Technology Trends and Research Directions 1 March 2004

1. Technology Trends and Research Directions 1 March 2004 Stuart Feldman VP, Internet Technology

2. Outline • Fundamental Trends – Hardware, Systems, Software • Major Research System Directions • On Demand Technologies – Grid, Autonomic, etc. • Services and Service Models • Business Models and Processes • Business Processes – Analysis, Management, Optimization • Tera-Trends – Long Term Technology-Driven Futures • Web Fountain • Privacy

3. Some Fundamental Trends • Hardware continues exponentially improving • Moore’s law, disk densities, network bandwidth through 2010 • Computational shift:procedures, objects to Services • Technology push, affordability and base technology enables • Economic shift: agriculture, resource extraction, manufacturing to Services • Sufficiency and need transitions • Role reversal • People used computers to do their work better • Now computers use people to do their work

4. Fundamental Hardware Trends The good news continues

5. US $1000 buys …

9. But Moore’s Law is in Trouble … • OK for next few years • But we are near end of classic scaling • Some layers <10 atoms thick, heading to 5. • Power dissipation, active/passive • Device-dependent scale failures • So, back to architecture!

10. Systems and Software Trends Higher level of abstraction, integration

11. Major Drivers • Higher levels of abstraction and virtualization • Concerns about security, availability, reliability • Needs to adapt • More powerful and capacious computers permit general solutions • More numerous small devices and power limits demand specific solutions • Some relevant directions: • Web Services • Grid Services • Autonomic Computing • Sensor networks

12. 2003 = Year 0 of New Pardigm

13. Professional Services Autonomic Capabilities System Management Services Grid Services Open Grid Services Architecture (OGSA) OGSI – Open Grid Services Infrastructure Web Services OGSA Enabled OGSA Enabled OGSA Enabled Storage Servers Network OGSA Enabled OGSA Enabled OGSA Enabled OGSA Enabled OGSA Enabled OGSA Enabled File Systems Workflow Database Directory Messaging Security Technical Desiderata of the On Demand Operating Environment • On Demand Computing = Internet + Open Standards + Autonomic Computing + Grid + Web Services + Utility Computing + Security + Privacy + Service-Oriented Architectures + Business Process Integration + Business Transformation Outsourcing + Analysis & Modeling + …

14. Business Value Improve Operating Efficiency/ROI Reduce Capital Expenses Accelerate Business Processes Enhance Employee Productivity Quickly Adapt to Changing Requirements IT Value Improve Asset Optimization Integrate Heterogeneous Resources Enable Data Access, Integration and Collaboration Strengthen Redundancy and Resiliency Quickly Respond to Variable Demands Grid Computing Enables IT and Business Value

15. Grid Adoption Steps – Roadmap to Value Transaction Management: • Manage the execution of e-business transactions across distributed resources • Enable dynamic allocation of resources for transactional and parallel application models Billing and Metering: • Enable applications to be set up in a usage-based charging model • Track usage and bill/chargeback users based on cost models Transaction Mgmt Automated Provisioning: • Identify and allocate resources to meet quality of service goals for applications • Configure and initiate these resources as required Task Scheduling: • Manage the execution of parallel, short running tasks across distributed resources • Provide a programming model to enable applications to leverage this capability Grid Value and Capability Workload Management: • Monitor and manage resources to help applications achieve quality of service goals • Manage the prioritization and resource selection for tasks and jobs Data Virtualization: • Enable data federation, location, replication, caching, and access • Data Grids work on block level data, files, or information in databases Base Grid: • Machines/Clusters to run workload • Middleware and agents to make machines/clusters accessible and manageable • Management functions to distribute and manage tasks and machines/clusters

16. Rollout Sequence • Research community (physics, astronomy, biosciences, etc.) • Technical community (engineering, etc.) within companies • Government (infrastructure and first uses) • Large enterprises for internal operations • Inter-company virtual organizations (including SMB participants in value nets)

17. IBM System Research Directions

18. Blue Gene • Aim for the PetaFLOP • And getting there • Highly extensible, very dense • With excellent power efficiency

19. PERCS • Part of the DARPA 2010 effort • How to build systems that are extensible, economically effective • programmable with decreased cost of ownership and especially time to solution

20. Other

21. On Demand Technologies: Grid, Autonomic, etc. etc. The next step

22. “On Demand” An enterprise whose business processes – integratedend-to-end across the company and with key partners, suppliers and customers – can respond with speed to any customer demand, market opportunity or external threat.

23. On-Demand Attributes

24. Financial & Utility Offerings BusinessTransformation OperatingEnvironment The Three Market Plays

25. Integration of People – Process – Information Anywhere, any time, from any device Transactional Processes Information Management Collaboration Application Development, Deployment & Maintenance Business Objectives and Policies Open Standards-based Policy-based Orchestration Provisioning Availability Security Optimization Virtualization Engine Distributed Systems Network Servers Storage On Demand Operating Environment

26. Web Services OGSA Enabled OGSA Enabled OGSA Enabled Network Storage Servers OGSA Enabled OGSA Enabled OGSA Enabled OGSA Enabled OGSA Enabled OGSA Enabled Security Directory Workflow File Systems Database Messaging Architecture Framework Applications Professional Services Autonomic Capabilities System Management Services Grid Services Open Grid Services Architecture (OGSA) OGSI – Open Grid Services Infrastructure

27. e-business on demand Business Transformation Framework Where to Focus What to Change Customers & Channels Governance Model People / Culture Supply Chain Support Services On Demand Business Model Process Innovation & Prod Dev People, Org & Support Application / Infrastructure Technology Optimization

28. Service-Oriented Architectures • Building block is the independent service • Well defined execution interfaces (what it does, what it works on) • Syntax and semantics/behavior • Well defined management interfaces (initiate, control, monitor) • Recursive – services built upon other services • New families of computing standards • Web services • Grid • Fundamental to distributed computing • Enables physical and logical alternatives

29. Service-Oriented Economy • Service involves producing a result, not a physical object • Although massive motions may be part of a service • Major trend in worldwide economy for 50+ years • Agricultural revolution • Industrial revolution • Information revolution • US economy has long been >50% service based • And trend continues • Implications for economy, education, etc.

30. HIGH END COMPUTING

31. Supercomputing • Overwhelming computing leads to shifts in capabilities and expectations • supercomputers for knowledge creation, awareness, connectivity - and intelligence • ongoing since the 70s, just redefining "super"

32. Next+1 Gen HPC • Hypothesis – current types of high end computing expand • Current workload models and system architectures will evolve • Traditional customer base (government, industry) will grow • New opportunities opening in medicine, advanced business • New customer sets possible as price points shift downward • TF local computer for medical and research labs • Business modeling (risk, rapid adaptation, etc.) • Localized weather forecasting on demand • Personalized news and drama creation on the fly • Brand new areas are appearing, with enormous up-side potential • New workload models for new applications • Collaboration and Interactions: gaming, training, modeling • Information-driven, knowledge-rich analysis • Driven more by bandwidth and memory than by floating point • IBM is uniquely positioned in hardware experience and projects as well as involvement in aggressive apps (new patterns and loads) • Multiple revolutionary projects • Web Fountain, other shystems • Grid and service models – On Demand software stack as base

33. High End Computing – Application Drivers • Scientific Computing • ODE, PDE, optimization, number theory • Discrete simulation • Data analysis • Business-Computing • Transactions, databases • Financial engineering and analysis • Interacting business processes • Information-Intensive Computing • Data publishing • Collaboration • Information extraction, knowledge creation business scientific Info Knowledge

34. High End Computing Drives – Scientific Computing • Number theory • Fixed point, modest memory, huge number of calculations • Customers – intelligence, finance, research • Scientific Computing • Simulations of (ordinary and partial) differential equations • Optimizations • Customers – defense, aerospace, manufacturing, life sciences • Financial computing • Monte Carlo and integral equation problems • Discrete Simulations • Traffic, epidemiology, social phenomena • Disaster management and training • Network and Computer system modeling • Scientific data analysis • Astronomy, clinical science, high energy physics, etc. • Radiography (CAT, fMRI, etc.) • Genome, proteome, systems biology, etc. • Epidemiology

35. High End Computing Drives – Business Computing • Transactions • High reliability, typically small amounts of information per transaction, large amounts of back-end data being accessed and sometimes updated. • Database Management • Interacting business processes • Collaborative execution • Outsourced massive processes • Financial computing • Monte Carlo and integral equation problems

36. High End Computing Drives – Data-Intensive Computing • Data publishing • Massive web server farms (e.g., Yahoo, Google, eBay, etc.) • Video and digital media serving (significant buffer memory, large disks, wide external network capacity) • Computing load proportional to number of customers or query rate • Data creation and adaptation • Personalized data adaptation and delivery (ad insertion, alternate plots, fantasy games); computing proportional to input length and edit complexity • Extreme interaction and collaboration • Games (latency sensitivity, potentially large bandwidth) – need to meet QoS goals • Massively Multi-Player Broadband games (1M users at 1 Gb/s ?) • Collaborative work, training • Information and Knowledge-Intensive computing • Data read, examined - Enormous inputs, stream and stream-like (web crawling) • Information found via parsing and analysis, load proportional to input analyzed • WF, Distillery • Knowledge extracted and created – adding semantics, detecting patterns, noting relationships • Graph models and analyses, large lookups, pattern derivation and analysis – running times polynomial or exponential in sizes of subgraphs • Huge random memories, large interprocessor bandwidth, unbounded computing • Systems always overloaded – more inputs to be processed, results to be processed than possible

37. Predictions: Hottest New Areas 2007+ • “Low-end” possibilities (mass market for teraflops) • Cost and software sensitive • Collaboration and interactions (personal and enterprise) • Computation, bandwidth, and latency sensitive • Data analysis and distribution • Bandwidth and latency sensitive • Knowledge based computing • Memory and interconnect sensitive

38. Marketing Issues • Competition • From below • Game chips, informal clusters • From the side • Government subsidized competition • Other chip and system makers • Market definition, segmentation, making • Thinking and looking outside the box • Priming the pump with complementors, early adopters

39. Technology Issues • Understand computing patterns and workloads of new applications • Phases of computation, active/inactive patterns, memory and interconnection demands • Implications for • power consumption and cooling • adaptable architectures • memory hierarchies • interconnects

40. Business Models and Processes

41. Organizational Productivity means that business operations must shift from a vertical to horizontal focus… On Demand: real-time adaptable operations Access digital information Integration: real e-business transactions Processes are bounded by functions Processes led by functions, integrated across functions Processes led by business, extended to value nets Function Function Function Function Function Function Function Function Function A B C A B C A B C Develop Services & Products Provide Financial Management Manage Supply & Logistics • Functions lead business • Traditional business applications – limited integration • Core processes defined, functions still lead business • Integration is “reactive” • Enterprise applications are integrated • Middleware exploits the internet • Planned process integration leads the business activity • Adaptive, integrated enterprise applications • Processes linked with partners and suppliers

42. Business Business Design Financial & Delivery Models Computing Environment Technology These 3 areas combine to yield a state of business… What is an on demand business and why should I become one? • Responsive • Variable • Focused • Resilient Can on demand redefine the way I acquire and manage computing? What kind of computing environment does on demand require, and how do I build one? • Flexible • Variable • Managed • Self-funding • Open • Integrated • Virtualized • Autonomic …with defining attributes

43. Utility Services Road Map High Virtualized server, network, application resources Policy-based, virtualized, delivery Precise metering and billing Core management functions automated Utility Capabilities Where we are Pooled server and management services Managed services Consolidated data centers Already in place Individual data centers Low Time 2001 2002 2003 2004

44. Business Process as Unit of Analysis • Each process has deep knowledge and value • Need to study and focus • But there are generic properties also • Business processes • Measures of success • Control features • Formal properties and descriptions • Physical and real-world constraints • Decomposition and composition

45. Business Process Analysis Optimization • Once properties are quantitative and rigorously defined, can • Apply modeling techniques • Apply mathematical analysis • Do simulations and hypotheticals • Quantify risk

46. Business Process Execution • Performing • Real-time monitoring and control • Rapid decision-making within the process • Feedback to the management layer • Adaptation and performance.

47. Challenges Computing, Business

48. Challenges • Make “it” work well • Real Soon • Reliably • Adaptably • Repeatably • Better than anyone else can do it • Meet customer’s needs • Variability and flexibility and adaptability • Economy and efficiency • Trustworthiness and survivability • Competitiveness and profitability

49. Making IT Real • Autonomic application stacks • How to get all the self-* features in real life • For new apps and major middleware • Atop managed infrastructures • Sharing resources, providing backup, etc. • Getting all the –ilities • Fault management • Configuration • Accounting and auditing • Performance monitoring and improvement • Security

50. Making IT Real for On Demand • Appropriate measures of efficiency and trust • What are the tradeoffs • What are the minima • Heterogeneous everything • Hardware • Middleware • Legacy apps • Business processes (across divisions, partners, mergers)’ • Dynamic everything too • Configuration, deployment • Choice of services and server • Coping with volatility and variability • Including the role of humans! • As actors • As participants and collaborators • As monitors • As beneficiaries