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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 http://automate.rutgers.edu http://autonomic-conference.org parashar@caip.rutgers.edu UPP, September 15-17, 2004

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