Abhishek Verma, Saurabh Nangia

Cloud computing hypeCynicismMapReduce Vs Parallel DBMSCost of a cloudDiscussion

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Google App Engine(April 2008)

Microsoft Azure(Oct 2008)

Facebook Platform

(May 2007)

Amazon EC2(August 2006)

Amazon S3(March 2006)

SalesforceAppExchange(March 2006)

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Cloud Computin

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

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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.”

“Cloud computing is simply a buzzword used to repackage grid computing

and utility computing, both of which have existed for decades.”

whatis.comDefinition of Cloud Computing

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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?”

“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 EllisonDuring Oracle’s Analyst Day

From http://blogs.wsj.com/biztech/2008/09/25/larry-ellisons-brilliant-anti-cloud-computing-rant/ 8

From http://geekandpoke.typepad.com 9

Many enterprise (necessarily or unnecessarily) set their SLAs uptimes at 99.99% or higher, which cloud providers have not yet been prepared to match

Amazon’s cloud outages receive a lot of exposure …

July 20, 2008

Failure due to stranded zombies, lasts 5 hours

Feb 15, 2008

Authentication overload leads to two-hour service outage

October 2007

Service failure lasts two days

October 2006

Security breach where users could see other users data

… and their current SLAs don’t match those of enterprises*

Amazon EC2 99.95% Amazon S3 99.9%

* 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

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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

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

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Architectural Element

Parallel Databases MapReduce

Schema Support

Structured Unstructured

IndexingB-Trees or Hash based

None

Programming Model

Relational Codasyl

Data Distribution

Projections before aggregation

Logic moved to data, but no optimizations

Execution Strategy

Push Pull

FlexibilityNo, but Ruby on Rails, LINQ

Yes

Fault ToleranceTransactions have to be restarted in the event of a failure

Yes: Replication, Speculative execution

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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

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

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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

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SELECT * FROM Data WHERE field LIKE ‘%XYZ%’;

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SELECT pageURL, pageRankFROM Rankings WHERE pageRank > X;

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SELECT INTO Temp sourceIP,AVG(pageRank) as avgPageRank,SUM(adRevenue) as totalRevenueFROM Rankings AS R, UserVisits AS UVWHERE R.pageURL = UV.destURLAND UV.visitDate BETWEEN Date(‘2000-01-15’)AND Date(‘2000-01-22’)GROUP BY UV.sourceIP;

SELECT sourceIP, totalRevenue, avgPageRankFROM TempORDER BY totalRevenue DESC LIMIT 1;

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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?

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Albert Greenberg, James Hamilton, David A. Maltz, Parveen Patel

MSR Redmond

Presented by: Saurabh Nangia

Cost of cloud service

Improving low utilization Network agility Incentive for resource consumption Geo-distributed network of DC

Where does the cost go in today’s cloud service data centers?

Amortized Costs (one time purchases amortized over reasonable lifetimes, assuming 5% cost of

money)

45%

25%

15%

15%

Can existing solutions for the enterprise data center work for cloud

service data centers?

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

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”

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

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

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%

Uneven Application Fit

Uncertainty in demand forecasts

Long provisioning time scales

Risk Management

Hoarding

Virtualization short-falls

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

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

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

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

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

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

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

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

*http://perspectives.mvdirona.com/2008/11/28/CostOfPowerInLargeScaleDataCenters.aspx

Increasing Network Agility

Appropriate incentives to shape resource consumption

Joint optimization of Network & DC resources

New mechanisms for geo-distributing states

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

DC network two types of traffic Between external end systems &

internal servers Between internal servers

Load BalancerVirtual IP address (VIP)Direct IP address (DIP)

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

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

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!

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

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

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

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!

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

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

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)

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

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

Data center costs Server, Infrastructure, Power,

Networking

Improving efficiency Network Agility Resource Consumption Shaping Geo-diversifying DC

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"

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

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.”

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,”

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

John Chambers, Cisco Systems’ CEO "a security nightmare.” “cloud computing was inevitable, but

that it would shake up the way that networks are secured…”

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”

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“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