1. Autonomic Computing Manish Parashar
The Applied Software Systems Laboratory
Rutgers, The State University of New Jersey
UPP – Autonomic Computing
Mt. St. Michel, France
September 15 – 17, 2004
Autonomic Computing Tutorial, ICAC 2004

2. Emerging Pervasive Information Grids - Smaller/Cheaper/Faster/Powerful/Connected ….
Explosive growth in computation, communication, information and integration technologies
computing & communication is ubiquitous
Pervasive ad hoc “anytime-anywhere” access environments
ubiquitous access to information
peers capable of producing/consuming/processing information at different levels and granularities
embedded devices in clothes, phones, cars, mile-markers, traffic lights, lamp posts, medical instruments …
“On demand” computational/storage resources, services
UPP, September 15-17, 2004

3. Unprecedented Complexity, Uncertainty …
Very large scales
million of entities
Ad hoc (amorphous) structures/behaviors
p2p/hierarchical architecture
Dynamic
entities join, leave, move, change behavior
Heterogeneous
capability, connectivity, reliability, guarantees, QoS
Unreliable
components, communication
Lack of common/complete knowledge
number, type, location, availability, connectivity, protocols, semantics, etc.
UPP, September 15-17, 2004

4. Convergence of Information Technology and Biology
Our system programming paradigms, methods and management tools seem to be inadequate for handling the scale, complexity, dynamism and heterogeneity of emerging pervasive Grid systems
Biological systems have evolved strategies to cope with dynamic, complex, highly uncertain constraints
UPP, September 15-17, 2004

5. 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
UPP, September 15-17, 2004

6. Ashby’s Ultrastable System
UPP, September 15-17, 2004

7. Autonomic Computing?
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 !!!
e.g. the autonomic nervous system
tells you heart how fast to beat, checks your blood’s sugar and oxygen levels, and controls your pupils so the right amount of light reaches your eyes as you read these words, monitors your temperature and adjusts your blood flow and skin functions to keep it at 98.6ºF
coordinates - an increase in heart rate without a corresponding adjustment to breathing and blood pressure would be disastrous
is autonomic - you can make a mad dash for the train without having to calculate how much faster to breathe and pump your heart, or if you’ll need that little dose of adrenaline to make it through the doors before they close
can these strategies inspire solutions?
of course, there is a cost
lack of controllability, precision, guarantees, comprehensibility, …
A.I. ? – duplication of human thought is not the ultimate goal
UPP, September 15-17, 2004

8. Autonomic Computing – A Holistic View
Distributed (and parallel) computing has evolved and matured to provide specialized solutions to satisfy very stringent requirements in isolation
security, dependability, reliability, availability, performance, throughput, efficiency, pervasive/amorphous, automation, reasoning, etc.
However, in pervasive Grid environments requirements and objectives are dynamic and not know a priori
requirements, objectives and choice of specific solutions (algorithms, behaviors, interactions, etc.) depend on runtime state, context, and content
The goal of autonomic computing is to use appropriate solutions based on current state/context/content based on specified policies
UPP, September 15-17, 2004

9. Programming Distributed Systems
A distributed system is a collections of logically or physically disjoint entities which have established a processing for making collective decisions.
if (Decision(CurrentState,Request)) then
TransitionState(CurrentState,Request)
Central/Distributed Decision & Transition
Programming System
programming languages/abstraction – syntax + semantics
abstract machine, execution context and assumptions
infrastructure, middleware and runtime
Conceptual and Implementation Models
UPP, September 15-17, 2004

10. Programming Pervasive Grid Systems
Programming systems must be able to specify applications which can detect and dynamically respond during execution to changes in both, the execution environment and application states.
Grid applications should be composed from discrete, self-managing components which incorporate separate specifications for all of functional, non-functional and interaction-coordination behaviors.
The specifications of computational (functional) behaviors, interaction and coordination behaviors and non-functional behaviors (e.g. performance, fault detection and recovery, etc.) should be separated so that their combinations are composable.
The interface definitions of these components should be separated from their implementations to enable heterogeneous components to interact and to enable dynamic selection of components.
UPP, September 15-17, 2004

11. Autonomic Computing Architecture
Autonomic elements (components/services)
Responsible for policy-driven self-management of individual components
Relationships among autonomic elements
Based on agreements established/maintained by autonomic elements
Governed by policies
Give rise to resiliency, robustness, self-management of system
Mention self-management = self-* at both element and system level.
Relationships are the architecture’s way of achieving this for system as a whole; No architectural single point of failure
UPP, September 15-17, 2004
Autonomic Computing Tutorial, ICAC 2004

12. Autonomic Elements: Structure
Ack. J. Kephart
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
UPP, September 15-17, 2004

13. 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. J. Kephart
UPP, September 15-17, 2004

14. Autonomic Elements: Composition of Autonomic Systems
Ack. J. Kephart
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.
UPP, September 15-17, 2004
Autonomic Computing Tutorial, ICAC 2004

15. Some Research Issues and Challenges
Unprecedented Scale, Complexity, Dynamism, Heterogeneity, Unreliability, Uncertainty
Defining autonomic elements
Programming paradigms and development models/frameworks
Autonomic elements definition and construction
Policies/constraints definition, representation, enforcement
Constructing autonomic systems/applications
Composition, coordination, interactions models and infrastructures
Dynamic (policy/rule-based) configuration, execution and optimization
Dynamic (opportunistic) interactions, coordination, negotiation
Execution and management
Runtime, middleware, infrastructure
Discovery, interaction/coordination, communication, security, management, …
Security, protection, fault tolerance, reliability, availability, …
More Information
UPP, September 15-17, 2004

16. UPP 2004 – Autonomic Computing
Objective: Investigate conceptual and implementation models for Autonomic Computing
Models, Architectures and Infrastructures for Autonomic Computing
Manish Parashar et al.
Grassroots Approach to Self-Management in Large-Scale Distributed Systems
Ozalp Babaoglu et al.
Autonomic Runtime System for Large Scale Applications
Salim Hariri et al.
UPP, September 15-17, 2004