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Autonomic (Grid) Computing Introduction, Motivations, Overview. Manish Parashar and Omer Rana. Pervasive Grid Environments - Unprecedented Opportunities. Pervasive Grids Environments

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autonomic grid computing introduction motivations overview

Autonomic (Grid) Computing Introduction, Motivations, Overview

Manish Parashar and Omer Rana

pervasive grid environments unprecedented opportunities
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
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

Pervasive Grid Environments – Unprecedented Challenges: Complex & Uncertainty
integrating biology and information technology the autonomic computing metaphor
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.

slide5
Convergence of Information Technology and Biology
  • Without requiring our conscious involvement
  • when we run, it increases
  • our heart and breathing rate
adaptive biological systems
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
self adaptive software
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
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
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
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.
the homeostat
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
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
Basic Exploratory Behavior

http://extremenxt.com/walter.htm

managerial cybernetics
Future

Direction

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

vsm stafford beer
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
autonomic grid computing a holistic approach
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
slide27
normal object event

Object

Events

Fault

Failure

1

2

3

Up

Down

External Monitoring

(i.e. objects, network)

Defensive Module

Offensive Module

MANAGER

Supporting Fault Tolerance (Gaston, George, Park)

autonomic elements structure
Autonomic Manager

Analyze

Plan

Monitor

Execute

Knowledge

Managed Element

S

E

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

An Autonomic Element

autonomic elements interactions
Autonomic Elements: Interactions

Ack. IBM

  • Relationships
    • Dynamic, ephemeral, opportunistic
    • Defined by rules and constraints
    • Formed by agreement
      • May be negotiated
    • Full spectrum
      • Peer-to-peer
      • Hierarchical
    • Subject to policies
autonomic systems composition of autonomic elements
Reputation

Authority

Network

Network

Registry

Registry

Workload

Manager

Workload

Manager

Event

Correlator

Database

Database

Storage

Storage

Storage

Broker

Negotiator

Planner

Arbiter

Sentinel

Monitor

Broker

Monitor

Sentinel

Provisioner

Monitor

Aggregator

Server

Server

Server

Autonomic Systems: Composition of Autonomic Elements

Ack. IBM

autonomic grid computing pervasive grid environments some research issues opportunities
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
conclusion
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
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/
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