ContentMotivationWhy Autonomic ComputingAutonomic Computing ParadigmProperties Autonomic Computing TodayGeneral architecture of Autonomic ComputingChallenges and Conclusion

Motivation• Advanced computing development– Good news: benefits in all areas (research, business)– Bad news: difficult to configure/operate, manage

Large number of nodes and parametersOperating behaviors become complex and

unanticipated, large task for managementNew challenges of computing systems

– Scalability (million nodes)– Heterogeneity (various operating systems)– Dynamics (ad-hoc connection, add/remove entities arbitrary)– Reliability ( reliable components/operating systems)

Why Autonomic Computing?The main reason for large blue-chip

companies, like IBM, being interested in autonomic computing is the need to reduce the cost and complexity of owning and operating an IT infrastructure .

In particular, there is a need to alleviate the complexity with which system administrators of IT services are faced today.

The aim is to allow administrators to specify high-level policies that define the goals of the autonomic system, and let the system manage itself to accomplish these goals.

Contd… At present, system administrators must

tweak hundreds of settings and often spend weeks before getting a system to run optimally.

Autonomic systems are also faster at adapting to changes to the environment, e.g. by distributing its resources differently when a critical-project requires more CPU processing power.

Autonomic Computing Paradigm

• To design and build computing systems capable of running themselves, adjusting to varying circumstances, and preparing their resources to handle most efficiently the workloads we put upon them.

• Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that are reflective and self-adaptive.

• Autonomic computing is generally considered to be a term first used by IBM in 2001 to describe computing systems that are said to be self-managing

Properties of Autonomic Computing

Self-ConfigurationAdapt automatically to thedynamically changingenvironment• Internal adaptation– Add/remove new components(software)– configures itself on the fly• External adaptationSystems configure themselvesinto a global infrastructure

Self-healing• Discover, diagnose and

react to disruptions without disrupting the service environment

• Fault components should be

– detected– Isolated– Fixed– reintegrated

Self-optimizationMonitor and tune resourcesautomatically– Support operating inunpredictable environment– Efficiently maximization ofresource utilization withouthuman intervention• Dynamic resource allocationand workload management.– Resource: Storage, databases,networks– For example, Dynamic serverclustering

Self-protectionAnticipate, detect, identifyand protect againstattacks from anywhere– Defining and managing

useraccess to all computingresources– Protecting againstunauthorized resourceAccess, e.g. SSL– Detecting intrusions andreporting as they occur

Autonomic Computing TodayThe ideas behind autonomic computing are not

new. In fact, it is possible to find some aspects of autonomic computing already in today’s software products .

Windows XP optimises its user interface (UI) by creating a list of most often used programs in the start menu. Thus, it is self-configuring in that it adapts the UI to the behaviour of the user

It can also download and install new critical updates without user intervention, sometimes without restarting the system. Therefore, it also exhibits basic self-healing properties.

DHCP and DNS services allow devices to self-configure to access a TCP/IP network. PCs on a LAN can discover other devices, such as printers, and install their drivers automatic

General Architecture of Autonomic Computing

An Autonomic Element manages itself and delivers service

Interaction between different Autonomic Elements using Policies

Autonomic ElementsConsist of one or more managed elements coupled

with a singleautonomic manager

Management using MAPE:– Monitoring managedelements and theirexternal environment– Analyzing the gatheredinformation– Planning and executingbased on informationA Managed Element can be:

Hardware resource, CPU,Printer, Database, Application service,etc

PMAC – An example of Autonomic ComputingPolicy Management for Autonomic Computing (PMAC)– An autonomic core technology published in 2005– Available under http://www.alphaworks.ibm.com/tech/pmac• Purpose: Providing a Policy management infrastructure– Automating what administrators do today• Administrators follow written policies• With autonomic, autonomic managers follow machine-readable policy• Autonomic Manager – Selects policies, evaluates policies, andprovides decisions to the managed element in order to manage itsbehavior• Using Autonomic Computing Policy Language(ACPL) as commonpolicy language– ACPL contains 4 tuples: Scope, Condition, Business value, Decision• Scope represents managed elements, Business value is the decision

priority• Decision can be Actions, Configuration Profiles and Results

PMAC - Architecture

PMAC – Example• Consider the goal policy– Scope: Company A’s on-line ordering system– Condition: During business hours– Business value: 100– Decision: 2-second average response time• In this case the Managed element is an on-line ordering system• Autonomic Manager makes the decision by– Monitoring data coming from the online ordering system– Analyzing the gathered data using conditions (business

hours?)– Planing and executing based on the previous analyses• Calculate the average response time and• If it is far from 2 seconds then adding servers in order to provide

functionality

Challenges of Autonomic Computing• Autonomic System challenges– Self-configuration in large-scale application

– Problem localization and automated remediation

– Decision making of coordination of optimizing process

– Self-protecting against active threatsspecific types of threats

Conclusion• Solution of today’s increasing complexity in computingscienceSelf-Management and dynamic adaptive behaviors• Still challenges in diverse fields of science andtechnology– Autonomic behavior in one field of scienceSystem managements, software engineering, etc.– Needs for a abstraction and co-operation in relevant fields