Transcript Autonomic Computing Tutorial

Autonomic (Grid) Computing
Introduction, Motivations, Overview
Manish Parashar and Omer Rana
Pervasive Grid Environments - Unprecedented
Opportunities
• Pervasive Grids Environments
– Seamless, secure, on-demand access to and aggregation of,
geographically distributed computing, communication and information
resources
• Computers, networks, data archives, instruments, observatories, experiments,
sensors/actuators, ambient information, etc.
– Context, content, capability, capacity awareness
– Ubiquity, mobility
• Knowledge-based, information/data-driven, context/content-aware
computationally intensive, pervasive applications
– Symbiotically and opportunistically combine services/computations, realtime information, experiments, observations, and to manage, control,
predict, adapt, optimize, …
• A pervasive paradigm
– seamless access
• resources, services, data, information, expertise, …
– seamless aggregation
– seamless (opportunistic) interactions/couplings
Pervasive Grid Environments – Unprecedented
Challenges: Complex & Uncertainty
• System Uncertainty
– Very large scales
– Ad hoc structures/behaviors
• p2p, hierarchical, etc, architectures
– Dynamic
• entities join, leave, move, change
behavior
• Information Uncertainty
– Availability, resolution, quality of
information
– Devices capability, operation,
calibration
– Trust in data, data models
– Semantics
– Heterogeneous
• capability, connectivity, reliability,
guarantees, QoS
– Lack of guarantees
• components, communication
– Lack of common/complete
knowledge (LOCK)
• number, type, location, availability,
connectivity, protocols, semantics,
etc.
• Application Uncertainty
– Dynamic behaviors
• space-time adaptivity
– Dynamic and complex couplings
• multi-physics, multi-model, multiresolution, ….
– Dynamic and complex (ad hoc,
opportunistic) interactions
– Software/systems engineering
issues
• Emergent rather than by design
Integrating Biology and Information Technology: The
Autonomic Computing Metaphor
• Current programming paradigms, methods, management
tools are inadequate to handle the scale, complexity,
dynamism and heterogeneity of emerging systems
• Nature has evolved to cope with scale, complexity,
heterogeneity, dynamism and unpredictability, lack of
guarantees
– self configuring, self adapting, self optimizing, self healing, self
protecting, highly decentralized, heterogeneous architectures that
work !!!
• Goal of autonomic computing is to build a self-managing
system that addresses these challenges using high level
guidance
– Unlike AI duplication of human thought is not the ultimate goal!
“Autonomic Computing: An Overview,” M. Parashar, and S. Hariri, Hot Topics,
Lecture Notes in Computer Science, Springer Verlag, Vol. 3566, pp. 247-259, 2005.
Convergence of Information Technology and
Biology
Without requiring our
conscious involvement
- when we run, it increases
our heart and breathing
rate
Adaptive Biological Systems
• The body’s internal mechanisms
continuously work together to maintain
essential variables within physiological
limits that define the viability zone
• Two important observations:
– The goal of the adaptive behavior is
directly linked with the survivability
– If the external or internal environment
pushes
the
system
outside
its
physiological equilibrium state the system
will always work towards coming back to
the original equilibrium state
Ashby’s Ultrastable System
Environment
Motor
channels
Essential Variables
Sensor
channels
Reacting Part R
Step Mechanisms/Input Parameter S
Self-Adaptive Software
•
•
•
Defined by Laddaga in the 1997 DARPA Broad Agency Announcement as:
– “...software that evaluates its own performance and changes behaviour
when the evaluation indicates that it is not accomplishing what the
software is intended to do...”.
To adapt, the system reacts to environmental change - the problem is
recognising the need for change, then planning, enacting and verifying the
change - these are management issues - self-managing systems
Progress to date has been informed by three guiding metaphors
–
–
–
control systems theory
dynamic planning systems
self-aware or reflective systems.
• “Managing complexity is a key goal of self-adaptive software. If a
program must match the complexity of the environment in its own
structure it will be very complex indeed! Somehow we need to be
able to write software that is less complex than the environment in
which it is operating yet operate robustly.” (Robertson, Laddaga et
al, 2000)
A View of Biological Adaptation and Evolution
•
Living systems can be described in terms of interdependent variables:
– each capable of varying over a range with upper and lower bounds, e.g. bodily
temperature, blood pressure, heart rate etc.
– environmental change may cause fluctuations but bodily control
mechanisms autonomically act to maintain variables at a stable level, i.e.
homeostatic equilibrium with the environment
•
Three types of adaptation to environmental disturbance are available to
higher organisms:
– Short-term change - e.g. Environmental temperature change moves the bodily
temperature variable to an unacceptable value. This rapidly induces an
autonomic response in the (human) organism i.e. either perspiring to dissipate
heat or shivering to generate heat. Such adaptation is quickly achieved and
reversed.
– Somatic change - prolonged exposure to environmental temperature change
results in the impact of the change being absorbed by the organism i.e.
acclimatization. Such change is slower to achieve and reverse.
– Genotypic change - adaptation through mutation and hence evolution. A
species adapts to change by shifting the range of some variables. e.g. in a cold
climate grow thicker fur. Such genotypic change is recorded at a cellular level
and becomes hereditary and is irreversible in the lifetime of the individual.
Cybernetics: The Foundations of the Bridge
•
A cross-disciplinary approach developed in the 1940’s and broadly
encompassing contributions from biology, social sciences and nascent
computer science.
• Wiener defined cybernetics as
“the science of communication and control in the animal and machine”.
• Ashby’s contribution...
–
–
–
Both the system and the environment in which it exists are represented by a
set of variables that represent that form a state-determined system
Consequently, the environment is defined as those variables whose
changes affect the system and those variables that are affected by the
system.
Complexity as Variety, i.e. The number of different states a system can
adopt.
Ashbean Cybernetics
• The Homeostat - ultra-stable system
capable of returning to stability after it
has been disturbed in a way not
envisaged by the designer.
• Self-vetoing homeostasis
• “Variety Engineering”
– The notion of balancing the varieties of
systems with different variety levels
– Environment - huge variety
– Operation - much less variety
– Management - even less variety
• Achieved through attenuation and
amplification
• The Law of Requisite Variety control can
only be attained if the variety of the
controller is at least as great as the situation
to be controlled.
Man agem en t
Un it
V
Environment
System
V
V
K ey
V
= Variety
= Attenuation
= Amplification
The Homeostat
• Ashby designed a “Homeostat” device, consisting of
four pivoting magnets, motion constraints, and various
electrical connections and switches, to demonstrate
what he called an “ultrastable” system—one that
would return to homeostasis regardless of the
magnitude of its perturbations
http://www.hrat.btinternet.co.uk/Homeostat.html
W. Grey Walter
• Walter Grey Walter, author of The Living Brain (1953),
experimented with electro-mechanical “turtles”
– Family “Machina Speculatrix”
– Genus “Testudo” (tortoise)
• Built between Easter 1948 and Christmas 1949, the
first two of these turtles were Elmer and Elsie, after
ELectro MEchanical Robots, Light-Sensitive, with
Internal and External stability
– “Stability” may have been related to Ashby’s homeostasis
– “External” might be intended to distinguish Testudo from
Homeostat
http://www.ias.uwe.ac.uk/Robots/gwonline/gwonline.html
Basic Exploratory Behavior
http://extremenxt.com/walter.htm
Attraction to Light
Multiple Lights
Charging Home
Obstacle Avoidance
The Mirror Dance
Elmer and Elsie Dance
Home Sweet Home
Managerial Cybernetics
•
•
•
Beer’s VSM implements a control &
communication structure via
hierarchies of homeostats (feedback
loops)
6 major systems ensure ‘viability’ of
the system
– Implementation
S1
– MonitoringS2
Here
– Audit
S3*
and
Now
– Control
S3
– Intelligence
S4
Future
– Policy
S5
Offers an extensible, recursive, modelDirection
based architecture, devolving autonomy to
sub-systems
TOTAL
Environment
F IVE - Polic y
Homeostasis
Future
System FOUR
Exte rnal & F uture
S elf-refere nce
S imula tion
P lann in g
System TWO
anti-oscillatory
System THREE
Inte rna l & Curre nt
S elf-organiz at io n
A uton omic re gu la tion
System THREE*
Sporadic Audit
TWO Local
ONE
anti-oscillatory
Local
Regulatory
TWO Local
anti-oscillatory
ONE
Local
Regulatory
TWO Local
anti-oscillatory
ONE
Local
Regulatory
Na me of the Viable Syste m in Focus:
The Viable System M ode l
Form at © S. Be er 1985
From Wikipedia
VSM – Stafford Beer
•
Consists of 5 interacting sub-systems – mapped to organizational structures
–
–
–
Systems 1 to 3: “here and now” (current view);
System 4: “then and there” (strategic response to external, environment & future demands);
System 5 – balancing “here and now” with “then and there”
•
System 1 in a viable system contains several primary activities. Each System 1 primary
activity is itself a viable system due to the recursive nature of systems. These are
concerned with performing a function that implements at least part of the key
transformation of the organisation.
•
System 2 represents the information channels and bodies that allow the primary
activities in System 1 to communicate between each other and which allow System 3
to monitor and co-ordinate the activities within System 1.
•
System 3 represents the structures and controls that are put into place to establish
the rules, resources, rights and responsibilities of System 1 and to provide an
interface with Systems 4/5.
•
System 4 - The bodies that make up System 4 are responsible for looking outwards to
the environment to monitor how the organisation needs to adapt to remain viable.
•
System 5 is responsible for policy decisions within the organisation as a whole to
balance demands from different parts of the organisation and steer the organisation as
a whole
Autonomic Computing Characteristics (IBM)
By IBM
Autonomic Grid Computing – A Holistic Approach
• Computing has evolved and matured to provide specialized solutions to
satisfy relatively narrow and well defined requirements in isolation
– performance, security, dependability, reliability, availability, throughput,
pervasive/amorphous, automation, reasoning, etc.
• In case of pervasive Grid applications/environments, requirements,
objectives, execution contexts are dynamic and not known a priori
– requirements, objectives and choice of specific solutions (algorithms,
behaviors, interactions, etc.) depend on runtime state, context, and content
– applications should be aware of changing requirements and executions
contexts and to respond to these changes are runtime
• Autonomic Grid computing - systems/applications that self-manage
– use appropriate solutions based on current state/context/content and based
on specified policies
– address uncertainty at multiple levels
– asynchronous algorithms, decoupled interactions/coordination, content-based
substrates
Autonomic Computing – Conceptual
Architecture from IBM
Supporting Fault Tolerance (Gaston, George, Park)
normal object event
Up
Object
Events
Fault
1
Failure
2
3
Down
MANAGER
Defensive Module
External Monitoring
(i.e. objects, network)
Offensive Module
Autonomic Elements: Structure
Ack. IBM
• Fundamental atom of the
architecture
– Managed element(s)
• Database, storage system,
server, software app, etc.
Autonomic Manager
Analyze
Plan
– Plus one autonomic manager
• Responsible for:
– Providing its service
– Managing its own behavior in
accordance with policies
– Interacting with other
autonomic elements
Monitor
Knowledge
S
Execute
E
Managed Element
An Autonomic Element
Autonomic Elements: Interactions
• Relationships
– Dynamic, ephemeral,
opportunistic
– Defined by rules and
constraints
– Formed by agreement
• May be negotiated
– Full spectrum
• Peer-to-peer
• Hierarchical
– Subject to policies
Ack. IBM
Autonomic Systems: Composition of Autonomic
Elements
Ack. IBM
Workload
Manager
Arbiter
Planner
Provisioner
Registry
Reputation
Authority
Network
Broker
Server
Workload
Manager
Network
Sentinel
Event
Correlator
Monitor
Server
Database
Server
Negotiator
Aggregator
Sentinel
Database
Registry
Broker
Monitor
Storage
Storage
Storage
Monitor
Autonomic Grid Computing & Pervasive Grid
Environments – (Some) Research Issues & Opportunities
• Programming systems/models for data integration and runtime selfmanagement
– components and compositions capable of adapting behavior and
interactions
– policy driven deductive engine
– correctness, consistency, performance, quality-of-service constraints
• Content-based asynchronous and decentralized discovery and access
services
– semantics, metadata definition, indexing, querying, notification
• Data management mechanisms for data acquisition and transport with
real time, space and data quality constraints
– high data volumes/rates, heterogeneous data qualities, sources
– in-network aggregation, integration, assimilation, caching
• Runtime execution services that guarantee correct, reliable execution
with predictable and controllable response time
– data assimilation, injection, adaptation
• Security, trust, access control, data provenance, audit trails, accounting
Conclusion
• Emerging Pervasive Grid Environments
– Unprecedented opportunity
• can enable a new generation of knowledge-based, data and information
driven, context-aware, computationally intensive, pervasive applications
– Unprecedented research challenges
• scale, complexity, heterogeneity, dynamism, reliability, uncertainty, …
• applications, algorithms, measurements, data/information, software
• Autonomic Grid Computing
– Using inspiration from nature and biology to addresses the
complexity of pervasive Grid environments
Some Information Sources
• “Autonomic Computing: Concepts, Infrastructure and
Applications,” M. Parashar and S. Hariri (Ed.), CRC Press, ISBN
0-8493-9367-1 (Available at http://www.crcpress.com/)
• Autonomic Computing Portal
– http://www.autnomiccomputing.org
• IEEE International Conference on Autonomic Computing
– http://www.autonomic-conference.org
• IEEE Task Force on Autonomous and Autonomic Systems
– http://tab.computer.org/aas/