonomic Manager
– Multiple interacting expert modules
Analyze
 AE: How to detect/resolve conflicts arising from
– Internal decisions by independent expert modules
– External directives (possibly asynchronous)
– Internal policies vs. external directives
Plan
Monitor Knowledge
S
Execute
E
Managed Element
 System-level: Enable more flexible,
service-oriented patterns of interaction
An Autonomic Element
– As opposed to traditional top-down, hierarchical systems management
– Multi-agent architecture
– Communication
– Representing and reasoning about needs, capabilities, dependencies
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Challenge: Policy
 Human interface
– Authoring and understanding policies
– Avoiding or ameliorating specification errors
Policy: “Set of guidelines or
directives provided to autonomic
element to influence its behavior”
 Developing a universal representation and grammar
– Many different application domains, disciplines
– Many different flavors of policy
– Covers service agreements too?
S
E
Autonomic Manager
Analyze
Monitor
 Algorithms that operate upon policies (and agreements?)
Plan
Knowledge Execute
S
E
Managed Element
– Automated derivation of actions (e.g. planning, optimization)
– Automated derivation of lower-level policies from high-level policies
• E.g. “Maximize profit from this set of service contracts”
 Conflict resolution
– Both design time and run time
– Need to establish protocols, interfaces, algorithms
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Three flavors of (policy = “decision-making guide”)
Possible
State
s1
 Action rule
– If (S) then do a2
– Results implicitly in desired state s2
a1
 Goal
Current
State
S
– Achieve a most desired state s2
– Compute a2 most likely to result in s2
– Assumes that most desired state can be determined a priori
Possible
State
s2
a2
a3
Possible
State
s3
 Utility function
– Achieve state s with maximal net value V(s) – C(aSds)
– Benefit and burden of being explicit about value
– States have intrinsic value; value of policy is a derived quantity
[More levels of
code hierarchy]
Machine
code
Workflows
Programming
Rules
Decisiontheoretic
Planning
Element
Optimization
Adapters,
Translators
Element
Actions Generative Goals
Planning
utility
functions
Modeling,
Optimization
System
utility
functions
Higher-level specifications
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Challenge: Human-System Interface
 Develop new languages, metaphors and translation technologies that
enable humans to monitor, visualize, and control AC systems
– Specify goals and objectives to AC systems, and visualize their potential effect
– Techniques must be
– Sufficiently expressive of preferences regarding cost vs. performance,
security, risk and reliability
– Sufficiently structured and/or naturally suited to human psychology and
cognition to keep specification errors to an absolute minimum
– Robust to specification errors
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Challenge: Learning
 Single element level
Establish theoretical foundation for understanding
and performing learning and optimization in multiagent systems.
– AE needs to learn a model of itself and environment quickly; environment is noisy, and
dynamic in both state and structure
– On-line, so exploration of the space can be costly and/or harmful
– May be several hundreds of tunable parameters!
– Maybe only a few dozen are relevant, but which ones?
– Some of them can only be changed upon reboot – is it worthwhile?
 System level
– Multi-agent system: several interacting learners
– What are good learning algorithms for cooperative, competitive systems?
– What are conditions for stability?
– What is sensitivity to perturbations?
– Opportunities for layered learning
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Challenge: Negotiation
 Develop and analyze
– Methods for expressing or computing
preferences
– Negotiation protocols
– Negotiation algorithms
 Establish theoretical foundation for negotiation
– Explore conditions under which to apply
– Bilateral
– Multi-lateral (mediated, or not)
– Supply-chain
– Study how system behavior depends on
mixture of negotiation algorithms in AE
population
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Challenge: Control and Harness Emergent
Behavior
 Understand, control, and exploit emergent behavior in autonomic
systems
– How do self-*, stability, etc. depend on
– Behaviors and goals of the autonomic elements
– Pattern and type of interactions among AEs
– External influences and demands on system
– Invert relationship to attain desired global behavior
– How?
– Are there fundamental limits?
 Develop theory of interacting feedback loops
– Hierarchical
– Distributed
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Conclusions
 Autonomic Computing is a grand challenge, requiring advances in several fields of
science and technology
– Policy, planning, learning, knowledge representation, multi-agent systems, negotiation,
emergent behavior
– Human-system interfaces
 Integrating these technologies to support self-management in complex, realistic
environments is a research challenge in itself
– What are the best architectures and design patterns? Role of (multi-)agent systems?
– Building system prototypes is key to developing and validating AC technology and
architecture
 Technology to Manage Technology
– Autonomic Computing/Networking does not replace people, it empowers them
– Context-awareness is achieved by enabling the system to reconfigure itself, based on
• Changing business goals
• Changing user needs
• Changing environmental conditions
– Reconfiguration is model-driven, and uses ontologies to reason to build its conclusions
– This is slowly becoming a reality, but should not be a panacea
Autonomic Network Management
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•DPNM Lab.
How to realize
Autonomic Network Management?
Thank you for your attention
Autonomic Network Management
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Joon-Myung Kang
•DPNM Lab.
eliot@postech.ac.kr
References
1.
2.
IBM Corporation, “An architectural blueprint for autonomic computing,” White paper, 2003
J.O. Kephart and D.M. Chess, “The vision of autonomic computing,” Computer, vol. 36, no.
1, Jan. 2003, pp. 41-52
3. J.O. Kephart., “Research Challenges of Autonomic Computing,” Proceedings of the 27th
International Conference on Software Engineering, 2005
4. John Strassner, “Autonomic Systems and Networks: Theory and Practice”, NOMS 2008
Tutorial, 2008
5. R. Sterritt, “A concise introduction to autonomic computing,” Journal of Advanced
Engineering Informatics, Engineering Applications of Artificial Intelligence, Elsevier
Publishers, Vol. 19, pp. 181-187, 2005
6. Brendan Jennings, “Towards Autonomic Management of Communications Networks”, IEEE
Communications Magazine, Vol. 45, Issue 10, pp. 112-121, 2007
7. http://www.autonomiccomputing.org/
8. http://www.research.ibm.com/autonomic/
9. http://www.autonomic-communication-forum.org/
10. http://dnac.org/autonomic-networking/
Autonomic Network Management
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•DPNM Lab.