Introduction to cloud technology

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Introduction to cloud technology

  1. 1. ▼ Lme * 5>ave c m n e u An Introduction to Cloud Computing
  2. 2. ▼ • r utm ovn W hat is Cloud Computing? • How can we define Cloud Computing? • W hat does (and does not) constitute a "cloud” platform? • Enabling technologies fo r cloud computing • Virtual Machines • Virtualised Storage • Web Services
  3. 3. Cloud Computing defined... a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet” - W ikipedia
  4. 4. Cloud Computing defined... • “ Clouds are hardware-based services offering compute, network and storage capacity where: Hardware management is highly abstracted from the buyer, Buyers incur infrastructure costs as variable OPEX.and Infrastructure capacity is highly elastic” - M cKinsey & Co. Report: “Clearing the A ir on Cloud Com puting”
  5. 5. Cloud Computing defined... “ Cloud computing has the following characteristics: (l)T h e illusion of infinite computing resources... (2)The elimination of an up-front commitment by Cloud users... (3).The ability to pay for use...as needed.. . - UCBerkeley RA D Labs
  6. 6. . - f Cloud Computing defined... • “ ...a pay-per-use model for enabling available, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, services) that can be rapidly provisioned and released w ith minimal management effort or service provider interaction.” - National Institute of Standards and Technology (N IST)
  7. 7. Cloud Computing defined... • "Clouds are a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services).These resources can be dynamically re-configured to adjust to a variable load (scale), allow-ing also for an optimum resource utilization.This pool of resources is typically exploited by a pay-per-use model in which guarantees are offered by the Infrastructure Provider by means of customized SLAs.” - Paper by Vaquero et. al.: “A break in the clouds: towards a cloud definition” 12
  8. 8. Common Ground? Pay-per-use (no commitment, utility prices). Elastic capacity - scale up/down on demand. Self-service interface. Resources are abstracted / virtualised.
  9. 9. Enabling Technologies • Several key components have matured and are essential building blocks of many Cloud services: • Virtual Machines • Virtualised Storage • Web Services
  10. 10. Virtual Machines In the last 5 years VM technology has become pervasive and “ cheap” / free The main players are: VMWare [ESX/GSX Server,Workstation] XenSource [XenEnterprise/Server/Express] SWsoft/Parallels [Virtuozzo, Desktop] Microsoft [Virtual Server]
  11. 11. Factors contributing to rising profile: • Commodity multi-core, 64bit machines [dual/quad core, » 4 gig memory] • Hardware assisted virtualisation - using Intel VT(tm) and AM D Pacifica(tm) - runs unmodified OS at near native speeds • Integration ofVM tech. into mainstream server OS’s (RHEL, Ubuntu,Windows) Virtual Machines
  12. 12. Virtual Machines Improves utilisation by multiplexing many VMs on one physical host (consolidation) Allows agile deployment and management of services. On demand cloning, (live) migration & checkpoint improves reliability. VM can be a self-contained unit of execution and migration!
  13. 13. Virtualised Storage Distributed File Systems: • Google File System (GFS) • Hadoop Distributed File System (HDFS) Cluster File Systems: • VMware vStorageVMFS • XenServer Storage Pool
  14. 14. W eb Services SOAP • XM L Messages • Web Services Description Language (WSDL) • W S-* REST / RESTful • GET, POST, PUT. DELETE fo r HTTP (crud) • More lightweight HTTP / JSON schemes
  15. 15. Types of Clouds? I E m a i l ▼ L m e * 5 ) a v e o c m n e u Hybrid ( Private/ J / . 7 Internal J — j - / Public/ External J On Premises / Internal Off Premises I Third Party Cloud Computing Types
  16. 16. hi email TLwe *:>ave « cninea Public Cloud • Publicly accessible, self-service model: • Access via well-defined & published Web Services (i.e. SOAP o r REST). • Access via management portal. • Free o r pay-per-use. • N o ongoing contract o r commitment. 21
  17. 17. k M t r n a n ▼ l i k e ♦ i a v e <> c i n n e u Private Cloud • Emulate Public Cloud on private / internal resources. • Gives benefit of Clouds (elasticity, dynamic provisioning, multiplexing) while: • Maintaining control o f resources (security). • Meeting C orporate/Regulatory req. (governance). • O ption to scale ou t to Public Cloud ... 22
  18. 18. Hybrid Cloud • Combination of private/internal and external Cloud resources. • “ Cloudbursting" to handle “ Flash Crowds” • Provision additional resources from Public Clouds on-demand. • Release resource when no longer needed. • Can outsource non-critical functions. 2)
  19. 19. How did we get here? • W hat about: • Cluster Computing? • Grid Computing? • U tility Computing? C lu s te r C o m p u tin g G rid C o m p u tin g U tility C o m p u tin g
  20. 20. ▼iwn •-■r. ixnmvu Cluster Computing • Clusters are linked computer systems that can co-operate to perform computations and deliver services: • Often function / appear as single server. • Typically linked over LAN. • Offers scalability over single-server. • Used for high-availability (redundant), load balancing, shared compute (w/ IPC). 25
  21. 21. k M t r n a n t u k b ♦ i a v e o c n i n e a Grid Computing • Grids are autonomous and dynamic distributed resources contributed by multiple organisations. • Can offer computing, network, sensor and storage resources. • Resources are loosely coupled, heterogeneous, geographically dispersed. • Used in diverse fields: climate modelling, drug design and protein analysis to solve "Grand Challenges” . 26
  22. 22. kM trn a n t u k b ♦ ia v e o c n in e a Grid Computing • Traditional Grid resource management techniques did not ensure fair and equitable access to resources in many systems: • Traditional metrics (throughput, waiting time, slowdown) failed to capture the m ore subtle requirements o f users. • N o incentives for users to be flexible about resource req. o r job deadlines. • N o provisions to accommodate users w ith urgent work. 27
  23. 23. w j t n i a i i ▼ L m e * 5 ) a v e o c m n e u Utility Computing • U sers assign a "u tility " value to th e ir jobs: • U tility is a fixe d o r tim e -v a ry in g valu atio n th a t captures various Q o S con straints (deadlin e, im p o rta n c e , satisfaction) • V aluation is a m o u n t th e y are w illing to pay a service p ro v id e r to satisfy dem ands. • S ervice p ro vid ers a tte m p t to m axim ise th e ir o w n utility: • U tility m ay d ire c tly c o rre la te w ith th e ir p ro fit. • P rio ritis e high yield (i.e. p ro fit p e r unit o f re s o u rc e ) user jobs. • Shared G rid system s are then v ie w e d as a m a rketp lace, w h e re users c o m p e te fo r reso u rc es based on th e p erce ived u tility / value o f th e ir jobs. 28
  24. 24. I tn ia ii LIK8 : ia v e o c t n n e u Utility Computing Table 1 Summary oi price setting and negotiation System name Price setting Acceptance criteria Penally Nlmrod-G (Ij Filed Posted price No G -commerce [39] Filed CUrtent price > mean_pricr N/A Bellagio |4| Fixed 'Threshold'-based N/A Mirage [12] Fixed 'Winner determination pmblcin' N/A Tycoon (21.23) Fixed First/second price No Libra [35) Fixed Minimum cost No Uhra+$ |J1) Fixed DcadllneJbudgcl feasible Yes (LJhraSLA |-I2]) IJ |24j Flied nth Price No FlrslPrice [13] Variable None No FirxIReward [20) Variable Risk versus reward Yes FirstPiofli 132| Variable Risk versus per-Jnb reward Yes FirslOpporfunity [32] Variable Affect on prolll Yes Aggregate utility [5] Variable Contract feasibility/profitability Yes J Grid O unpuling (H I* ) fc235-27* DOI I0.l007/I|Q723.(1D7.‘)0 '« .J 29
  25. 25. h id t r iia ii ▼ Lm e ♦ .^ a v e <> c rn n e u Utility Computing T ilik 2 Summary ol utility-driven resource management systems System name Allocation Scheduler Adm control Bcllagiu [4] Combinatorial auction-based Proportional share N/A Mirage [12] Auctlon-hosed Proportional share N/A Tycoon [21.22] Auction-based Auction share No LI bin [32] N/A Proportional share Yes (basic) Libra-t-J [41] N/A Proportional share Yes Ll|24] Double auction Proportional shaic N/A FiislPrlce [13] N/A Highest unit value flisl No FlrslRcssard [20] N/A Highest unit value nisi* Yes FlislProfll ]32] N /A Highest pcr-Joh profit first Yes FirslOpportunily |32] N/A Highest total profit first Yes Aggregate utility [3] N/A Hlglieil contract profit first Yes 'R Is k -iM it. J Grid rnrnpullng (2H0H) 6JU5-27h DOI 10.1007/s 1072.1-007-SIN5-J
  26. 26. Public Compute Clouds - Infrastructure as a Service • Amazon EC2 • GoG rid • Slicehost • Mosso Cloud Servers
  27. 27. ▼iwii I ■■r* ocmnva Amazon Elastic Compute Cloud (EC2) • Provides resizable compute capacity in the cloud. • Can be leveraged via: • Web Services (SOAP or REST). • AWS Management Console. • EC2 Command Line Tools. • Provides hundreds of pre-made AMIs. 32
  28. 28. Amazon Elastic Com pute Cloud (EC2) • Standard Instances (S, L, XL) • Prop, more RAM than CPU • High CPU Instances (M ,XL) • Prop, more CPU than RAM • Choice ofW indow s o r Linux Images • US-East and EU-West Regions ($EU>$US) • “ Value-added” AM Is cost more 33 ▼ i iwi • ■ i i n u n u
  29. 29. AmazonEC2Reserved 0 0 <D U C a4-> </> c i i i i ) . i i t . i s 5 3 3 3 5 3 8 I 1 1 1 I I . t I 1 I I I * i 3 | ! a 5 3 2 3 5 3 8 i Ia UJ i.i i | * ^ s ! i f 1 1 ? i , S 1 1 ! ! S J ! 11 i 1 ! I I f I
  30. 30. ^ t i n an Amazon EC2 Windows Server Pricing for Instances Running Windows Server SQL S r v * ErtVMA. M cnM oft IIS a n d ASP NET can Em sited on *ny A m aifln IC 2 n r i J K i rurw^rsg W n d A M S « w ft* no addflionai COM. ▼ Lm e ♦ i a v e <> m n n e u U n lttd fitila a fu ro p * S ta n d a r d I m l i n c i i W ifid a w a W in d o w * w ith A id h a m i c a tio n fta m c o a S m a i fD a fa u ii] t o . 125 par i t t a iO 25 par ru n * Lanja SO 50 par h o t* 10 75 par has* L it n Larg* 11.00 par hour 91.50 par hour W in d o w a W in d o w ! w ith A u th e n tic a tio n W - v tr * a Had SO. 30 pa« hour SO 50 par r i f t r Extra L * g a 11-20 par hour 92 00 par t t t * Pridng for Instances Running Windows Server with SQL Server Standard U n it e d 5 t a t n t i i n p i I n a to n e a T y p a W in d o w s W ln d e w a w ith A u ttw n tic a tio n I t n k a a S U n da ifj ta ig a Standard Extra Larva f 1.10 par h o t* 12 .20 par hour 11 IS par h o w 12 .70 par h m * Nigfi OTJ E m Larga 12 «0 par hour 36 t J 20 pa* h o t*
  31. 31. ^ t i n an Amazon EC2 IBM Platforms Pricing for Instances Running IBM Lotus Web Content Management Server Standard Edition la a u n t a T y p i U f HagI on KU N a tio n Standard Lar^a 4248par h o i* 42 U par hour Standard Extra La.'tga 1482par hour (4 40 par hour C *u E s tn Latga 1871par hour 48 79 par hour Pricing for Instances Running IBM WebSphere Portal Server and IBM Web Content Management Server Standard Edition I r n U n c a ty p e Standard La^a S ta n d a rd E x m L * ? a ll^n C7UCtfri Larga ▼ L in e ♦ ^ a v e c> t m n e u 37 US B a g lo n CU t a g io n flft 31 p a r h o u t 1 4 .4 ] p a r ho u r 1 1 2 .4 4 pa# hour f ] 2 .7 ? p ar ho u r 1 2 4 .3 5 p ar ho u r f 2 4 .4 3 p a r ho u r
  32. 32. ^ t t n a i i Amazon EC2 IBM Platforms Pricing for Instances Running IBM Lotus Web Content Management Server Standard Edition lo a u n t a 1> m US fta flo n f u H a tto n Standard Larga f 2 40 par hour *2 52 par hour Standard Extra La% c I A 82 par hour |4 40 par hour H«ft Cm Extra Largt IB 71 par hour 48 79 par hour Pricing for Instances Running IBM WebSphere Portal Server and IBM Web Content Management Server Standard Edition ImUncfl 1>p« Standard La*ga Standard Extra La*ga llq n C7U (a tra Larga ▼ Lm e ♦ ^ a v e o t t n n e u 37 US R a g la n EU la g le n 16 31 DOT hOut 18 .41 par hour 112.64 pa« hour S I2.T2 par hour 124. IS par hour 424.43 par hour
  33. 33. Amazon EC2 Success Stories • SmugMug - Online Photo Sharing • EC2 instances handles batch ops: upload, rotate, transcode, watermark, etc... • Hybrid approach: O w n datacenter + Cloud, autoscale up/down as needed. • Anim oto - Creates Cool Videos from Photos & Music • Scaled from SO instances of EC2 up to 3,500 instances o f EC2 within days.
  34. 34. ▼lwii • uinmvo GoGrid Cloud Hosting • Provides pre-made images: • W indows and Linux. • “Value-added” stacks on top. • Range of (fixed) instance sizes. • Offers hybrid dedicated / cloud server infrastructure. • Free hardware LB and autoscaling. }»
  35. 35. SlicehostVPS Hosting • Fixed size “ slices” . • Slice size measured by RAM, not CPU. • Pre-made Linux images. • N o SLA, simply “ best effort” . • Monthly billing (not per-hour). • Does that fit “ C loud" definition?
  36. 36. Mosso Cloud Servers • Fixed size Cloud Servers. • Server size measured by RAM, not CPU • Offers range of pre-made Linux instances • Offers hybrid dedicated / cloud server infrastructure. • Offers fixed IPs andA-DNS by default.
  37. 37. Compute Cloud Comparison Amazon EC2 GoGrid Slicehost Mosso Cloud Servers instance Cose JO. 10 $ 1.28/ hr $0,095-$ 1.32 / hr $20 $80 / month $0.0l5-$0 96 / hr Linux Support Yes Yes Yes Yes Windows Support Yes Yes No No Cores/CPU 1-20 1-6 4 (weighted) 4 (weighted) Memory 1.7GB -ISGB 0.SGB - 8GB 256MB - IS.5GB 256MB - ISGB Persistent Storage No {req, S3/EBS) Yes Yes Yes Default Public IP No (req. Elast. IP) Yes Yes Yes Managed DNS No Yes No Yes Support Cost $400 / 20% Spend Free Free SAI00 Hybrid Claud? No Yes No Yes SLA 99.95% 100% N/A 100% Running instances 20* > > Unlimited* API Yes Yes Yes Yes 42
  38. 38. memail ▼Line *^ave 1•cninea Public Storage Clouds Storage as a Service • Amazon Simple Storage Service (S3) • Amazon CloudFront • Nirvanix Storage Delivery N etw ork • Mosso Cloud Files • M icrosoft Azure Storage Services n ir v a n ix J WindowsAzure
  39. 39. ▼iwn • • uunova Amazon Simple Storage Service (S3) • Amazon S3 was launched US in March 2006, and EU in November 2007. • REST/SOAP interfaces to storage resources • Users can read, w rite o r delete an unlimited # of objects, I byte to 5 GB each. • Files stored in flat “ bucket” structure. • BitTorrent can be used to minimise cost. 44
  40. 40. Amazon CloudFront • Launched in November 2008. • CDN service that adds 14 edge locations (8 in the US, 4 in EU, and 2 in Asia). • CloudFront does not offer persistent storage. • Analogous to proxy cache, with files deployed to and removed from CF locations based on demand. 45
  41. 41. Mosso CloudFiles Launched late 2008. Base storage location in Dallas Texas. Files stored in flat “ bucket” structure. Offers integration w ith Limelight C D N . • Adds more than 70 locations. • N o transfer costs from origin to CDN.
  42. 42. t i l E m a i l ▼ L m e ± 5 ) a v e <> c m n e u Cost Structures Nirvanix Global SDN < 2TB Amazon S3 USA < 10TB Amazon S3 Europe < 10TB Amazon CloudFront < 10 TB Mosso CloudFiles < STB Incomingcfau($/ GB) 0.18 0.1 0.1 N /A 0.08 Outgoing daca{SI GB) 0.18 0.17 0.17 0.17-0.21 0.22 Storage (S/GB/monrh) 0.25 0.15 0.18 N /A 0.15 Requests (S/1.000PUT/ POST/LIST) 0.00 0.01 0.012 N/A 0.02 Requests (VI0.000GET) 0.00 0.01 0.012 0.010-0.013 0.00
  43. 43. <> C H in e u Feature Comparison Nirvanix Global SDN Amazon S3 Amazon CloudFront Mosso CloudFiles SLA 99.9 99-99.9 99-99.9 99.9 Max File Size 256GB 5GB 5GB 5GB US PoP Yes Yes EU PoP Yes Asia PoP Yes Yes AUS PoP N o Yes Per file ACL Yes Yes Yes Yes A utom atic replication □f files Yes No Yes Yes Developer API's 1 W e b Services Yes Yes Yes Yes
  44. 44. SUSD/month h id t n ia n ▼ L m e * 5 > a v e <> c t n n e u Pricing comparison Outgoing TB Dala/month
  45. 45. Public App Clouds - Platform as a Service • Google AppEngine • Microsoft Azure Services Platform • Salesforce.com / Force.com • Mosso Cloud Sites • Heroku 52
  46. 46. Google AppEngine Python stack Java stack (J2EE 6,JDO,JPA,JSP) [new] App Engine serving architecture allows real time auto-scaling w ithout virtualization. • ...provided you use limited native APIs. • ...provided you use Google APIs. • URLFetch, Datastore, memcache, etc.
  47. 47. K4 ttn a ii ▼ L in e ± ^ a v e o c rim e n Google AppEngine - Free vs Billed Reaouica Free Default Quota Billing Enabled Q ua il Dally Limit Maximum Rale Dally Limit Maximum Rata Requests 1,300.000 requests 7,400 raqueilsftnnula 43.000.000 raquaata 30,000 ■equeaia/minula OctQOng Bandwidth la able mckjdes HTTPS) 10 gigabytes 56 megabytes/minute 10 ggabytaa tree; 1.046 gigabytes maximien 740 megabytes/minuta Incoming Bandwidth (a able, ndudes HTTPS) 10 gigabytes 56 megabytes/minute 10 gigabytes free: 1.046 gigabytes maximum 740 megabylaa/mnute CPU Time (b'able) 46 CPU-houra 15 CPU-mim/le/minuta 4G CPU-houra tree, 1.728 CPU-hOurl maximum 72 CPU-mirajla/mintrta Source: http://code.google.com /appengine/docs/quotas.htm l 54
  48. 48. ▼ uimova Google AppEngine - Billed • $0.10 per CPU core hour (actual CPU time an app uses to process requests and any Datastore usage). • $0.10 per GB bandwidth incoming, $ 0 .12 per GB bandwidth outgoing, (in/out data, data usage of URLFetch API and Email API). • $0.15 per GB o f data stored per month. • $0.0001 per email recipient for emails sent SS
  49. 49. khi tmaii ▼Lme *iave ocmoeu Azure Services Platform • Hosted .NET Stack (C# .VB.Net. ASRNET). • Java & Ruby SDK for .NET Services also available. • Windows Azure Fabric Controller: • Provides Scaling and reliability. • Manages memory resources and load balancing. • .NET Service Bus registers & connects applications together. • .NET Access Control identity providers, including enterprise directories and Windows LivelD. • .NETWorkflow allows construction and execution of workflow instances. a
  50. 50. Salesforce.com Force.com PaaS allows developers to create add-on functionality that integrates into main Salesforce C R M SaaS app. Hosted Apex/Visualforce app • A pex is a proprietary Java-like language. • Visialforce is a XM L-like syntax for building Uls in HTML, AJAX or Flex. AppExchange paid & free app directory.
  51. 51. wlmp • r • cmnuo Mosso Cloudsites • W indows and Linux stacks: • Linux - PHP 5, Perl, Python, MySQL 5 • W in - .Net 2.0, 3+3.5,ASP, MS SQL 2k8 • Runs application with very little (if any) modification (no custom APIs) • Transparent load balancing and autoscale. • Can run PHP,ASP, HTML, Python, and Perl pages concurrently from I site!
  52. 52. tmaii ▼Lme ♦ save <>ctnneu Heroku • A platform for instant deployment of Ruby/ Rails web apps. • Servers are invisibly managed by the platform, and are never exposed to you. • Apps are dispersed across different CPU cores, maximizing performance and minimizing contention. • Logic can route around failures. 59
  53. 53. Private / DYI Clouds • Many platforms exist to run your own Cloud: • OpenNebula • Eucalyptus • Nimbus / Globus Workspaces • Others ... OpenNebula.org r i j§r
  54. 54. Kgemail ▼ljkb *5>ave <>ctnneu OpenNebula • O pen-sourceVirtual Infrastructure management software. • Supports dynamic resizing, partitioning and scaling of resources • Supports Private / Public / Hybrid Cloud models: • Turn cluster into private cloud. • Can expose service to public. • Cloud plugins (EC2, G oG rid) enable hybrid model. • Offers XML-RPC W eb Services 61
  55. 55. Eucalyptus Open-source (BSD) software infrastructure for implementing "cloud computing" on clusters. Public o r Private Cloud Web Service is fully AWS API compliant • EC2,S3 and EBS are all emulated. • Enables a Hybrid approach - mix and match EC2 and Eucalyptus resources.
  56. 56. Nimbus / Globus • Open-source fram ework to turn your cluster into an laaS cloud. • Offers tw o Web Service interfaces: • Amazon EC2WSDLs. • Grid community WSRF. • Several test "Science Clouds” deployed: • U .of Chicago, Florida, Perdue & Marsarky U. «>
  57. 57. kj tmaii ▼ Lme ♦ 5>ave <> c m n e « Get your head in the Clouds Cloud Case Study - MetaCDN 64
  58. 58. tin aii ▼ likb *5>ave c m o e u M etaCDN : Enabling High Performance, Low Cost Content Storage and Delivery via the Cloud Dr.James Broberg (brobergi@csse.unimelb.edu.au) http://www.csse.unimelb.edu.au/~brobergj 0TMMftiunvfll MELBOURNE http://www.m etacdn.org m e taMHIOURNI Sc h o o l O f •R M 1 T ENGINE!RING am
  59. 59. Content Delivery Networks (C D N s) • W hat is a CDN? • Content Delivery Networks (CDNs) such as Akamai, Mirror Image and Limelight place web server clusters in numerous geographical locations to improve the responsiveness and locality of the content it hosts for end-users.
  60. 60. Existing C D N providers • Akamai is the clear leader in coverage and market share (approx. 80%), however... • Price is prohibitive fo r SME, N G O , Gov... • Anecdotally 2 - 15 times m ore expensive than Cloud Storage, and require 1-2 year com m itm ents and min. data use ( 10TB+) • Academic C D N s include Coral, Codeen, Globule, however... • N o SLA / QoS provided, only ‘best effort'
  61. 61. M ajor C D N pricing M ost m ajor C D N providers do not publish prices “ Average" prices from the 4-5 m ajor C D N s in the market: • 50TB: $0.40 - $0.50 per GB delivered • 100TB: $0.25 - $0.45 per GB delivered • 250TB: $ 0 .10 - $0.30 per GB delivered Inform ation taken from Dan Rayburn @ www.cdnpricing.com,StreamingMedia.com
  62. 62. t m a i i ▼ L m e ± 2s a v e o c m n e a Storage Clouds ‘Storage as a Service’ amazon M O S S O webservices' n lrv in ix Now! f— v - C O R A L Q Windows Azure >.>>>» G o o g l e " Y f t H O O f
  63. 63. ifluouyasns huf tm a ii ▼ Lin e * 5 > a v e » c m n e a Pricing comparison Oulqanq TB DaU/month
  64. 64. ▼ L m e * 5 ) a v e o r m n e a Introducing MetaCDN • W h a t if w e cou ld c re a te a lo w -c o s t, high p erfo rm a n c e o verlay C D N using these sto rag e clouds? • M e ta C D N harnesses th e d iversity o f m u ltip le storage cloud providers, offering a choice o f d e p lo y m e n t lo catio n , features and pricing. • M e ta C D N p rovides a u n ifo rm m e th o d / A P I fo r harnessing m ultiple sto rag e clouds. • M e ta C D N p rovides a single nam espace to c o v e r all su p p o rte d providers, m aking it sim ple to in te g ra te in to origin sites, and handles load balancing fo r end-users. • T h e M e ta C D N system offers c o n te n t c re a to rs (w h o are n o t necessarily p ro g ra m m e rs !) a trivial w ay to harness th e p o w e r o f m u ltip le cloud storage p ro vid ers via a w e b p o rta l
  65. 65. H ow M etaC D N w orks J < lS ]l to o lk it Java SUK O p an S o u tc e AmazenSSConnaelor Java s ijb f.leiiC D N era J jvj »!uD Mcf.iCOfJx’q____ S C P C o n n e c lo f J . I V . I :M llb F T P C o n n te lo r J i . ' j ilu L ’ Mi'l.iCOVctq____ N irv a ra i SDK Java SDK N r .n r.ix , In c NlrvinlrCnm eder Java >lvb Ate.'jCON^vg Microsoft Azira Sloiaga Samoa AzuroConnector Java stub M ct j CDfJ iv j U a u C D N ftlataC D N QoS U alaC D N M alaCDN M anagar M o n ito r A llo c a to r O alabasa Clouiff llosConnaelor Java y jb MolaCQNoro CoralCortnoctor Jav .1stub M olxCCN otq______ W&b Portal load R fdrfclor Wat Sen/tea Java (JSi-/EJB)ba«*<l poitsi Support HTTP POST Nmu*vviv*'modify dapkiymanl Handom radraclion Googtaphcal redaecbon Laad coal radaacbon s o a p w«b setwro HESTIui WubSarvca Provjuimmaic acca&s Mo <wo Cloud H a * CON Coral CON htelaCDN Amazon S3 & CloudFrom N iv a n a SON m
  66. 66. tm a ii ▼ LiKe ♦ .^ a v e <> c rn n e u H o w M e ta C D N works AmMorS3Conn»cten______ L-ciilot ;• :v*iio*J«f',am c, oca'.;o.v dole laFDU«i(lDJd«tnoma) aealoFilejUa foldainame. location, daio) aeataFileffiaUPL lotdBinama. location, dale) ranamoFie(lio«ame. nawnama. location) Itvowe FealuioNol SupportodExooplion aealaTormnl(lle) croaloTorr»nl((l«URL| daleloFiallils. location) IstFilesAndFoldarsO « ca acaptla n» FaaturaNotSupportadEacapUon FaaluraNotSiippoitedEi. ________ DtlauttCom tclor_______ DEPLOY USA DEPLOY EU DEPLOY ASIA DEPLOY AUS_________________ . itiaisf " 'rllr'iarnam loealon) deleloFolder(lcldemamB) creataFeaflile. lotdamame location, data) ciealeFiollikiURL. foidcmamo location, data) renameFle)lik)name. newnomo. location) crealflToirenl(file) crealeTorrenl(lileURL) dalateFiellto. location) llslFilasAndFoUorsO dalolaFeasAndFoldeiM)_________ _______N livinlxC cnnactor______ cwdieFoldertloldarnama. location) daletaFoldorfloldei nama] cranlaFie(Tile, loldemame location, data) croalaFio|lilflURL. Iold am arm location, data) renamaFlaflilanaine. rvawnama. location) ciealaTonant(llc) throws FenhveNolSLf] portedException cisoleTcnanlllioURL) throws Faal'jeNolSuppofledExcnpbon deleleFia)He. location) listFlasAndFoUorsQ deleleFlesAndFoldeisQ__________
  67. 67. M etaCDN Allocator • The M etaC D N A lloca to r allows users to deploy files either directly o r from a public origin URL, w ith the following options: • Maximise coverage and performance • Deploy content in specific locations • Cost optimised deploym ent • Q uality of Service (QoS) optim ised
  68. 68. M etaCDN Manager • The M etaC D N Manager ensures that: • All current deployments are meeting QoS targets (where applicable) • Replicas are removed when no longer required (minimising cost) • A users’ budget has not been exceeded, by tracking usage (i.e. storage/download)
  69. 69. M etaCDN Manager • The M etaC D N Manager ensures that: • All current deployments are meeting QoS targets (where applicable) • Replicas are removed when no longer required (minimising cost) • A users’ budget has not been exceeded, by tracking usage (i.e. storage/download)
  70. 70. M etaCDN QoS Monitor • The M etaC D N QoS M onitor: • Tracks the performance o f participating providers at all times • M onitors and records through pu t response tim e and uptime from a variety o f locations • Ensures that upstream providers are meeting their Service Level Agreements
  71. 71. email ▼ lin e ♦ >>dvp o i m n e n M etaC D N Database CON Credentials 1 CON Provider 1 X 1 has 1 1 M / w p i o y i t f V 0 : M MetaCDN Replica M M 1 Coverage locations 1 QoS Monitor M
  72. 72. M etaCDN Web Portal Developed using Java Enterprise and Java Server Faces (JSF) technologies JPA/MySQL back-end to store persistent data W eb portal acts as the e n try point to the system and application-level load balancer M ost suited fo r small o r ad-hoc deployments, and especially useful fo r less technically inclined content creators.
  73. 73. k J trnau l ik e ♦ ^ a v e o t m n e a m eta ■Ml LWIVlUin LM • R M IT UNIVEBSFTY Don’t have a MetaCDN account? ( Register ) Sign in Username: master Password: ........ ( L o g h ) AJI trademarks mentioned herein are the exclusive property of their respective owners. This project is supported by the: S u ttn h in I ■ in rn rn titl Iw ln la a i U m n h I o n
  74. 74. tmaii Lme ±5»ave o t m n e u m eta • KM 11 Register new MetaCDN a ecouit Username: (oattlogp PascMonl Preferred P>orders: FtJ Name Emal. Amaron S3 Ja* Biogo* jo *(|« o g i Sf MrvanxSDN 0 Motso Cloud Fles Q Weresdt Azure Scrag* Service Q SharedPnvde Host Hrvaiu SDN Amaze* S3 H ose Cb.H U‘ I Enter your Arrcuon S3 Credenbate AWS b e n Key AWS S*a*4 Kay Enact* O o rfro rr V ( Rapater ) CCancel j Ail freda mates menlanad hered are the eadtsrve pr(party olthe* rotpedMi (xvnors ___________________ The pmjaci nuppodad bylha ____________________________
  75. 75. ▼ L u te ♦>»d ve u t i m i e n m eta cyr~/ 1 -1 • KMITUNtvrasrrr Welcome to the MetaCDN Portal! Deploy content View existing contenl ( View existing replicas ' Edit Account Deployment Map ( MetaCDN Analytics ^ d Logout All trademarks mentioned herein are the exclusive propedy of Iheir respective owners. This project is supported by the: A tulraltan ( im m o t d il A iM u b n K m i r t i <
  76. 76. u tn iaii ▼ l i k e i ^ a v e o t m n e u flie ta - Till LMIVJUIIYQf ♦ RMITuwrvtasrTY Sideload Conlent: Choose URL: h1lpJ/plchlaifc.eomfmadi8ffrariteri(l'innagasSheedBr_loc Deployment region(s): M North America Asia fSf Europe Australasia Host Untit 3 0 /0 5 /2 0 0 9 n Deploy Caned At trademarks mentioned herein are the exclusive property of their respective owners This project is supported by the
  77. 77. ktftnum▼LiKe±:sdveocmnen
  78. 78. Xdtmaii▼liks±5>avec>cmneu £ • I f ii i 1i H | II i i) !»: J3 ® 111 !»■ Ill i ii11 ii i i i i I § f 1 1 6 s 1 1 1 i 9 l i Is * n i i * ! * » IB IB s a » » » § 2 2 2 2 *1 i i >1 i i « i IB IB IB IB IB • j t bl| if i • ii t i i I 3 3 3, 3 * s, * { i I 1 I i i i I I I I I ! □ | § 5 5 a a c a 3 i i I ! I I ! I i i l l i llii * J
  79. 79. tniau TLiKe ♦ ^ave <> c m o e u m e l a __________________ CMDeployment Map
  80. 80. MetaCDN Web Service • Makes all functionality available via Web Services (SOAP & REST/HTTP) • Web interface is useful for novices and for ad-hoc deployments, but doesn’t scale • Larger customers have 1,000’s - 10,000 - 100,000s of files that need deployment • Let them automate their deployment and management via Web Services! • Perfect for Mashup developers!!!
  81. 81. M e ta C D N Load Redirector W e have created a unified namespace to simply deployment, routing, management Currently, fie deploym ent results in m ultiple URLs, each mapping to a replica • http://m etacdn-eu-user.s3-am azonaw s.corn/m yfile.m p4 • http://m etacdn-us-users3.am azonaw s.com /m yfile.m p4 • http://nod e3.nirvanix.com /M etaCD N /user/m vfile .m p 4 Single namespace is created fo r fn e control • http://www.metacdn.org/FileMapper?itemid=2
  82. 82. l ik e ± . ia v e u E n iiie o MetaCDN Load Redirector (cont.) Actual load redirection logic depends on deployment option used by MetaCDN user: • Maximise coverage and performance? - Find closest physical replica • Deploy content in specific locations! - Find closest physical replica • Cost optimised deployment? - Find cheapest replica, minimises cost to maximise lifetime • Quality of Service (QoS) optimised? - Find best (historically) performing replica
  83. 83. i t t n a n L m e * 5>ave o c n i n e a M etaC D N Load R edirector (cont.) I i R o o K 't www.metec<*n.org | QNS^erwf | I MtUCDti qjlceav I t“ r- Rflha n IP ol dosflsl M el■CON galeway. www-nn.ma1icdn.org ^pro cm ^R aqua s( Q 'H TTP 302 HafVacI to h ip //m e tic tfi •ui-utam em * O ■m u a da m . compile neme pdi a a Resohe melatdrvus-username.eS a m a a in » rt com ' R a L i m p o l m e i ^ h ^ i ' U t a n i A f n i ^ 3 [ GEThtyi J i m lacdiM i-uum ena d am unniaa.com /N anana pdI i R ilcm rapteii • I A | I
  84. 84. Seconds b a tm a n ▼ L in e <>t in n e d Redirector Overhead 1 B 1 6 1.4 1.2 1 OB 0 6 0 4 0 2 0 0 5 10 15 20 25 HOW from USA S3 USA *— «— ' M M CON » i : « • J
  85. 85. Soeonds I tm a ii L m e * 5 > a v e <> c t n o e u Redirector Overhead from Australia0.9 0.65 N tva n ix SDN J 3 -------- MolaCDN 0.6 0.7S 0.7 0.6S 0 6 0.55 * ; i r * 10 15 Hour 20 25
  86. 86. Evaluating M etaCDN performance Ran tests over 24 hour period (mid-week) Downloading test replicas (IK B , 10MB) 30 times per hour, take average and conf. inter. • 10MB - Throughput, I KB - Response Time Ran test from 6 locations: M elbourne (AUS), Paris (FRA),Vienna (AUT), San Diego & N ew Jersey (USA). Seoul (KOR) Replicas located across US, EU,ASIA, AUS
  87. 87. Summary of Results - Throughput (KB/s) S3 US S3 EU S D N # 1 S D N # 2 SDN #3 SD N #4 Co ral Melbourne Australia 264.3 389.1 30 366.8 408.4 405.5 173.7 Paris France 703.1 2116 483.8 2948 416.8 1042 530.2 Vienna Austria 490.7 1347 288.4 2271 21 1 538.7 453.4 Seoul South Korea 312.8 376.1 466.5 41 1.8 2456 588.2 152 San Diego USA 1234 323.5 5946 380.1 506.1 820.4 338.5 New jersey 2381 1949 860.8 967.1 572.8 4230 636.4USA
  88. 88. V I M • Summary of results - Response Tim e (Sec) Vienna Austria Seoul South Korea San Diego USA S3 US S3 EU S D N * 1 S D N # 2 S D N #3 SD N #4 Co ral 1.378 1.458 0.663 0.703 1.195 0.816 5.452 0.533 0.2 0.538 0.099 1.078 0.316 3.1 1 0.723 0.442 0.585 0.099 1.088 0.406 3.171 1.135 1.21 0.856 0.896 1 0.848 3.318 0.232 0.455 0.23 0.361 0.775 0.319 4.655 0.532 0.491 0.621 0.475 1.263 0.516 1.916
  89. 89. Summary of results (cont) Clients benefited greatly from local replicas Results are consistent in terms of response time and throughput w ith previous studies of dedicated CDNs Back-end cloud providers have sufficient performance and reliability to be used to host replicas in the M etaCDN system Adding “ C D N " Cloud Providers (Amazon CloudFront, Mosso Cloud Files) will likely significantly improve results.
  90. 90. How can we make M etaCD N scale? Already grown from I gateway to m ultiple gateways: • I gateway in M elbourne,Australia (physical) • I gateway in Seattle,W A (EC2 "USA-East") • I gateway in Dublin, IRE (EC2,“ EU-W est” ) Deploy more gateways on-demand as required: • Based on locality of requests, add gateways • As demand decreases, remove gateways.
  91. 91. How can we make M etaCD N scale? Already grown from I gateway to m ultiple gateways: • I gateway in M elbourne,Australia (physical) • I gateway in Seattle,W A (EC2 “ USA-East") • I gateway in Dublin, IRE (EC2,“ EU-W est” ) Deploy more gateways on-demand as required: • Based on locality of requests, add gateways • As demand decreases, remove gateways.
  92. 92. How do we make M etaCDN scale? Use DNS-based redirection to closest gateway. • Dynamically update A -D N S entries using W eb Service API. Fill Cloud black holes w ith seamlessly integrated non-"C loud” storage: • Add shared / private host support. • FTP.WebDAV, SCP accessible.
  93. 93. M e ta C D N features in planning / development • Support as many providers as possible • W indows Azure Storage Service support will be finished very shortly. • Integrated Flash video and MP3 audio streaming with embeddable players for customers to place on their origin sites.
  94. 94. M e ta C D N features in planning / developm ent • Autonomic deployment management (expansion/contraction) based on demand • Autonomic deployment management (expansion/contraction) based on QoS • Security / ACL framework that spans all cloud storage providers • “ One time” MetaCDN URLs
  95. 95. Collaborations Always looking fo r people to collaborate on the project W o rk to be done on: • Load balancing / redirection algorithms • Intelligent Caching / replication algorithms • Security / Access C ontrol of content • Improving M etaC D N W eb Services Please contact me if you are interested...
  96. 96. ▼ Line i ^ave o rm n e a Publications • J. Broberg and Z.Tari. MetaCDN: Harnessing Storage Clouds for High Performance Content Delivery. In Proceedings ofThe Sixth International Conference on Service-Oriented Computing [Demonstration Paper] (ICSOC 2008). LNCS 5364. pp. 730-731. 2008. • J. Broberg. R. Buyya and Z.Tari. Creating a‘Cloud Storage' Mashup for High Performance, Low Cost Content Delivery. Second International Workshop on Web APIs and Services Mashups (Mashups'08), In Proceedings ofThe Sixth International Conference on Service-Oriented Computing Workshops. LNCS 5472, pp. 178-183, 2009. ■ J. Broberg. R. Buyya, and Z.Tari, MetaCDN: Harnessing'Scorage Clouds' for high performance content delivery. Journal of Network and Computer Applications (JNCA).To appear. 2009 • R. Buyya. C. S.Yeo, S.Venugopal, J. Broberg. and I. Brandic. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems.To appear, 2009.
  97. 97. ▼ L m e ± ^ a v e c> c n in e u Open Research Issues in Cloud Computing
  98. 98. Research Issues Interoperability Portability Reliability & Monitoring Data Security and Jurisdiction Innovative Cloud Pricing Models Innovative Service Models
  99. 99. Interoperability • Many ongoing “ open" standards efforts: • Cloud Computing Interoperability Forum • Open Grid Forum (OGF) Open Cloud Computing Interface W orking Group • DMTF Open Cloud Standards Incubator • GoGridAPI (CC licensed) • Sun Cloud API (CC licensed) • Amazon Web Services as “ de-facto" ▼ u t n in v n
  100. 100. Portability Amazon Machine Image (AMI) VMWare Virtual Machine Disk Format (VMDK) C itrix XenServer / Microsoft Hyper-V Virtual Hard Disk (VHD) Open Source Xen Virtual Block Device (VBD) - File o r LVM based
  101. 101. K fltrn a n ▼ L in e ♦ ia v e o tm n e a Portability - Solutions? • Virtual to Virtual (V2V): • VMware vCenter Converter • C itric XenConvert • DMTFVirtualization Management Initiative • Open Virtualization Format (OVF) • Initial support in XenServer /VM W are • Project Kensho (Xenserver/Citrix) • OVF Tool Technology Preview (VMWare)
  102. 102. Reliability & Monitoring • Outages happen! • Amazon AWS: 8hrs (7/08), 2hrs (2/08), 48hrs (10/07) • Google GMail - 2hrs (2/09) • Google - 1.5hrs (5/09) - “ An error in one of our systems caused us to direct some of our web traffic through Asia.”
  103. 103. Reliability & Monitoring • Full transparency is needed: • Cloudstatus.com (Hyperic) • http://trust.salesforce.com/ (Salesforce.com) • http://status.aws.amazon.com/ (Amazon)
  104. 104. i t u i a n L iK e * s a v e u c m n e i i Clo udStatusnm » ■ • N M VP® Otoifrt ov*ui« Ooudi Sc?) *koraoa W a < (O) A m a zo n W e b Services Health Sum m a ry '» •m dum ruv*ra huwi u a ji at<3v i Ua-1mnry l»w tairt d r v n t - , a.tjg»n Wcti - ■ i M t x m m t l i o m a i o r o « .i - a : « « ; « ^ a r H * u > . «-■■■,*. and r r « « n n « i » . *m<»e OtKJ«W vn (VJSJ O^j^Hitva » ( » ) F k itfia S « w i I K ) El H t i t f t w S f c V f l l i t u t t H t a - j v ; a ^ f i ™T - I Q ^or. c lorCKudA#^: UcA a ECJ S3 dtoratf Br IQ; Map 4 M i A -a* AH • r l r j . ■ i J r V * w A il « V M I Br«« «>ai | 9 /jto l • •IAO SQS SOB Ft/OA« r« a «rt] M M wreme • WNMI • 4«IM FPS
  105. 105. tmaii ▼ Lme ♦ 5>ave o t m o e a
  106. 106. tm a n ▼ l i k 8 *^>ave <> c m n e u H ay 0, 2009 Reocn tr stu t C um n i Status Details DBS © Amazon CkxcFrani Serve* a operating normaly. B © Amaior Elatte Compute Cloud (Ell) Serve* a opanbng normaly B © Amazon Eiaidc Compute Cloud (US) Serve* ■ oceiiunc normaly. B © Amazon ta m e MvHeduce Snvca a oparalmg normaly B © Amazon PlexMe Payments S e v a S tives a opennng normaly. □ © Amazon Merhnnf 1 Tiofc (Requestor) Sn-ve* a ooenieg normaly. 5) © Amazon Macharwai Ti*k (Wtorxer) Service is operating rormoay. B © Amazon Snrpia Queue Se-ve* Serve* aoperating normaly B © Amazon S rrptt Suraga Serve* (Ell) Swvoa is operating normaly B © Amazon Snrpa Stonge Samoa (US) Serves a opening normaly B © Amazon Sn-paOfl Serve* a opening normaly. EJ © AWS Conao* Serve* ti opening normaly. B © Samoa a opening normaly O Pe'tornanea a iu a i O Samoa fll|(i« l on I *-*n— U r n ' ~an»Q i I I I
  107. 107. k j tm a n T u n e ♦ s>ave o t n i n e a May 13. 2009 CtvTwit Blalua ^ Amazon CAxztfronl ^ A m u o r E ttd c Compuia Cloud (EU) ^ Amazon Elastic Compute Cloud (US) Q Amazon EuaUC U ip lW u ca Q Amazon Flam b* Paym aNi Sezvica Q Amazon M td a n a i Tum |Fk<*jaaie«] (3 Amazon Mechanical T irt (Worker) 42 Amazon Srrple Ooaua Saova ( 2 Amazon Sanpla Swaoe Sennet (ELI) Q Amazon Sarola Smaoa Service (US)
  108. 108. Raoorl an Issue Oatalla nsa Sannoa la 0pernmg normaly B Servrce la operating normaly a Elevalad API Error Ru m 1a m si Q I 14 PM PC r We are currency im eM qalng released API error rMM tor Ihe DeacnbeVoAmea and DaacrfbaSnacafioa eato It tie US-EAST-l reocn 6 A3 PM POT We are currency nvattgjam g hqh API arro iiu e la ihe EBS API rn ttu d cute (CraewVofjr e DitcnbaVbAimaa C-eelaSnibehcl Deacr beSnaperoia OalalaSnapanaj r the US-EASM region fl 22 PM (>0t Wa are connung to invealgato Mended EBS API error r d t l tf *M US-EAST-1 Regen 10Ofl PM POT EC2 EBS API error rues have racovuad We are nveeugaena poianau m pad u esaang ESS vohxrrai II OB PW l'U I EC2 EBS i* curranOr capacity eoneeanad r a tingle AvMabray Zona m da U S -EAST-1 Region Samoa U npnsing normaly B Sarvca la Dpaiaing norma»y a Serves la opaiaing normaly a Sarvca la operaing normaly a Serves la opaiung normaly a Samoa M operating nam ely a Samoa la apamng normaly a 112
  109. 109. h id tt iia ii ▼ Lm e ± ^ a v e o t m n e u Status History Amazon Wnh Servcas keeos ■ running log of al servce nanupinns ra t we puHah in the ldbte below lor the prm m fl 39 day*. Mouse m e any ol me status cons below to see e detailed modem report (dek an the con 10 perosi tie poptc) O ck on tie enow busons at r e too of r e tame to move forward and bedewaids thiou^i the ralenrlar »mA p r il Apr 20 Apr I t Apr I t Apr 17 Apr I t Apr IS Amazon Cloud Fiord 0 0 0 0 0 0 0 Amazon Elaallc Computa Cloud |EU) o 0 o 0 0 0 0 Amazon Elaatle Computa Cloud IUS) 0 0 0. 0 0 0 0 Amazon Elaallc MapRaduca 0 0 0 0 0 0 0 Amazon F taatjli Paym erle Service 0 0 0 0 0 0 0 Amazon Mechanical Turk (Requaaur) 0 0 0 0 0 0 0 Amazon Mechanical Turk (Worker] o 0 0 0 O 0 0 Amazon Simple Queue Service 0 0 0 0 0 e 0 Amazon Simple Storage Service (EU) 0 0 0 0 0 0 0 Amazon Simple Storage Service |UB) 0 0 0 0 0 0 0 Amazon SimpleDB o 0 0 O O 0 0 AWS Management Conaole 0 0 0 0 0 0 0 113
  110. 110. W hy is monitoring important? • “ To be eligible, the credit request must (i) include your account number in the subject of the e-mail message (ii) include, in the body o f the e-mail, the dates and times of each incident o f non-zero E rror Rates that you claim to have experienced; (iii) include your server request logs that docum ent the errors and corroborate your claimed outage and (iv) be received by us w ithin ten (10) business days after the end o f the billing cycle in which the errors occurred.” - SLA fo r S3
  111. 111. W hy is monitoring important? • “ To receive a credit, you must contact your The Rackspace Cloud account team within th irty (30) days follow ing the end of the downtim e. You must show that your use of the Cloud Servers was adversely affected in some way as a result o f the dow ntim e to be eligible fo r the credit.” - SLA fo r Mosso Cloud Servers
  112. 112. W hy is monitoring important? “ Custom er must open a support case (a "Case") during the Failure in question. Custom er w ill open all Cases through the Custom er Portal. In opening a Case, Custom er w ill provide complete inform ation regarding the nature of the problem, including any inform ation reasonably necessary fo r diagnosis and correction, by follow ing the Case opening procedures at the Custom er Portal. [...]” - SLA fo r G oG rid
  113. 113. Why is monitoring important? Service credits are not automatic! Requires immediate user action: • Claim within certain time period • Note specific dates and times of outage • Provide proof (i.e. logs) of outage Automated monitoring & alert services are invaluable in this environment.
  114. 114. Data Security and Jurisdiction in the Cloud • Cloud Servers abstract w here processing occurs and where your data is stored. • Software/data are subject to laws & regs. of where they are executed & stored. • Consider: • Digital Millennium C op yrig h tA ct (D M C A ) • Cryptography Export laws • O th e r regs (SEC, Sarbanes-Oxley, HIPPA)
  115. 115. Innovative Cloud Pricing Models • Pricing fo r Cloud services is very static • Lets take some cues from traditional utilities: • Peak / O ff Peak pricing. • Spreads demand, making capacity planning m ore predictable fo r providers. • Flexible Cloud Consumers can save money.
  116. 116. Innovative Service Models for Clouds. Create fram ew ork fo r Cloud Consumers to be Cloud Providers. Again, cues from utilities like power: • End-users & businesses can sell back electricity generated by solar energy to grid • Enable sell back o f under-utilised resources using same tech. cloud providers' use! W in-w in situation as companies could resell these resources and expand their footp rint.
  117. 117. Introduction to Cloud Computing • W hat we covered: • Part I - An Introduction to Cloud Computing. • Part II - “ Get your head in the Clouds” - MetaCDN Cloud Case Study. • Part III - Open Research Issues in Cloud Computing. ▼ i x m o v a 122
  118. 118. I mI E m a il ▼ L iK e * ^ a v e c i n o e a Thanks Any questions?

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