Autonomic Computing: Standards for Self-Managing Systems

Transcription

1 Autonomic Computing: Standards for Self-Managing Systems Alan Ganek Vice President IBM Autonomic Computing ibm.com/autonomic 1

2 x On Demand Era Responsive in real-time Variable cost structures Focused on what s core and differentiating Resilient around the world, around the clock Integrated Open Virtual a 2

3 Complex heterogeneous infrastructures are a reality! Dozens of systems and applications Directory and Security Services Existing Applications and Data Business Data DNS Server Web Server Data Web Server Application Server Thousands of tuning parameters Data Hundreds of components BPs and External Services Storage Area Network 3

4 CIO blues Complexity in running and managing the IT infrastructure The inability to manage the infrastructure seamlessly Too much time and money is spent running existing infrastructure Difficulty in deployment of complex systems Most of my IT budget is spent on what I already have, leaving little for new projects Swamped by the proliferation of technology and platforms to support Customers need the ability to configure systems automatically based upon demand, with 24x7 availability of mission critical applications. They need assurance that their employees, customers and partners have secure access to the right systems, which are continuously optimized for productivity. 4

5 What Is The 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 Frees your business to focus on business, not infrastructure 5

6 Autonomic Computing Attributes Self-Managing Systems that Deliver Increased 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 6

7 Levels of Automation Autonomic Basic Manual analysis and problem solving Managed Centralized tools, manual actions Predictive Cross-resource correlation and guidance Adaptive System monitors, correlates and takes action Dynamic business policy based management Level 1 Level 2 Level 3 Level 4 Level 5 Evolution Not Revolution 7

8 How Does Autonomic Computing Help Customers? Increased return on IT investment (ROI) Lower administrative costs Improved utilization of assets Free up IT skills to address business goals Improved resiliency: Quality of Service (QoS) Reduced downtime Improved security Increased performance Accelerated implementation of new capabilities: Time to Value (TTV) Faster and more accurate installation of new capabilities Reduced test cycles 8

9 Autonomic Computing Delivering Autonomic Capability on demand operating environment Business Policy 9 Autonomic Core Capabilities Autonomic Autonomic Product Product Features Features and and Enablers Enablers Software Resources System Resources

10 A Holistic Approach to Autonomic Computing Common Autonomic construct for all system elements Distributed components and systems integrated as one virtual operating system Web Services Interfaces to elements System Mgmt Database Industry standards are key to the success of Autonomic Computing Customer Applications ISV Solutions OGSA Meta OS Services Web Services Application Servers Servers Storage Network Architecture Framework Architecture Framework 10

11 Core Building Blocks for an open architecture Analyze Plan Monitor Knowledge Execute Sensors Effectors Element An autonomic element contains a continuous control loop that monitors activities and takes actions to adjust the system to meet business objectives Autonomic elements learn from past experience to build action plans Managed elements need to be instrumented consistently 11

12 Multiple Contexts for Autonomic Behavior Customer Relationship Management Enterprise Resource Planning Business Solutions (Business Policies, Processes, Contracts) Server Farm Enterprise Network Storage Pool Groups of Elements (Inter-element self-management) Servers Storage Network Devices Middleware Database Applications System Elements (Intra-element self-management) 12

13 Autonomic Computing Core Capabilities For Enabling Autonomic Computing on demand operating environment Business Policy 13 Autonomic Core Capabilities Common Systems Administration Service Delivery Service Support Policy based mgmt/security Solution install/maintain Autonomic monitoring Problem determination Complex analysis Heterog. Workload Mgmt Evolving Interfaces Provisioning Autonomic Autonomic Product Product Features Features and and Enablers Enablers

14 Open Standards for Self-Managing Systems Why Standards? Autonomic computing is an industry-wide initiative Proprietary solutions with vendor lock-in are unacceptable to customers Open, level playing field where vendors compete with best solutions Standards-based components can interoperate Easier to integrate multivendor components into an end-to-end solution Game Plan Industry standards are key to the success of Autonomic Computing Leverage existing standards when feasible Drive new standards through open standards bodies when necessary Coordinate disparate standards efforts when required 14

15 Install/Config Package for Solution Install Customer pain point: Difficulty of deployment in complex systems Value: One consistent software installation technology across all products Consistent and up-to-date configuration and dependency data, Reduced deployment time with less errors Reduced software maintenance time, improved analysis of failed system components Component-based product install GUI GUI Interface Interface Install package developer Deployment Deployment Descriptor Descriptor Product Product Files Files (binaries, (binaries, etc.) etc.) Custom Extensions Dependency Dependency Checkers Checkers Install Install Actions Actions Verification Verification Actions Actions Configuration Configuration Actions Actions Meta-Data Name UUID Vendor Version Configuration Properties Install Input Runtime Attributes Dependencies HW, SW, OS, Configuration Extensions Install Actions Extensions Verification Actions Extensions Configuration Actions Extensions Package Structure OGSA, Web Services,CIM 15

16 Integrated Solutions Console for Common System Administration Customer pain point: Complexity of operations Value: One consistent user interface across product portfolio Common runtime infrastructure and development tools based on industry standards, component reuse Provides a presentation framework for other autonomic core technologies Through a unified portal... n Actions Views J2EE, JSR168 Controls Common Lookand-Feel Graphics Tabs HELP 16

17 Log and Trace Tool for Problem Determination Customer pain point: Difficulty in analyzing problems in multi-component systems Value: Reduced time spent in problem analysis Central point of interaction with multiple data sources Introduces standard interfaces and formats for logging and tracing Correlated views of data Standard Interface ISC Collector Logging Agent Common situations and data model... Parser... Logging Agent Viewer Analysis Engine Common situations and data model Embedded adapter B... Collector Parser Log Logging Agent Data Exploiters Data Store Data Producers Common situations and data model Parser Embedded adapter eserver Log Embedded adapter A Log JSR47, Apache 17

18 Tivoli Autonomic Monitoring Engine Customer pain point: Difficult to determine problem s root cause required to take corrective action Value: Root cause analysis for IT failures - not just surfacing symptoms Server level correlation of multiple IT systems Applies intelligent, automated corrective action Resource Model Builder Res. Model Res. Model Res. Model Problem Analyzer JMX Operating System Resource Models Resource Model Engine SNMP Problem Aggregator Problem Correlator Provider Layer WM I Lo g P MI Autonomic Action Manager CIM Local Data Store Web Health Console CIM, SNMP, WMI, JMX 18

19 Business Workload Manager for Heterogeneous Workload Management Customer pain point: Unable to definitively determine cause of bottleneck in system Value: Response time measurement and reporting of transaction processing segments Dynamic learning of transaction workflow topology through servers and middleware Drill-down through service class reporting to identify bottleneck processes Tivoli UI Web Browser / ISC All UI platforms Tivoli Management Server BWLM integrated ARM Tivoli Agent - Detailed Reporting AIX OS/400 z/os Windows Linux/Intel Solaris Linux/z Coarse-Grained Management Windows Linux/Intel Solaris Linux/z 19

20 ABLE Rules Engine for Complex Analysis Customer pain point: Complex algorithms required to implement intelligent autonomic behavior Value: 20 Fast, reusable and scalable set of learning and reasoning components Intelligent agents for capturing and sharing individual and organizational knowledge Learns from experience and predicts future states Correlates events and applies policy to take action FIPA, JSR87

21 Policy Tools for Policy-based Management Customer pain point: Complexity of product and systems management DMTF, OASIS, OGSA Value: Uniform cross-product policy definition and management infrastructure, needed for delivering system-wide selfmanagement capabilities Simplifies management of multiple products; reduced TCO Easier to dynamically change configuration in on-demand environment M O N I T O R Activate Implement Analysis Facts Enforcement Point Resource Definition Local Repository Push or pull Distribution Push or pull Adaptation Resource Validation Enforcement Point Resource 21

22 Autonomic Computing Standards Core Capabilities Solution Install Common System Administration Problem Determination Autonomic Monitoring Heterogeneous Workload Management Complex Analysis Policy-Based Management Arenas GGF, OASIS, Open Group, IETF, W3C (Web Services, ) J2EE, JCP (JSRs) SNIA (Storage networks) DMTF (Systems management) OGSA, Web Services CIM, SNMP, WMI, JMX FIPA, JSR87 J2EE, JSR168 JSR47, Apache ARM DMTF, OASIS, OGSA 22

23 DMTF s Role in Autonomic Computing Standards Autonomic Elements, Monitoring and Data Collection Use of CIM Emerging data collection format standards Emerging autonomic element definition standards Solution Install Work with DMTF to define the installation standard Policy-Based Management Addressed today in multiple standards bodies Each in their own domain and framework No coordination or synergy Work with DMTF and other standards bodies to coordinate policy standards to support self-managing systems General Establish liaisons with other relevant standards organizations Especially GGF, OASIS 23

24 Autonomic Computing alphaworks Zone Get started developing autonomic solutions now Available on alphaworks: Log and Trace Tool Business Workload Management Demo Tivoli Resource Model Builder Agent Building and Learning Environment (ABLE) IBM Grid Toolbox Web Services Tools Coming in 2H03 components from: Autonomic Computing Toolkit Solution Install Policy-based Management and more! 24

25 The journey has started Products, services available today Architecture and core technologies emerging Standards are key to autonomic computing industry success IBM is working with business partners and standards organizations to develop open standards for self-managing systems Broad IT industry participation is required this is an industry-wide initiative DMTF will be key to the autonomic computing standards effort Freeing customers to focus on their business instead of their infrastructure 25

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