Autonomic Computing

Content Motivation Why Autonomic Computing Autonomic Computing Paradigm Properties Autonomic Computing Today General architecture of Autonomic Computing Challenges 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 parameters Operating behaviors become complex and unanticipated, large task for management New 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 •

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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-Configuration Adapt automatically to the dynamically changing environment • Internal adaptation – Add/remove new components (software) – configures itself on the fly • External adaptation Systems configure themselves into 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-optimization Monitor and tune resources automatically – Support operating in unpredictable environment – Efficiently maximization of resource utilization without human intervention • Dynamic resource allocation and workload management. – Resource: Storage, databases, networks – For example, Dynamic server clustering

Self-protection Anticipate, detect, identify and protect against attacks from anywhere – Defining and managing user access to all computing resources – Protecting against unauthorized resource Access, e.g. SSL – Detecting intrusions and reporting as they occur

Autonomic Computing Today The 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 selfconfigure to access a TCP/IP network. PCs on a LAN can discover other devices, such as printers, and

General Architecture of Autonomic Computing

An Autonomic Element manages itself and

delivers service Interaction between different Autonomic Elements using Policies

Autonomic Elements Consist of one or more managed elements coupled

with a single autonomic manager

Management using MAPE: – Monitoring managed elements and their external environment – Analyzing the gathered information – Planning and executing based on information

A Managed Element can be: Hardware resource, CPU,Printer, Database, Application service,etc

PMAC – An example of Autonomic Computing Policy 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, and provides decisions to the managed element in order to manage its behavior • Using Autonomic Computing Policy Language(ACPL) as common policy 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 threats specific types of threats

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