isa5428: 普及計算 autonomic computing 金仲達教授清華大學資訊系統與應用研究所...

ISA5428: 普及計算

Autonomic Computing

金仲達教授清華大學資訊系統與應用研究所

九十一學年度第二學期

(Slides are taken from the presentations byAlan Ganek, Alfred Spector, Jeff Kephart of IBM)

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Trillions of heterogeneous computing devices connected to the Internet

Dream of Pervasive Computing …

or Nightmare!

3

Core of the Problem

• Complexityin systems themselves and in the operating environment– As systems become more interconnected

and diverse, architects are less able to anticipate and design interactions among componentspush to runtime, late bindinge.g., hot-plug, JVM, JIT compilation, service discovery, mobile agents, …

• Complexity managementhuman intervention and IT costs

4

Need Complexity Management

• But complexity is beyond that human can handleHuman out of the control loop autonomic

• Even though we are moving along this direction, is there any systematic way of addressing this issue?

• Autonomic Computing

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Alan G. GanekVice PresidentIBM Autonomic Computing

http://www.ibm.com/autonomic/

Autonomic Computing

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Directory Directory and Security and Security

ServicesServicesExistingExisting

ApplicationsApplicationsand Dataand Data

BusinessBusinessDataData

DataDataServerServer

WebWebApplicationApplication

ServerServer

Storage AreaStorage AreaNetworkNetwork

BPs andBPs andExternalExternalServicesServices

WebWebServerServer

DNSDNSServerServer

DataData

Dozens of systems and applications

Hundreds of components

Thousands of tuning

parameters

Complex Heterogeneous Infrastructures Are a Reality!

7

Industry Trends

• Administration of systems is increasingly difficult– 100s of configuration, tuning parameters for DB2

• Heterogeneous systems are increasingly connected– Integration becoming ever more difficult

• Architects can't plan interactions among components– Increasingly dynamic; frequently with unanticipated

components• More burden must be assumed at run time

– But human administrators can't assume the burden• 6:1 cost ratio between storage admin and storage• 40% outages due to operator error

• Need self-managing computing systems– Behavior specified by sys admins via high-level

policies– System and its components figure out how to carry

out policies

8

Autonomic Computing Vision

• “ Intelligent” open systems that…– Manage complexity– “Know” themselves– Continuously tune themselves– Adapt to unpredictable conditions– Prevent and recover from failures– Provide a safe environment

• Self-management:– free administrators from details of operations– provide peak performance 24/7– Concentrate on high-level decisions and

policies

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Increase Responsiveness

Adapt to dynamically changing environments

Business Resiliency

Discover, diagnose, and act to prevent disruptions

Operational Efficiency

Tune resources and balance workloads to maximize use of IT resources

Secure Information and Resources

Anticipate, detect, identify, and protect against attacks

Self-managing Systems That …

Aware/Proactive

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Self-Configuring Example:DB2 Configuration Advisor

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Self-Healing Example: IBM Electronic Service Agent

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InternetInternet

Appliance Appliance ServersServers

Web Web Application Application

ServersServersData and Data and

Transaction Transaction ServersServers

Internet/Internet/ExtranetExtranet

Business Business PartnersPartners

Self-tuning, end-to-end performance Self-tuning, end-to-end performance managementmanagement

Dynamic allocation of network resourcesDynamic allocation of network resources Workload balancing & routingWorkload balancing & routing Cross platform reportingCross platform reporting Policy-based for various classes of users & applicationsPolicy-based for various classes of users & applications

Heterogeneous, distributed components working together

Self Optimizing:Enterprise Workload Management

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Automate incident responseProtect systems and dataHelp prevent service disruptions

Risk MgrIDS Rules

Event Database

CorrelationEngine

Intrusion Detection System (IDS)

RouterWebServer

Firewall

ApplicationServer Intrusion

Detection

InternetIntranet

RiskManagerSecurity Event

ApplicationServer

"The Tivoli security management software portfolio is helping our clients extend their businesses to the

Internet while providing security and privacy..."Mark Ford, Principal

Deloitte & Touche

Rapid / automated analysisof complex situations

Self-Protecting Example: IBM Tivoli Risk Manager

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Evolving towards Self-management

Today The Autonomic Future

Self-configure

Corporate data centers are multi-vendor, multi-platform. Installing, configuring, integrating systems is time-consuming, error-prone.

Automated configuration of components, systems according to high-level policies; rest of system adjusts automatically. Seamless, like adding new cell to body or new individual to population.

Self-heal Problem determination in large, complex systems can take a team of programmers weeks

Automated detection, diagnosis, and repair of localized software/hardware problems.

Self-optimize WebSphere, DB2 have hundreds of nonlinear tuning parameters; many new ones with each release.

Components and systems will continually seek opportunities to improve their own performance and efficiency.

Self-protect Manual detection and recovery from attacks and cascading failures.

Automated defense against malicious attacks or cascading failures; use early warning to anticipate and prevent system-wide failures.

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Manual Autonomic

Ben

efit

sSk

ills

Ch

arac

teri

stic

s

ManagedLevel 2

PredictiveLevel 3

AdaptiveLevel 4

AutonomicLevel 5

BasicLevel 1

Multiple sources of

system generated data

Requires extensive,

highly skilled IT staff

Basic Requirements

Met

Evolving to Autonomic Computing

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Manual Autonomic

Ben

efit

sSk

ills

Ch

arac

teri

stic

s

BasicLevel 1

PredictiveLevel 3

AdaptiveLevel 4

AutonomicLevel 5

Multiple sources of


Requires extensive,


Basic Requirements

Met

ManagedLevel 2

Consolidationof data and

actions through

managementtools

IT staffanalyzes andtakes actions

Greater system

awarenessImproved

productivity


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Manual Autonomic

Ben

efit

sSk

ills

Ch

arac

teri

stic

s

BasicLevel 1

ManagedLevel 2

AdaptiveLevel 4

AutonomicLevel 5

Multiple sources of


Requires extensive,


Basic Requirements

Met


actions through

managementtools


Greater system

awarenessImproved

productivity

PredictiveLevel 3

Systemmonitors,

correlates and recommends

actions

IT staffapproves and

initiates actions

Reduced dependency on

deep skillsFaster/better

decision making


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Manual Autonomic

Ben

efit

sSk

ills

Ch

arac

teri

stic

s

BasicLevel 1

ManagedLevel 2

PredictiveLevel 3

AutonomicLevel 5


Multiple sources of


Requires extensive,


Basic Requirements

Met


actions through

managementtools


Greater system

awarenessImproved

productivity

Systemmonitors,


actions


initiates actions



decision making

AdaptiveLevel 4

System monitors,

correlates and takes action

IT staff manages

performance against SLAs

Balanced human/system

interactionIT agility and

resiliency

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Manual Autonomic

Ben

efit

sSk

ills

Ch

arac

teri

stic

s

BasicLevel 1

ManagedLevel 2

PredictiveLevel 3

AdaptiveLevel 4

Multiple sources of


Requires extensive,


Basic Requirements

Met


actions through

managementtools


Greater system

awarenessImproved

productivity

Systemmonitors,


actions


initiates actions



decision making

System monitors,

correlates and takes action

IT staff manages

performance against SLAs

Balanced human/system

interactionIT agility and

resiliency

AutonomicLevel 5

Integrated components dynamically managed by

business rules/policies

IT staff focuseson enabling

business needs

Business policy drives IT

managementBusiness agility and resiliency


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IBM’s Architecture Model

• Intelligent control loop:– Implementing self-managing attributes

involves an intelligent control loop

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Control Loops Delivered in 2 Ways

Combinations of Management

Tools

Recourse Provider

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3 Layers of Control Loop Management

• Composite resources tied to business decision-making

• Composite resources decision-making, e.g., cluster servers

• Resource elements managing themselves

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Autonomic Element - Structure

• Fundamental atom of the architecture– Managed element(s)

• Database, storage– Autonomic manager

• Responsible for:– Providing its service– Managing own

behavior inaccordance withpolicies

– Interacting with other autonomic elementsAn Autonomic Element

Monitor

Analyze

Sensors

Execute

Plan

Effectors

Knowledge

Au

ton

omic

Man

ager

Man

aged

Ele

men

t

Sensors Effectors

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Alerts, events & problem analysis request interface

SLA/Policy interface, interprets & translates into "control logic"

PlanPolicy TransformsPlan Generators

Policy Interpreter

Analyze

Execute

Service Dispatcher

Distribution Engine

Scheduler Engine

Workflow Engine

Monitor

Metric Managers

Filters

Simple CorrelatorsKnowledge

Policy

CalendarTopology

Recent Activity Log

Sensors Effectors

Rules Engines

Analysis Engines

Policy Validations

Policy Resolution

Autonomic Manager Substructure

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Autonomic Elements - Interaction

• Relationships– Dynamic, ephemeral– Formed by agreement

• May be negotiated– Full spectrum

• Peer-to-peer• Hierarchical

– Subject to policies

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Multiple Contexts for Autonomic Behavior

System Elements

(Intra-elementself-

management)

Groups of Elements

(Inter-elementself-

management)

Business Solutions

(Business Policies,

Processes, Contracts)

ServerFarm

EnterpriseNetwork

StoragePool

Customer Relationship Management

Enterprise

ResourcePlanning

Servers StorageNetworkDevicesMiddleware

DatabaseApplications

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Mapping to IT Processes

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Levels of Maturity

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Enabled capabilities

Core technologies

Administrative Console

Policy Infrastructure

Data Collection (Logging/Tracing)

Infrastructure Provisioning

Install/Dependency Management

Heterogeneous Workload Management

Solution Management

Policy-based Management

End-to-end Problem Determination

Automated Root Cause AnalysisAuto-Update

Identity/Security Management

Auto-Detection

Dynamic Provisioning

Autonomic Computing Requires Core Technologies

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Integrated Solutions Console for Common System Administration

• Value:– One consistent interface across

product portfolio– Common runtime infrastructure

and development tools basedon industry standards, component reuse

– Provides a presentation framework for other autonomic core technologies

...n

Customer pain point:Complexity of operations

Standards-based: J2EE, JSR168

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Log and Trace Tool for Problem Determination

• Value:– Introduces standard

interfaces and formats for logging and tracing

– Central point of interaction with multiple data sources

– Correlated views of data– Reduced time spent in

problem analysis

Analysis Engine

Data Exploiters

Data Producers

ISC

StandardInterface

LoggingAgent

Common situations and data model

BLog

Embedded adapter

....

Data Store

LoggingAgent


eServer

Log

Embedded adapter

LoggingAgent


ALog

Embedded adapter

Collector Collector....

Parser

Parser

Parser

Viewer....

Customer pain point:Difficulty in analyzing problems in multi-component systems

Standards-based:JSR47, Apache

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Install/Config Package for Solution Install

• Value:– One consistent software installation

technology across all products– Consistent and up-to-date configuration

and dependency data, key to buildingself-configuring autonomic systems

– Reduced deployment time with less errors– Reduced software maintenance time,

improved analysis of failed system components

– Component-based install for IBM and non-IBM products

Install package developer

Meta-DataNameUUIDVendorVersion

Configuration PropertiesInstall InputRuntime Attributes

DependenciesHW, SW, OS, ConfigurationExtensions

Install ActionsExtensions

Verification ActionsExtensions

Configuration ActionsExtensions

Package Structure

Product Files (binaries, etc.)

Product Files (binaries, etc.)

Deployment Descriptor

Deployment Descriptor

Verification Actions

Verification Actions

DependencyCheckers

DependencyCheckers

Custom Extensions

InstallActions

InstallActions

Configuration Actions

Configuration Actions

GUI Interface

GUI Interface

Customer pain point:Difficulty of deployment in complex systems

Standards-based:OGSA, Web Services

Partnering with InstallShield

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Policy Tools for Policy-based Management

•Value:–Uniform cross-product policy definition and management infrastructure, needed for delivering system-wide self-management capabilities

–Simplifies management of multiple products; reduced TCO

–Easier to dynamically change configuration in on-demand environment

Customer pain point:Complexity of product and systems management

Standards-based:DMTF, OASIS, OGSA

Adaptation

Definition

ValidationLocal

Repository

Distribution

Enforcement

Point

Push or pull

Push or pull

Activate

Implement

MON ITOR

Facts

Analysis

Resource

…

…

Enforcement

Point

Resource Resource

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Technologies for Implementing Autonomic Managers

Value:• Components to simplify the incorporation of

autonomic functions into applications – Building blocks for self-management– Monitoring, analysis, planning and execution

components – Including autonomic computing technologies,

grid tools, and services• Pluggable

– Defines interfaces and provides implementations for each major toolkit component

Customer pain point: How to implement end-to-end autonomic solutions

Standards-based:OGSA, W3C

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Summary of Autonomic Computing Architecture

• Based on a distributed, service-oriented architectural approach, e.g., OGSA– Every component provides or consumes services– Policy-based management

• Autonomic elements– Make every component resilient, robust, self-

managing– Behavior is specified and driven by policies

• Relationships between autonomic elements – Based on agreements established and maintained

by autonomic elements– Governed by policies– Give rise to resiliency, robustness, self-

management of system

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Summary

isa5428: 普及計算 autonomic computing 金仲達教授 清華大學資訊系統與應用研究所...

Documents

isa5428: 普及計算 autonomic computing 金仲達教授清華大學資訊系統與應用研究所...