1. Autonomic (Grid) Computing Introduction, Motivations, Overview
Manish Parashar and Omer Rana
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

2. 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, real-time 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
Individual systems approaching human limits
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

3. 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
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.
Information Uncertainty
Availability, resolution, quality of information
Devices capability, operation, calibration
Trust in data, data models
Semantics
Application Uncertainty
Dynamic behaviors
space-time adaptivity
Dynamic and complex couplings
multi-physics, multi-model, multi-resolution, ….
Dynamic and complex (ad hoc, opportunistic) interactions
Software/systems engineering issues
Emergent rather than by design
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

4. 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 , 2005.
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5. Convergence of Information Technology and Biology
Without requiring our conscious involvement
when we run, it increases
our heart and breathing rate
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6. 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
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

7. Ashby’s Ultrastable System
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

8. 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)
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

9. 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.
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

10. 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.
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

11. 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.
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

12. 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

13. 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

14. Basic Exploratory Behavior
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15. Attraction to Light
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16. Multiple Lights
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17. Charging Home
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18. Obstacle Avoidance
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19. The Mirror Dance
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20. Elmer and Elsie Dance
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21. Home Sweet Home
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22. 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
Monitoring S2
Audit S3*
Control S3
Intelligence S4
Policy S5
Offers an extensible, recursive, model-based architecture, devolving autonomy to sub-systems
Here
and
Now
Future
Direction
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23. VSM – Stafford Beer
From Wikipedia
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

24. Autonomic Computing Characteristics (IBM)
By IBM
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25. 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 Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

26. Autonomic Computing – Conceptual Architecture from IBM
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

27. Supporting Fault Tolerance (Gaston, George, Park)
normal object event
Object
Events
Fault
Failure Up
Down 1 2 3
External Monitoring
(i.e. objects, network)
Defensive Module
Offensive Module
MANAGER
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28. Autonomic Elements: Structure
Ack. IBM
Fundamental atom of the architecture
Managed element(s)
Database, storage system, server, software app, etc.
Plus one autonomic manager
Responsible for:
Providing its service
Managing its own behavior in accordance with policies
Interacting with other autonomic elements
Managed Element E S
Monitor
Analyze
Execute
Plan
Knowledge
Autonomic Manager
An Autonomic Element
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

29. 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 Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

30. Autonomic Systems: Composition of Autonomic Elements
Ack. IBM
Reputation
Authority
Network
Registry
Event
Correlator
Database
Monitor
Server
Workload
Manager
Storage
Negotiator
Broker
Provisioner
Sentinel
Aggregator
Arbiter
Planner
Need to have flexibly composable autonomic systems as opposed to monolithic ones.
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

31. Autonomic Grid Computing & Pervasive Grid Environments – (Some) Research Issues & Opportunities
Programming systems/models for data integration and runtime self-management
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

32. 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
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)

33. Some Information Sources
“Autonomic Computing: Concepts, Infrastructure and Applications,” M. Parashar and S. Hariri (Ed.), CRC Press, ISBN (Available at
Autonomic Computing Portal
IEEE International Conference on Autonomic Computing
IEEE Task Force on Autonomous and Autonomic Systems
Autonomic Grid Computing Tutorial, CCGrid '07 (Manish Parashar)