Download What is autonomic computing (communications)

Architectures for
Autonomic
Communications
Visa Holopainen, [email protected]
What is autonomic computing
(communications)
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Continuous automatic
improvement of the system
functioning based on measured
feedback and pre-defined goals
Any types of improvement goals
can be set
Hierarchical systems possible
(Managers of manager)
The manager(s) can be software
or hardware component(s)
External manager component
enhances modularity
The conversion/mapping layer
makes the system more platformindependent
The basic concept is simple,
algorithms (code) make the
difference in any particular
autonomic architecture
Why autonomic computing
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A study made in 2002 estimates that half of all network outages result from
configuration errors made by administrators [1]
Most autonomic architectures try to tackle the increasing complexity of
network configurations by adding some (complicated) distributed software to
the network
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Issue: Will there be problems in code base management (security, updates, etc.)
The objective is to get to a point in which only high-level configurations are
injected into the network and the network figures out independently what
should be done at physical level
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Issue: more powerful configuration commands -> more potential for network-wide
problems
 Solution: architecture should contain reversible operations and should allow easy
access for administrators if needed
AUTONOMIA: An Autonomic Computing
Environment, X. Dong, S. Hariri, L. Xue, H. Chen, M. Chang, S.
Pavuluri, S. Rao, 2003
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Provides control and management services to support the development
and deployment of smart (intelligent) applications
Application developer can specify the application’s autonomic
requirements (e.g. self-optimizing and self-healing) through Application
Management Editor (AME)
The next step is to utilize the Autonomic Middleware Service (AMS) to
build the appropriate application execution environment that can
dynamically control the allocated resources to maintain the application
requirements during application execution
Self-healing
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For each fault type (system, component or agent), there is a software agent
(fault handler) that is responsible for executing the procedures
 During the monitoring phase, the appropriate fault handler focuses on detecting
faults once they occur
 Recovery procedures are run if fault is detected
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Self-optimization
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Similar software agents than in self-healing
The implementation
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The mobile agent system (MAS) for Autonomia is designed to provide
mobile agents a uniform execution environment independent of the
underlying hardware architecture and operating system
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Java/Jini platform used
Jini (http://www.jini.org/)
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A network architecture for the construction of distributed systems where scale,
rate of change and complexity of interactions within and between networks are
extremely important
 The interaction can use any type of networking technology such as RMI,
CORBA, or SOAP
The architecture
Self-aware management of IP networks
with QoS guarantees, F. Krief, 2004
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Self-aware management
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Allows network to react and adapt to changes of situation
Operators can focus on defining high-level policies (operational objectives)
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Policy-based management
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Business policy -> Network level policy -> Low-level policy
Agent may transport business policies so that the negotiation and the decision
can be carried out locally (no need for policy exchange protocol)
An architecture was described in the paper that implements the principles of
self-aware mgmt and policy-based mgmt
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Faster & cheaper
Each logical component improves itself and alerts the component upper in the
hierarchy if SLA cannot be satisfied without modifications to upper level
objectives
The solution is said to allow the self-configuration, self-provisioning and selfmonitoring of service as well as proactive SLA management
No testing, just a framework
An automated policy-based management
framework for differentiated
communication systems, N. Samaan, A. Karmouch, 2005
Main theme: policy based management
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Policies can be created dynamically (in contrast to traditional model in which
policies must be created statically)
A framework was proposed to decouple
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The mapping of high-level policies to network level objectives
The functionality of adapting network components behavior
The component that takes care of decoupling is Automated Policy
Adaptor (APA)
Network operators can inject business objectives to APA using a Graphical
User Interface
Users/user applications can also specify requirements to APA in form of
policies
User preferences ans business goals for QoS are translated into network-level
objectives
APA can modify policies at runtime based on feedback from the network
Novelty of the proposition lies in that given sets of objectives, constraints
and policies are assembled at runtime -> flexibility to network control
Both fixed and wireless networks can be handled by the architecture
Slight increase in network throughput when APA was compared to static
configuration (using a simulator)
The APA architecture
The testing scenario (in simulator)
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Initially, 40%, 30%, and 30% of
the available bandwidth are set for
EF, AF, and BE, respectively, for
domains A, B, and C
Traffic was generated from D to A
with sending rate of 6 Mb/s, while
A is moving from domain A to B at
t=50, and then from B to C at t=80
As the user moves from one
domain to the other, the policy P is
translated to different networklevel objectives by the APA at
domain A depending on the
location of the user.
See the paper for detailed testing
parameters
Simulation results
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Throughput
comparison
between the
statically configured
network and the
proposed scheme.
(a) = Achieved
throughput in case
of static
configurations.
(b) = Achieved
throughput in case
of APA.
An Architecture for Coordinating Multiple
Self-Management Systems, S. Cheng, A. Huang, D.
Garlan, B. Schmerl, P. Steenkiste, 2004
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Some possible self-management systems and their responsibilities
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Self-configuring system
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Self-healing system
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Monitor and tune resources automatically
Self-protecting system
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Discover, diagnose and react to disruptions
Self-optimizing system
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Adapt automatically to changes in the environment
Anticipate, detect, identify and protect itself from attacks
How to deal with (possibly) conflicting decisions coming from different selfmanagement modules?
Solution: separate logical layers for data presentation (translation) and
system access
Rainbow: Architecture-Based SelfAdaptation with Reusable Infrastructure,
D. Garlan, S. Cheng, A. Huang, B. Schmerl, P. Steenkiste, 2004
Mechanisms that support self-adaptation currently exist in the form of
programming language features such as exceptions and in algorithms
such as fault tolerant protocols
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The Rainbow-architecture tries to solve two problems
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These mechanisms are often highly specific to the application and tightly
bound to the code. As a result, self-adaptation in today’s systems is costly to
build, difficult to modify, and usually provides only localized treatment of
system faults
How to handle a wide variety of systems using external control
How to make 1) in a cost-effective manner
The solution: re-usable infrastructure with a mechanism to customize the
infrastructure to particular application’s needs
Reusable Rainbow units
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System-layer infrastructure
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Architecture-layer infrastructure
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Low-level system information can be
published by or queried from the
probes. Additionally, a resource
discovery mechanism can be
queried for new resources based on
resource type and other criteria
Gauges aggregate information from
the probes and update the
appropriate properties in the
architectural model
Translation infrastructure
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Translation repository maintains
translations
Architectural style
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While reusable infrastructure helps reduce the costs of adding selfadaptation to systems, it is also possible to leverage commonalities in
system architecture to encapsulate adaptation knowledge for various
system classes
To capture system commonalities, Rainbow adapts the notion of an
architectural style
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An architectural style characterizes a family of systems related by shared
structural and semantic properties
Case study 1: Client-server system
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The client and server components are
implemented in Java and provide remote
method invocation (RMI) interfaces for the
effectors to use in performing adaptation
operations
Clients connected to a server group send
requests to the group’s shared request queue,
and servers that belong to the group grab
requests from the queue
Focus on response time that clients percieve
Architectural style created for the system
(Client.responseTime, Server.load,
ServerGroup.load, Link.bandwidth,
ServerGroup.addServer(), Client.move(),…)
If response time for a client is too long, the
Adaptation Engine executes a response
strategy (move client to another group, add
server to group, …)
Towards a Model-Driven Architecture for
Autonomic Systems, D. Gračanin, S. Bohner, M. Hinchey,
2004
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COUGAAR
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Agents (program code) running on Java virtual machines
Virtual machines are located in nodes of the network
Tasks sent between agents
One must locate the most suitable agent for the task
COUGAAR supports hierarchical task decomposition
COUGAAR provides facilities to measure and propagate performance metrics
collected at all levels of the architecture
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Platform specific measurements are taken by the computer system level
instrumentation and are translated into device-independent values.
Measured data is used by a Servlet (web) and adaptation engine
provides adaptive control through the Adaptivity Engine that makes use of
the measurements collected by the metrics service
COUGAAR agents
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An agent consists primarily of a blackboard and a set of plugins. The blackboard
is essentially a container of objects that adheres to publish/subscribe semantics
 Agents can move from one node to another through serialization
Autonomic WWW Server Management
with Distributed Resources, T. Araki, 2004
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Problem: Computing resources aren’t share effectively across organization
boundaries (only within organizations)
Solution: A working and tested concept that provides means to share
computing (server) resources across organization boundaries
Computing resources are rented from different organizations (new business
model)
Supports J2EE systems
Main component: Autonomic Web Server Manager
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Monitors QoS (response time to client requests)
 Autonomically manages the computational resources (addition, removal) based
on the response time
Hierarchical Model-based Autonomic
Control of Software Systems, M. Litoiu, M.
Woodside, T. Zheng, 2005
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A system to
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Autonomically react to workload changes
 Search for resource-optimal configurations
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Used in
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Information- and transaction-based software applications
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Provisioning Controller
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Adds and removes hardware components to the system at runtime (for
example web servers to a cluster)
Application Contoller
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E-commerce, insurance claim submission, web banking, etc.
Balances resources and workload across system
Component Controller
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Adjusts components’ run time parameters to achieve desired QoS level
A Framework for Self-Management of
Hybrid Wireless Networks Using
Autonomic Computing Principles, C. Shen, D.
Pesch, J. Irvine, 2005
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Hybrid Wireless Networks
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A framework proposition for the autonomic
management of hybrid wireless networks
In the paper the authors state that they are going to
use Artificial Intelligence techiques (neural networks,
fuzzy logic and Q-learning) in the management of
hybrid wireless networks to ease the management
burden
Q-learning
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Scalable and adaptive wireless network architecture
utilizing a mixture of cellular and ad hoc multi-hop routing
(less base stations needed)
Drawbacks: routing complexity, radio resource
heterogenity, network infrastructure design growth ->
difficult to manage
A variant of reinforcement learning
Autonomy is supposed to be implemented at MAC,
routing and application levels independently
No algorithms, implementation or testing is presented
Autonomic system for mobility support
in 4G networks, P. Vidales, J. Baliosian, J. Serrat, G. Mapp, F.
Stajano, A. Hopper, 2005
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Excellent paper with detailed description of the concept
Access technologies in 4G networks will be heterogenous (Satellite,
UMTS, IEEE 802.16a, IEEE 802.11, etc.)
Heterogenous networks introduce additional complexities
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Many different types of available access points, handover triggered by
multiple events, execution methods depend on context, etc.
More intelligence needed to handle these complexities
An architecture called PROTON was presented
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The architecture is supposed to
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”know” itself, it’s environment and context
Configure and reconfigure itself under varying and unpredictable conditions
Optimizes it’s functionality constantly
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Architecture divided to network- and host side components
Network side components
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A policy specification language is
used by operator to insert policies
to the network side of the
architecture through the policy
editor
Model Deployment Module sends
suitable policies to mobile devices
Policy master that acts as a PDP
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Sending is done by sending a
Java object through JNI
An example of a policy: ”Mobile
devices that are treveling at
speeds over 90 km/h should never
connect lo lower level access
point (like from GSM cell to
WLAN-hotspot)”
Host-side components
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Sentinels retreive dynamic data
(like the speed of the node from
GPS) and generate events
based on the data
Policy master
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receives events (like Transitionto-pedestrian-speed-event) from
sentinels
Decides the possible actions to
execute
Sends these actions to the
Enforcement Layer
Enforcement Layer acts as a
PEP
The Enforcement Layer
captures router advertisements
before they reach the IPv6handover-module and hand only
”legal” information to the
hadover procedure
Example sentinel code