Cloud Computing Skepticism PowerPoint Presentation

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Cloud Computing Skepticism Abhishek Verma, Saurabh Nangia

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Outline Cloud computing hype Cynicism MapReduce Vs Parallel DBMS Cost of a cloud Discussion 2

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Recent Trends Google App Engine (April 2008) Microsoft Azure (Oct 2008) Facebook Platform (May 2007) Amazon EC2 (August 2006) Amazon S3 (March 2006) Salesforce AppExchange (March 2006) 3

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Tremendous Buzz 4

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Gartner Hype Cycle* * From http://en.wikipedia.org/wiki/Hype_cycle 5

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Blind men and an Elephant CLOUD COMPUTING 6

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“Cloud computing is simply a buzzword used to repackage grid computing and utility computing, both of which have existed for decades.” whatis.com Definition of Cloud Computing 7

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“The interesting thing about cloud computing is that we’ve redefined cloud computing to include everything that we already do. […] The computer industry is the only industry that is more fashion-driven than women’s fashion. Maybe I’m an idiot, but I have no idea what anyone is talking about. What is it? It’s complete gibberish. It’s insane. When is this idiocy going to stop?” Larry Ellison During Oracle’s Analyst Day From http://blogs.wsj.com/biztech/2008/09/25/larry-ellisons-brilliant-anti-cloud-computing-rant/ 8

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From http://geekandpoke.typepad.com 9

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Reliability Many enterprise (necessarily or unnecessarily) set their SLAs uptimes at 99.99% or higher, which cloud providers have not yet been prepared to match * SLAs expressed in Monthly Uptime Percentages; Source : McKinsey & Company Not clear that all applications require such high services IT shops do not always deliver on their SLAs but their failures are less public and customers can’t switch easily 10

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A Comparison of Approaches to Large-Scale Data Analysis* Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel J. Abadi, David J. DeWitt, Samuel Madden, Michael Stonebraker To appear in SIGMOD ‘09 *Basic ideas from MapReduce - a major step backwards, D. DeWitt and M. Stonebraker

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MapReduce – A major step backwards A giant step backward No schemas, Codasyl instead of Relational A sub-optimal implementation Uses brute force sequential search, instead of indexing Materializes O(m.r) intermediate files Does not incorporate data skew Not novel at all Represents a specific implementation of well known techniques developed nearly 25 years ago Missing most of the common current DBMS features Bulk loader, indexing, updates, transactions, integrity constraints, referential Integrity, views Incompatible with DBMS tools Report writers, business intelligence tools, data mining tools, replication tools, database design tools 12

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MapReduce II* MapReduce didn't kill our dog, steal our car, or try and date our daughters.  MapReduce is not a database system, so don't judge it as one Both analyze and perform computations on huge datasets MapReduce has excellent scalability; the proof is Google's use Does it scale linearly? No scientific evidence MapReduce is cheap and databases are expensive We are the old guard trying to defend our turf/legacy from the young turks Propagation of ideas between sub-disciplines is very slow and sketchy Very little information is passed from generation to generation * http://www.databasecolumn.com/2008/01/mapreduce-continued.html 14

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Tested Systems Hadoop 0.19 on Java 1.6, 256MB block size, JVM reuse Rack-awareness enabled DBMS-X (unnamed) Parallel DBMS from a “major relational db vendor” Row based, compression enabled Vertica (co-founded by Stonebraker) Column oriented Hardware configuration: 100 nodes 2.4 GHz Intel Core 2 Duo 4GB RAM, 2 250GB SATA hard disks GigE ports, 128Gbps switching fabric 15

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Data Loading Hadoop Command line utility DBMS-X LOAD SQL command Administrative command to re-organize data Grep Dataset Record = 10b key + 90b random value 5.6 million records = 535MB/node Another set = 1TB/cluster 16

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Grep Task Results SELECT * FROM Data WHERE field LIKE ‘%XYZ%’; 17

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Select Task Results SELECT pageURL, pageRank FROM Rankings WHERE pageRank > X; 18

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Join Task SELECT INTO Temp sourceIP, AVG(pageRank) as avgPageRank, SUM(adRevenue) as totalRevenue FROM Rankings AS R, UserVisits AS UV WHERE R.pageURL = UV.destURL AND UV.visitDate BETWEEN Date(‘2000-01-15’) AND Date(‘2000-01-22’) GROUP BY UV.sourceIP; SELECT sourceIP, totalRevenue, avgPageRank FROM Temp ORDER BY totalRevenue DESC LIMIT 1; 19

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Concluding Remarks DBMS-X 3.2 times, Vertica 2.3 times faster than Hadoop Parallel DBMS win because B-tree indices to speed the execution of selection operations, novel storage mechanisms (e.g., column-orientation) aggressive compression techniques with ability to operate directly on compressed data sophisticated parallel algorithms for querying large amounts of relational data. Ease of installation and use Fault tolerance? Loading data? 20

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The Cost of a Cloud: Research Problem in Data Center Networks Albert Greenberg, James Hamilton, David A. Maltz, Parveen Patel MSR Redmond Presented by: Saurabh Nangia

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Overview Cost of cloud service Improving low utilization Network agility Incentive for resource consumption Geo-distributed network of DC

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Cost of a Cloud? Where does the cost go in today’s cloud service data centers?

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Cost of a Cloud Amortized Costs (one time purchases amortized over reasonable lifetimes, assuming 5% cost of money) 45% 25% 15% 15%

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Are Clouds any different? Can existing solutions for the enterprise data center work for cloud service data centers?

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Enterprise DC vs Cloud DC (1) In enterprise Leading cost: operational staff Automation is partial IT staff : Servers = 1:100 In cloud Staff costs under 5% Automation is mandatory IT staff : Servers = 1:1000

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Enterprise DC vs Cloud DC (2) Large economies of scale Cloud DC leverage economies of scale But up front costs are high Scale Out Enterprises DC “scale up” Cloud DC “scale out”

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Types of Cloud Service DC (1) Mega data centers Tens of thousands (or more) servers Drawing tens of Mega-Watts of power (at peak) Massive data analysis applications Huge RAM, Massive CPU cycles, Disk I/O operations Advantages Cloud services applications build on one another Eases system design Lowers cost of communication needs

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Types of Cloud Service DC (2) Micro data centers Thousands of servers Drawing power peaking in 100s of Kilo-Watts Highly interactive applications Query/response, office productivity Advantages Used as nodes in content distribution network Minimize speed-of-light latency Minimize network transit costs to user

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Cost Breakdown

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Server Cost (1) Example 50,000 servers $3000 per server 5% cost of money 3 year amortization Amortized cost = 50000 * 3000 * 1.05 / 3 = $52.5 million dollars per year!! Utilization remarkably low, ~10%

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Server Cost (2) Uneven Application Fit Uncertainty in demand forecasts Long provisioning time scales Risk Management Hoarding Virtualization short-falls

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Reducing Server Cost Solution: Agility to dynamically grow and shrink resources to meet demand, and to draw those resources from the most optimal location. Barrier: Network Increases fragmentation of resources Therefore, low server utlization

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Infrastructure Cost Infrastructure is overhead of Cloud DC Facilities dedicated to Consistent power delivery Evacuating heat Large scale generators, transformers, UPS Amortized cost: $18.4 million per year!! Infra cost: $200M 5% cost of money 15 year amortization

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Reducing Infrastructure Cost Reason of high cost: requirement for delivering consistent power Relaxing the requirement implies scaling out Deploy larger numbers of smaller data centers Resilience at data center level Layers of redundancy within data center can be stripped out (no UPS & generators) Geo-diverse deployment of micro data centers

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Power Power Usage Efficiency (PUE) = (Total Facility Power)/(IT Equipment Power) Typically PUE ~ 1.7 Inefficient facilities, PUE of 2.0 to 3.0 Leading facilities, PUE of 1.2 Amortized cost = $9.3million per year!! PUE: 1.7 $.07 per KWH 50000 servers each drawing average 180W

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Reducing Power Costs Decreasing power cost -> decrease need of infrastructure cost Goal: Energy proportionality server running at N% load consume N% power Hardware innovation High efficiency power supplies Voltage regulation modules Reduce amount of cooling for data center Equipment failure rates increase with temp Make network more mesh-like & resilient

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Network Capital cost of networking gear Switches, routers and load balancers Wide area networking Peering: traffic handed off to ISP for end users Inter-data center links b/w geo distributed DC Regional facilities (backhaul, metro-area connectivity, co-location space) to reach interconnection sites Back-of-the-envelope calculations difficult

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Reducing Network Costs Sensitive to site selection & industry dynamics Solution: Clever design of peering & transit strategies Optimal placement of micro & mega DC Better design of services (partitioning state) Better data partitioning & replication

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Perspective On is better than off Server should be engaged in revenue production Challenge: Agility Build in resilience at systems level Stripping out layers of redundancy inside each DC, and instead using other DC to mask DC failure Challenge: Systems software & Network research

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Cost of Large Scale DC *http://perspectives.mvdirona.com/2008/11/28/CostOfPowerInLargeScaleDataCenters.aspx

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Solutions!

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Improving DC efficiency Increasing Network Agility Appropriate incentives to shape resource consumption Joint optimization of Network & DC resources New mechanisms for geo-distributing states

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Agility Any server can be dynamically assigned to any service anywhere in DC Conventional DC Fragment network & server capacity Limit dynamic growth and shrink of server pools

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Networking in Current DC DC network two types of traffic Between external end systems & internal servers Between internal servers Load Balancer Virtual IP address (VIP) Direct IP address (DIP)

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Conventional Network Architecture

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Problems (1) Static Network Assignment Individual applications mapped to specific physical switches & routers Adv: performance & security isolation Disadv: Work against agility Policy-overloaded (traffic, security, performance) VLAN spanning concentrates traffic on links high in tree

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Problems (2) Load Balancing Techniques Destination NAT All DIPs in a VIPs pool be in the same layer 2 domain Under-utilization & fragmentation Source NAT Servers spread across layer 2 domain But server never sees IP Client IP required for data mining & response customization

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Problems (3) Poor server to server connectivity Connection b/w servers in diff layer 2 must go through layer 3 Links oversubscribed Capacity of links b/w access router & border routers < output capacity of servers connected to access router Ensure no saturation in any of network links!

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Problems (4) Proprietary hardware scales up, not out Load balancers used in pairs Replaced when load becomes too much

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DC Networking: Design Objectives Location-independent Addressing Decouple servers location in DC from its address Uniform Bandwidth & Latency Servers can be distributed arbitrarily in DC without fear of running into bandwidth choke points Security & Performance Isolation One service should not affect other’s performance DoS attack

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Incenting Desirable Behavior (1) Yield management to sell the right resources to the right customer at the right time for the right price Trough filling Cost determined by height of peaks, not area Bin packing opportunities Leasing committed capacity with fixed minimum cost Prices varying with resource availability Differentiate demands by urgency of execution

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Incenting Desirable Behavior (2) Server allocation Large unfragmented servers & Agility Less requests for servers Eliminating hoarding of servers Cost for having a server Seasonal peaks Internal auctions may be fairest But, how to design!

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Geo-Distribution Speed & latency matter Google 20% revenue loss for 500ms delay!! Amazon 1% sales decrease for 100ms delay!! Challenges Where to place data centers How big to make them Using it as a source of redundancy to improve availability

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Optimal Placement & Sizing (1) Importance of Geographical Diversity Decreasing latency b/w user and DC Redundancy (earthquakes, riots, outages, etc) Size of data center Mega DC Extracting maximum benefit from economies of scale Local factors like tax, power concessions, etc. Micro DC Enough servers to provide statistical multiplexing gains Given a fixed budget, place closes to each desired population

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Optimal Placement & Sizing (2) Network cost Performance vs cost Latency vs Internet peering & dedicated lines between data centers Optimization should also consider Dependencies of services offered Email -> buddy list maintenance, authentication, etc Front end: micro data centers (low latency) Back end: mega data centers (greater resources)

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Geo-Distributing State (1) Turning geo-diversity to geo-redundancy Distribute critical state across sites Facebook Single master data center replicating data Yahoo! Mail Partitions data across DCs based on user Different solutions for Different data Buddy status: replicated weak consistency assurance Email: mailbox by user ids, strong consistency

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Geo-Distributing State (2) Tradeoffs Load distribution vs service performance eg Facebook’s single master coordinate replication Speeds up lookup but loads on master Communication cost vs service performance Data replication-more inter data center communication Longer latency Higher cost message over inter DC links

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Summary Data center costs Server, Infrastructure, Power, Networking Improving efficiency Network Agility Resource Consumption Shaping Geo-diversifying DC

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Opinions

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Richard Stallman, GNU founder Cloud Computing is a trap “.. cloud computing was simply a trap aimed at forcing more people to buy into locked, proprietary systems that would cost them more and more over time.” "It's stupidity. It's worse than stupidity: it's a marketing hype campaign"

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Open Cloud Manifesto a document put together by IBM, Cisco, AT&T, Sun Microsystems and over 50 others to promote interoperability "Cloud providers must not use their market position to lock customers into their particular platforms and limit their choice of providers,” Failed? Google, Amazon, Salesforce and Microsoft, four very big players in the area, are notably absent from the list of supporters

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Larry Ellison, Oracle founder "fashion-driven" and "complete gibberish” “What is it? What is it? ... Is it - 'Oh, I am going to access data on a server on the Internet.' That is cloud computing?“ “Then there is a definition: What is cloud computing? It is using a computer that is out there. That is one of the definitions: 'That is out there.' These people who are writing this crap are out there. They are insane. I mean it is the stupidest.”

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Sam Johnston, Strategic Consultant Specializing in Cloud Computing, Oracle would be out badmouthing cloud computing as it has the potential to disrupt their entire business. "Who needs a database server when you can buy cloud storage like electricity and let someone else worry about the details? Not me, that's for sure - unless I happen to be one of a dozen or so big providers who are probably using open source tech anyway,”

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Marc Benioff, head of salesforce.com “Cloud computing isn't just candyfloss thinking – it's the future. If it isn't, I don't know what is. We're in it. You're going to see this model dominate our industry." Is data really safe in the cloud? "All complex systems have planned and unplanned downtime. The reality is we are able to provide higher levels of reliability and availability than most companies could provide on their own," says Benioff

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John Chambers, Cisco Systems’ CEO "a security nightmare.” “cloud computing was inevitable, but that it would shake up the way that networks are secured…”

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James Hamilton, VP Amazon Web Services “any company not fully understanding cloud computing economics and not having cloud computing as a tool to deploy where it makes sense is giving up a very valuable competitive edge” “No matter how large the IT group, if I led the team, I would be experimenting with cloud computing and deploying where it make sense” 67

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To Cloud or Not to Cloud?

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References “Clearing the air on cloud computing”, McKinsey&Company http://geekandpoke.typepad.com/ “Clearing the Air - Adobe Air, Google Gears and Microsoft Mesh”, Farhad Javidi http://en.wikipedia.org/wiki/Hype_cycle “A Comparison of Approaches to Large-Scale Data Analysis”, Pavlo et al MapReduce - a major step backwards, D. DeWitt and M. Stonebraker 69