Descriptions of cloud computing often emphasize the silver lining more than the chances of getting wet. Utility computing offers many benefits, but will the cloud -especially the public cloud- lead to the extinction of CIOs because IT will be consumed as simply as electricity? No doubt, cloud computing is a breakthrough technology that will continue to unleash new innovations and bring new efficiencies and advantages to business. It removes infrastructure and capital expense as a barrier to entry and allows startups to scale up cheaply and rapidly. On the other hand, enterprises face limitations in using the cloud for high-performance and mission-critical applications such as ERP. Unfortunately, the cloud’s limits are often obscured by all the hype. It’s time to stop looking at the cloud as a panacea. This presentation seeks to clear up some misperceptions and help people make better choices.

1. The Limits Of The Public Cloud For Enterprise Applications Paul Hofmann, PhDVP Research, SAP Labs, Palo Alto

2. What Does SAP Do? Financial/Mgmt Accounting Sales Order Management Talent Management Production Planning Business Intelligence

3. SUPPLIER CUSTOMER DISTRIBUTIONCENTER CFO CRO Sourcing CUSTOMS/ REGULATORY AGENCY CUSTOMS/ REGULATORY AGENCY Customs Operations COO Fulfillment Receiving Manufacturing EVERY 2ND DOLLAR OF WORLD TRADE RUNS ON SAP Suppliers and Customers Exports Imports Export Compliance GLOBAL ENTERPRISE

4. 100,000 Companies Run SAP

6. 100,000 companies run SAP software

7. 121,000 installations

8. Provides 26 industry solutions

11. Will There Be No More CIOs?

12. The Big SwitchNic Carr [A]t a purely economic level, the similarities between electricity and information technology are even more striking. Both are what economists call general-purpose technologies. Used by all sorts of people to do all sorts of things, they perform many functions rather than just one or a few. General purpose technologies, or GPTs, are best thought of not as discrete tools but as platforms on which many different tools, or applications, can be constructed. . . . Once it becomes possible to provide the technology centrally, large-scale utility suppliers arise to displace the private providers. It may take decades for companies to abandon their proprietary supply operations and all the investment they represent. But in the end the savings offered by utilities become too compelling to resist, even for the largest enterprises. The grid wins.(Carr 2008).

14. Pace of Innovation

16. Lock-In & Interoperability

17. SecurityCloud computing and electricity: beyond the utility modelErik Brynjolfsson, Paul Hofmann, John Jordan, ACM May 2010

18. Limits of ScaleFor a long while to come Three Open Research Questions besides others No scalable storage with API as rich as SQL Transactional systems in the cloud? CAP and eventual consistency Getting rid of C helps with scalability Service Level Agreements Composition is even tougher For more see Above the Clouds: A Berkeley View of Cloud Computing UC Berkeley Reliable Adaptive Distributed Systems Laboratory http://radlab.cs.berkeley.edu/

19. Traditional Database Architecture The Database-Tier limits application scalability! [R. Buck-Emden. The SAP R/3 System. Addison-Wesley, 2nd edition, 1999]

20. Cloud Computing Promise -> Cloud = Utility The Cloud Computingerapromises Scalability/Elasticity Virtualization Pay-as-you-go – Cap-ex to Op-ex Wide range of services offered Infrastructureservices (e.g., Data Storage, Virtual Machines...) Platformservices (e.g., Web-ApplicationHosting, Map-Reduce...) Resulting in a jungle of services Divergent properties and guarantees Different internalarchitectures Oftennotcompatible ...

21. Tested Services

22. Throughput [WIPS] Systems Group | Department of Computer Science | ETH Zurich

23. Cost [m$/WI] Systems Group | Department of Computer Science | ETH Zurich

24. Cost Predictability [m$/WI] In an optimal scenariotheCost / WI isconstant and independent of theload s thelowerthebetter Google‘s price model fits best the pay as-you-go paradigm! Systems Group | Department of Computer Science | ETH Zurich

25. SLA and Composition of SLA Outsourced Shared-Resource Computing for EnterpriseNeeds Reliability, Performance, Predictability and Service Price Pressure in the Cloud Opposes Guaranteed Levels of Performance Today’s Cloud Provider Offer Little or no Commitment For example variable performance on AWSWinterford, B.http://www.itnews.com.au/News/153451,stress-tests-rain-on-amazons-cloud.aspx Use of old HW, some cloud providers replace HW only when it fails Traffic shaping, drop of speed during transfer of infrastructure 99.999% uptime = 5 min downtime/year -> in reality = 10% discounttoday’s cloud offers realistically 3 9s, enterprise needs typically 4 9s -> costs grow with power 25 9s mean no single point of failure and always deployed in more than one location Big Challenge – Storage With Performance & Reliability that is Cheaper Traditional storage technologies fail since cloud workload is “not” predictable Randomness of disk access and working set size are proportional to number of different applications Composition of SLA is Still Unsolved Research Problem!

26. Pace of IT Innovation is NOT Typical for Commodities – Moore’s Law ICT Reinvents Itself Regularly - iPod would have cost $ 2 B in 1976 Electricity has evolved very slowly

27. Latency Is Not Dead Speed of Light is a Limiting Factor Network doesn't keep up with memory and storage 1990, 1 GBps – 2000, increase to 1 TBps Stock Exchange Data Centers are collocated Data should be close to computation Cloud cost advantage gets lost when moving data between cloud providers 10 TByte over network Berkeley to Seattle45 days & cost $1,000 10 X 1 TByte shipping overnight only $400

28. Beyond ElectricityThe Business Model of the Public Cloud Synergies and Co-invention Does the Cloud Stifle Innovation? Lock-In and Interoperability Cloud models are not compatible Security and Loss of Control of Data

29. Co-Invention – IT and Business ProcessesIT Reinvents Itself and Business Does Too Utility Model Assumes that IT Does Not Add Competitive Advantage No competitive advantage with one cloud instance for all companies Huge data traffic between cloud and on premise extensions Limitation by extensibility of cloud provider Example Apple AppStore > 10 M transactions/day with SAP Business Suite  Co-Invention of IT and new biz model allowed Apple to quadruple its revenue between 2004 and 2009 from $ 8 B to $ 32 B – bigger than MS Bresnahan, T., Greenstein, S., Brownstone, D. and Flamm, K. (1996), “Technical Progress and Co-Invention in Computing and in the Uses of Computers” Brookings Papers on Economic Activity. Microeconomics, Vol. 1996, pp. 1-83 Brynjolfsson, E. and Saunders, A. (2010) Wired for Innovation: How IT is Reshaping the Economy. Cambridge: MIT Press.

30. Interoperability Management Is Amazon’s API the Cloud Standard? Only 4 vendors deliver this proprietary APIAWS, Eucalyptus, Could.com and Nimbula EC2 is actually a tightly defined server & network architecture with little room for innovation in distributed application architectures & infrastructure configuration Image and Data DMTF's Open Virtualization Format (OVF)? Defines a format describing a VM that can be interpreted by a variety of virtualization platformsVMWare'svSphere, RedHat's KVM distribution Cloud providers have proprietary data storage For example, BigTable (Google), Salesforce, ...

31. Interoperability – General On premise enterprises control their infrastructure and platforms model In the cloud, they’re locked in to a provider no control Cloud providers speak different languages Cloud Computing Models are not compatible IaaS, PaaS and SaaS are only logical models Google’s BigTable, Amazon’s Dynamo, Facebook’s Cassandra, Salesforce uses RDBMS Facebook, Google, Amazon & Apple capture data in their DB

32. Dragons Control The Cloud

33. Security and Loss of Control of Data Cloud (Virtualization) and Security Are a Trade Off Different Apps run on the same physical machine How to audit if one doesn’t know where data resides? Loss of Control of Data Behind the fire wall enterprises control their data In the US one loses legal rights if data is stored in cloud Accountability

34. Cloud Services as a % of IT IT Cloud Services On-Premise IT 10% CAGR 26% 5% 4% Worldwide IT Spending by Consumption 600 500 44 400 17 Worldwide IT Spending ($ billion) 300 416 200 359 100 0 Source: IDC, September 2009 2009 2013

35. Six Reasons Why IT Won’t Be Like Electricity Technical Weaknesses of the Electricity Model The Limits of Scale Pace of Innovation Latency: Distance is not Dead Beyond ElectricityThe business model of the cloud Synergies and Co-invention Lock-In & Interoperability - flip side of innovation Security

36. Cloud Is Disruptive Opportunity for Apps Web apps, e.g. social networking, sustainability, etc Mobile Apps4B cell phones worldwide ,1.13 B sold 2009 vs. 15 B shoes “Embarrassingly parallel” batch processing BI, analytics, collaboration, promotions, failure recovery, Basic apps with little extensibility Salesforce, Workday Market Start ups will use cloud “like a foundry” Move peaks to cloud for testing, promotions, … SME – basic apps w limited extensibility like CRM, HCM Private Cloud SAP Business By Design, eSourcing, StreamWork ERP will take a while yet - 10 to 15 years

37. An Alternative To Non-Scaling RDBMS and High Latency

38. Cloud Computing Has Introduced High Latency Large-scale Cloud Apps Struggle with High Latency Web Application Storage Server Application Server 0.5-10ms latency

39. And a Technology Gap between Cloud & Modern Server With today’s servers: Cores and Memory scale in lock step Access latencies to large memories from modern cores (latency) Addressing those memories And the current business need: Low latency decision making and analytics Marketing analysis Search engines “real” real time This creates a technology conflict: Cores and Memory need to scale more independently

40. The Alternative to Remote Communication – Coherent Shared Memory Uses high-speed, low latency networks (40Gb/s or above) Typical latencies of this are in the area of 1-5 μsec Throughput is higher than the CPU can consume L4 cache needed to balance the longer latency on non-local access(cache-coherent non-uniform memory access over different physical machines) Separate the data transport and cache layers into a separate tier below the operating system- never seen by the application or the operating system! Do we need to separate application servers from database server? Applications and database code can just reference data The data is just “there”, i.e. it’s a load/store architecture, not network datagrams Application designers do not confront communications protocol design issues Parallelization of analytics and combining simulation with data are far simpler, enabling powerful new business capabilities of mixed analytics and decision support at scale

41. The Principle of Coherent Shared Memory

42. Big Iron - Commodity HPCDesign by SAP Enterprise Supercomputer – Back to Mainframe5 X 4U Nodes (Intel XEON x7560 2.26Ghz) 160 cores (320 Hyper-threads) 5 X 32 5 TB memory total, 30TB solid state disk 160 GB/s InfiniBand interconnect per node Scalable coherent shared memory (via ScaleMP) Developers don’t need additional skills for in-memory Data base becomes data structures Scalable DB on virtualized HW – Alternative to Cloud

43. A Simplified Programming Model and Landscape Virtual Machine 1 Virtual Machine 2 Virtual Machine 3 … Virtual Machine N Old View Physical Machine Physical Machine 1 Physical Machine 2 PhysicalMachine 3 … Physical Machine M Emerging View Virtual Machine

44. Summary Cloud computing has emerged BUT there be will CIOs in future It will take many years till ERP systems will run in the Cloud There is a growing computing technology gap The businesses of tomorrow will function in real time The “mainframe” can be reincarnated with commodity parts These enterprise supercomputers will enable new biz paradigm