SAS Visual Analytics for the Three Cs: Cloud, Consumerization, and Collaboration

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1 ABSTRACT Paper SAS SAS Visual Analytics fr the Three Cs: Clud, Cnsumerizatin, and Cllabratin Christpher Redpath and Nichlas Eayrs, SAS Institute Inc., Cary, NC SAS Visual Analytics delivers the pwer f apprachable in-memry analytics in an intuitive web interface. The scalable technlgy behind SAS Visual Analytics shuld nt just benefit the analyst r data scientist in yur rganizatin, but, indeed, everyne regardless f their analytical backgrunds. This paper utlines a framewrk fr the creatin f a clud deplyment f SAS Visual Analytics using the SAS 9.4 platfrm. Based n prven best practices and existing custmer implementatins, the paper fcuses n architecture, prcesses, and design fr reliability and scalable multi-tenancy. The framewrk allws yur rganizatin t mve away frm the departmental view f the wrld and ffer analytical capabilities fr cnsumerizatin and cllabratin acrss the enterprise. INTRODUCTION The current ecnmic climate dictates that yu need t be swift and agile in yur respnse t ever-increasing custmer expectatins and cmpetitin. This requires yu t maximize every cmpetitive advantage that yu can. Yur data is just that (i.e., a free-t-use resurce and key differentiatr prmising unrivaled insight int yur business and its imprvement). Realizing the true ptential f yur data is ften challenging because f the lack f expertise r capability t access, explre, and derive insight frm the data itself, as well as t share insight fr multi-disciplinary cllabratin. SAS Visual Analytics addresses this very challenge by empwering yu with selfservice advanced analytics capabilities geared fr driving cllabratin and infrmatin sharing via a flexible deplyment mdel that leverages the clud. CLOUD COMPUTING CHARACTERISTICS, SERVICE, AND DEPLOYMENT MODELS Fr the purpse f this paper, here is the Natinal Institute f Standards and Technlgy (NIST) definitin f clud cmputing: Clud cmputing is a mdel fr enabling ubiquitus, cnvenient, n-demand netwrk access t a shared pl f cnfigurable cmputing resurces (e.g., netwrks, servers, strage, applicatins, and services) that can be rapidly prvisined and released with minimal management effrt r service prvider interactin. [1] CLOUD COMPUTING CHARACTERISTICS The NIST definitin defines five essential characteristics f the clud cmputing mdel: 1. On-demand self-service cnsumers are able t prvisin sftware effectively and efficiently. 2. Brad netwrk access cnsumers can access sftware capabilities ver the netwrk, leveraging standards-based cnnectivity that supprts a diverse set f clients (i.e., thin r thick (e.g., tablets, mbile phnes, laptps, r wrkstatins)). 3. Resurce pling cnsumers share resurces that serve ther cnsumers in a secure mdel that allws fr ecnmies f scale. 4. Rapid elasticity cnsumers are able t scale resurces as needed. 5. Measured service cnsumers can leverage metered sftware capabilities (i.e., capacity-based, userbased, and value-based pricing). CLOUD COMPUTING SERVICE MODELS The NIST definitin defines three clud cmputing service mdels: 1. SaaS cnsumers are able t access sftware capabilities ver the netwrk, typically leveraging nthing mre than a thin client (i.e., a web brwser). The cnsumer des nt manage r cntrl the underlying clud infrastructure r sftware cnfiguratin, but is able t custmize user-specific settings within the sftware. 2. PaaS cnsumers are able t deply self-develped r acquired sftware using APIs, services, and tls supprted by the clud service prvider (CSP). The cnsumer des nt manage r cntrl the underlying clud infrastructure, but is typically able t custmize sftware deplyment and cnfiguratin settings. 3. IaaS cnsumers are able t prvisin prcessing, strage, netwrks, and ther fundamental cmputing resurces fr the develpment and deplyment f sftware. The cnsumer des nt manage r cntrl the

2 underlying clud infrastructure, but is typically able t custmize the perating system, deplyed sftware, and the netwrk cnfiguratin. CLOUD COMPUTING DEPLOYMENT MODELS The NIST definitin defines fur clud cmputing deplyment mdels: 1. Private clud clud infrastructure prvisined fr the exclusive use f a single rganizatin encmpassing multiple cnsumers. It can be wned, managed, and perated by the rganizatin, a third party, r sme cmbinatin, and it can reside n r ff premises. 2. Cmmunity clud clud infrastructure prvisined fr the exclusive use f a cmmunity f cnsumers frm rganizatins that have shared cncerns (e.g., regulatry cmpliance, security plicies, etc.). It can be wned, managed, and perated by ne r mre rganizatins within the cmmunity, a third party, r sme cmbinatin, and it can reside n r ff premises. 3. Public clud clud infrastructure prvisined fr use by the public. It can be wned, managed, and perated by a business, academic r gvernment rganizatin, r sme cmbinatin, and it resides n the premises f the CSP. 4. Hybrid clud the calescence f the clud infrastructure frm tw r mre distinct clud deplyment mdels that remain unique entities fr the purpse f data and applicatin prtability (e.g., clud bursting). THE SAS CLOUD The SAS Clud is a hsted, private clud that prvides rapid access t SAS sftware and an easy way t manage yur SAS envirnments. The SAS Clud characteristics align with the NIST definitin. In additin, it supprts tw f the three NIST clud cmputing service mdels, SaaS and PaaS. The IaaS clud cmputing service mdel is currently nt supprted by SAS because f ur cntinued fcus n being a sftware prvider as ppsed t an infrastructure prvider. Within the SAS Clud, yur rganizatin is licensed t use ne r mre dedicated SAS envirnments. A SAS envirnment is a cmplete, self-cntained, fully functining deplyment f SAS applicatins. Envirnments in the SAS Clud are deplyed using virtualizatin technlgy, which makes the envirnments easy t create and manage. A typical envirnment includes the fllwing: data strage that can be ppulated with yur rganizatin s data virtualized sftware, servers, and services that perfrm varius types f prcessing virtualized client applicatins thrugh which users can access SAS functinality administrative interfaces fr uplading data, rganizing SAS cntent, and managing permissins SAS CLOUD TOOLS AND TECHNOLOGIES The SAS Clud relies n virtualizatin technlgy t ease the deplyment and maintenance f envirnments. The essential tls and technlgies includes the fllwing: SAS Virtual Applicatins (vapps) a cllectin f SAS sftware, third-party cmpnents, and cnfiguratin settings that are packaged t prvide a ready-t-run SAS applicatin envirnment. The vapp is deplyed in a virtualizatin envirnment and external cnnectivity is established accrdingly (e.g., security, mnitring, and data strage). SAS Visual Analytics is the first SAS slutin t be ffered as a vapp.

3 Figure 1. Cmpnents f a SAS vapp SAS App Central a rle-based web prtal fr managing yur SAS Clud accunt, applicatins, users, and envirnments and fr uplading yur data. This is the default landing page fr SAS Visual Analytics fr SAS Clud. Figure 2. SAS App Central Initial Screen SAS App Engine a distributed technlgy that enables the assembly, delivery, and maintenance f SAS vapps thrugh SAS App Central. This facilitates the autmated prvisining f SAS Visual Analytics fr SAS Clud as well as prviding push ntificatins f the latest SAS sftware updates fr yu t selectively chse.

4 SAS VISUAL ANALYTICS CLOUD DEPLOYMENT MODELS There are several deplyment ptins fr SAS Visual Analytics. Whether yu prefer t deply the slutin n-site using yur wn hardware r via private cluds, public cluds, r the SAS Clud envirnment, we have a deplyment mdel t fit yur business. SAS VISUAL ANALYTICS FOR SAS CLOUD SAS Visual Analytics fr SAS Clud is the perfect chice if yu d nt have the time r resurces t supprt a traditinal, n-site implementatin. It prvides an affrdable license mdel using predefined, nn-distributed SAS Visual Analytics envirnment sizes, als knwn as team sizes, within the SAS Clud. The fllwing is included with each license: Clud infrastructure (sftware and hardware) Express Supprt Service, which cnsists f lading up t three data tables Standard SAS Technical Supprt services Access t nline dcumentatin Access t SAS Visual Analytics Vide Library The varius team sizes are summarized in the fllwing table: Small Medium Large Ttal Named Users (Unique user IDs) Data Capacity (GB) (Data stred n disk) Usable Strage (GB) (Data that can be laded t memry) Maximum Table Size (GB) (Maximum size f single table) Table 1. Team Sizes fr SAS Visual Analytics fr SAS Clud With this deplyment mdel, yu simply need t liaise with yur lcal SAS representative t prcure a SAS Visual Analytics fr SAS Clud envirnment and accept the assciated electrnic click-thrugh legal agreement. Yur envirnment will be autmatically prvisined fr use. Figure 3. High Level Prcess fr Prvisining SAS Visual Analytics in the SAS Clud All that is needed frm this pint n is Internet cnnectivity, a supprted web brwser [2], yur data, and a business prblem t explre. Typical tasks t cnfigure r custmize yur SAS Visual Analytics fr SAS Clud are as fllws: Add additinal users r administratrs up t the maximum specified by yur team size. Assign users t predefined SAS Visual Analytics fr SAS Clud rles as fllws:

5 Data administratr Data administratrs have the ability t lad data int the SAS Visual Analytics envirnment using the SAS vapp Data Manager applicatin. SAS administratr SAS administratrs have the ability t access administrative applicatins, such as SAS Management Cnsle and SAS vapp Ledger. SAS user SAS users have the ability t access SAS applicatins as regular users. Accunt administratr Accunt administratrs have the ability t maintain the custmer's accunt, including adding users, prviding users access t applicatins, and assigning users t rles. The accunt administratr is als able t manage envirnments. Prepare and lad data using either the SAS Visual Data Builder r the SAS vapp Data Manager applicatin. Fr mre infrmatin, see the SAS Visual Analytics fr the SAS Clud: Quick-Start Guide. [3] Cnfigure SAS Mbile BI fr ipad r Andrid-based tablets t access yur SAS Visual Analytics reprts. Fr mre infrmatin, see the SAS Visual Analytics fr the SAS Clud: Quick-Start Guide. Figure 4. SAS Mbile BI Cnnectin t the SAS Clud SAS Visual Analytics fr SAS Clud is available in the United States, Eurpe, the Middle East, and Africa (EMEA).

6 Figure 5. SAS Clud Lcatins SAS VISUAL ANALYTICS FOR PUBLIC CLOUD SAS Visual Analytics fr Public Clud currently supprts SAS Visual Analytics distributed and nn-distributed deplyments t Amazn Web Services (AWS) nly (i.e., Amazn Elastic Cmpute Clud (EC2) and Amazn Virtual Private Clud (VPC). Althugh EC2-Classic is supprted as a deplyment mdel, it is recmmended that custmers use VPC, which prvides increased islatin, cntrl, custmizatin, and security thrugh a virtual private clud envirnment within Amazn s public clud infrastructure. Different frm the SAS Clud mdel, with this apprach, SAS Visual Analytics is installed n Amazn s public clud infrastructure using the same prcess as the n-premises deplyment f SAS. Hwever, there are cnfiguratin and integratin differences between the traditinal n-premises deplyment f SAS and Amazn s deplyment f SAS, which are highlighted belw. With the public clud deplyment mdel, it is strngly recmmended that yu develp a SAS Visual Analytics fr Public Clud architecture custmized fr yur business with supprt frm SAS Prfessinal Services. Once the target architecture fr SAS Visual Analytics is defined, yu need t cmplete the fllwing steps frm yur AWS Management Cnsle: 1. Chse AMI 2. Chse Instance Type 3. Cnfigure Instance Details 4. Add Strage 5. Tag Instance 6. Cnfigure Security Grup 7. Review Instance Launch Figure 6. Steps fr Amazn Deplyment f SAS Visual Analytics 1. Chse AMI chse an Amazn Machine Image (AMI) with a virtualizatin type equal t the hardware virtual machine (HVM). This is the nly supprted ptin fr cr1.8xlarge instances. (See step 2 fr mre infrmatin.) In additin, yu must select a supprted perating system fr yur SAS Visual Analytics distributed r nn-distributed deplyment as fllws: SAS Visual Analytics Distributed: Red Hat Enterprise Linux 6 Suse Linux Enterprise Server 11 SAS Visual Analytics Nn-Distributed: Red Hat Enterprise Linux 6 Suse Linux Enterprise Server 11 Windws Server 2008 R2 (Enterprise r Datacenter) Windws Server 2012 (Standard r Datacenter)

7 2. Chse Instance Type Fr nw, the nly supprted instance type fr SAS Visual Analytics fr Public Clud is cr1.8xlarge. Size ECUs vcpus Memry (GiB) Instance Strage (GiB) EBS-Optimized Available cr1.8xlarge x 120 (SSD) 10 GB Table 2. Amazn Instance Specificatins Netwrk Perfrmance Nte: The cr1.8xlarge instance type is currently available nly within the fllwing regins (as f this writing): US-East/US Standard (Virginia) US-West-2 (Oregn) EU (Ireland) Asia Pacific (Japan) Asia Pacific (Tky) 3. Cnfigure Instance Details cnfigure the instances t meet yur business needs as fllws: Select the Number f Instances that yu intend t launch fr either a SAS Visual Analytics distributed r nn-distributed deplyment (i.e., yu can launch mre than ne instance at a time). Select the Purchasing Optin fr instances (e.g., spt r n-demand). Select yur preferred Netwrk t launch instances either int EC2-Classic r VPC. Select yur preferred Availability Zne t specify a distinct lcatin within a regin. Select a Placement Grup t specify a lgical gruping fr yur instances with lw-latency, full bisectin, 10-GBps cnnectivity between them. Select an IAM Rle, allwing yu t reuse yur existing Amazn credentials fr the launch f yur instances. Select yur preferred Shutdwn Behavir. Instances can either be stpped r terminated at shutdwn. Enable terminatin prtectin t prtect against accidental terminatin. Enable Mnitring t allw detailed mnitring via CludWatch (additinal charges apply). 4. Add Strage enables yu t attach additinal Elastic Blck Stre (EBS) vlumes and instance stre vlumes t yur instance r t edit the settings f the rt vlume. If yu are uncertain abut sizing, cntact yur lcal SAS representative t arrange a SAS sizing exercise fr yur specific wrklad requirements. 5. Tag Instance enables yu t categrize yur instances using key and value pairs. This can be an invaluable ptin fr ease f management. Fr mre infrmatin abut the basics f tagging, restrictins, and cnstraints, see Tagging Yur Amazn EC2 Resurces. [4] 6. Cnfigure Security Grup security grups need t be mdified t allw traffic t flw between hsts and t ensure that yu d nt expse yur envirnment unnecessarily t the Internet, which will pse security risks unless mitigatin and cnsideratin has been given t hardening yur envirnment. 7. Review Instance Launch review yur instance launch details befre launching and take nte f any warnings that might include security r pricing specifics. Once cnnectivity t the instances is validated using yur Amazn credentials, yu must cmplete all f the necessary prerequisite steps frm the SAS Visual Analytics deplyment and administratin dcumentatin. [5] If all prerequisites are met, then the deplyment f SAS Visual Analytics can be cmpleted using the instructins in the deplyment and administratin dcumentatin. Additinal public clud deplyment mdel cnsideratins are: Create a DNS Alias with a CNAME (Cannical Name) recrd in DNS fr each hst, pinting t the Elastic IP address, and use that as the fixed hst name. Expsing yur SAS Visual Analytics envirnment t the Internet can be risky unless mitigatin and cnsideratin has been given t hardening yur envirnment. If in dubt, please cntact yur lcal SAS representative t arrange fr supprt frm SAS Prfessinal Services. The time and cst t uplad data need t be carefully cnsidered in such a deplyment.

8 Fr mre details, cnsult yur lcal SAS Prfessinal Services befre embarking n a SAS Visual Analytics fr Public Clud deplyment. SAS VISUAL ANALYTICS FOR PRIVATE CLOUD SAS Visual Analytics fr Private Clud supprts SAS Visual Analytics distributed and nn-distributed deplyments in virtualized envirnments using the VMware vclud Suite, which prvides an pen and mdular architecture fr the develpment f public r private cluds. The VMware vclud Suite includes the fllwing technlgies: VMware vclud Directr and API rchestrates the prvisining f virtual data centres VMware vsphere prvides the virtualisatin platfrm fr the building f yur clud infrastructure VMware vclud Netwrking and Security prvides netwrking and security capabilities fr yur clud VMware vcenter Operatins Management Suite prvides insights int the perfrmance, capacity and health f yur clud infrastructure VMware vclud Applicatin Directr allws yu t build deplyment blue prints fr autmated applicatin deplyment using a DevOps mdel VMware vcenter Orchestratr allws yu t design and autmate cmplex wrkflws VMware vclud Cnnectr - links yur public and private cluds s that yu can easily manage them as a hybrid clud As yu can see by the extensive virtualizatin capabilities available t yu, yu have full cntrl ver the resurces within yur private clud and the prvisining at the mst granular level pssible. Therefre, careful cnsideratin must be given t mitigate resurce cntentin and t achieve ptimal perfrmance fr SAS Visual Analytics. SAS strngly recmmends leveraging the VMware vclud Architecture Tlkit (vcat) [6] bdy f knwledge t develp an apprpriate architecture fr yur specific needs. Figure 7. VMware vcat 5 Steps Once yur virtual machine instances are deplyed and cnnectivity t the instances is validated using yur supplied credentials, yu must cmplete all f the necessary prerequisite steps frm the SAS Visual Analytics deplyment and administratin dcumentatin. If all prerequisites are met, then the deplyment f SAS Visual Analytics can be cmpleted using the instructins in the deplyment and administratin dcumentatin. Additinal private clud deplyment cnsideratins are: VMware vsphere cnfiguratin maximums must be cnsidered. The fllwing cnfiguratin maximums are relevant t SAS Visual Analytics fr vsphere 5.5: Item Maximum number f virtual CPUs per virtual machine (Virtual SMP) 64 RAM per virtual machine 1TB Table 3. Virtual Machine Maximums Maximum

9 Item Maximum number f virtual CPUs per hst 4096 Maximum number f virtual machines per hst 512 Virtual CPUs per cre 32 Table 4. Cmpute Maximums Maximum Item Vlume size (cmmn t all VMFS versins) VMFS3 file size (default blck size = 8 MB) VMFS5 file size (default blck size = 1 MB) Table 5. Strage Maximums Maximum 64TB 2TB minus 512 bytes 62TB RAM per hst Table 6. Memry Maximums Item 4TB Maximum In the BIOS: Enable hardware-assisted CPU virtualizatin (i.e., either Intel (VT-x) r AMD-V). Enable hyperthreading. Enable Turb Bst, if supprted. Disable C1E and ther C-states in BIOS. D nt ver-prvisin resurces. Avid thin prvisining f disks, default t eager zered. Enable jumb frames fr netwrking. Use VMXNET3 virtual netwrk adapters. Set the ESXi pwer plicy t high r maximum perfrmance. Install the latest versins f VMware tls in the guest perating system. Prperly size strage envirnments t accmmdate wrst-case scenaris (such as maximum required MB per secnd I/O thrughput fr a virtual hst). Design the system t incrprate extra resurces t vercme virtualizatin verhead. Use VMware mnitring tls t help diagnse perfrmance issues at the virtual hst level. If yu are using Red Hat Enterprise Linux as the guest perating system, perfrm the fllwing steps: Set the Red Hat Enterprise Linux I/O elevatr t deadline. I/O barriers can be safely disabled with enterprise strage whse cache cntrller RAM is battery backed up. (Yu can switch the I/O elevatr t deadline using the tuned tl.) Enable the perfrmance CPUfreq gvernr. [7] Disable transparent huge pages because large file I/O might perfrm better with this feature disabled. This can be dne with the fllwing cmmand: # ech never > /sys/kernel/mm/redhat_transparent_hugepage/enabled Nte: The tuned tl enables this feature by default fr all prfiles, s anytime a tuned prfile is enabled, whether autmatically at start up via an init.d service r manually at the cmmand line, this feature will be re-enabled. Fr mre infrmatin, cnsult yur lcal SAS Prfessinal Services befre embarking n a SAS Visual Analytics fr Private Clud deplyment.

10 CONSUMERIZATION Cnsumerizatin is the emergence f design patterns arund the cnsumer, nt the rganizatin. Hwever, because f trends such as clud cmputing, app-ificatin, and mbility, it is having a prfund impact n the rganizatin. Organizatins must embrace cnsumerizatin by empwering their users t be prductive with persnal devices r risk users seeking access t n-demand self-service sftware capabilities utside f the rganizatin. These are ften in the frm f a persnal clud cnnected t a persnal device, typically a tablet. With SAS Visual Analytics, yu are able t supprt the bradest range f cnsumer devices with SAS Mbile BI client supprt fr ipad and Andrid tablets. SAS Mbile BI client supprt includes: ios Andrid COLLABORATION SAS Mbile BI fr ipad is a free applicatin fr ios available in the itunes App Stre. Platfrm r OS: Apple ios 6.0 and abve Devices: ipad 2, 3, 4, and Mini SAS Mbile BI fr Andrid is a free applicatin fr Andrid available frm Ggle Play. Platfrm/OS: Andrid 4.1 and abve Devices: Andrid tablets 10.1 ANALYTICS CENTER OF EXCELLENCE (COE) T ensure the cntinued realizatin f value frm yur investment in SAS Visual Analytics, it is imperative that yur fcus is nt nly n the technlgy cnsideratins, but, mre imprtantly, n the peple r cnsumers. A key enabler in achieving user adptin is t enable individuals t feel empwered and free t cllabrate n a shared analytic visin supprted by yur executives. SAS can supprt yu in establishing a business analytics center f excellence (CE) t achieve this. [8] MULTI-TENANCY IN A PRIVATE CLOUD CUSTOMER SCENARIO The fllwing scenari is based n a multi-tenanted private clud deplyment f SAS Visual Analytics. This deplyment is built in distributed mde with Hadp as the c-lcated data prvider. The prcesses adpted can be used as a guide t achieve a similar lightweight framewrk t handle multiple tenants. Hwever, it is nly ne example f a pssible framewrk t meet specific requirements. At a high level, the main requirements are: A centrally IT-prvisined and managed private clud platfrm that ffers SAS Visual Analytics as a SaaS t the business. This includes SAS Mbile BI access frm tablets. The platfrm has t be scalable, starting with a smaller cluster and grwing t meet the business demand as mre tenants cme n bard. Where required by the tenant, there needs t be end-t-end data segregatin frm ther tenants. There is supprt fr sharing cntent t prmte cllabratin between tenants, especially tenants belnging t the same parent business unit. There is supprt f an apprpriate chargeback mdel t fairly bill-back tenants fr usage and t drive the investment required t maintain and grw the platfrm. IT manages batch lading f data int the envirnment frm varius surce systems. The business is able t interactively lad its wn data int SAS Visual Analytics. There is supprt fr scripting t manage the bulk f the technical nbarding prcess fr new tenants. There is supprt fr a lightweight authenticatin framewrk with minimal maintenance. MULTI-TENANCY Fr the purpse f the scenari, we used the fllwing definitin f multi-tenancy prvided by Frrester [9]:

11 Multi-tenancy defines IT architectures that let multiple custmers (tenants) share the same applicatins and/r cmpute resurces with security, reliability, and cnsistent perfrmance. In line with this definitin, the SAS Visual Analytics platfrm prvides shared applicatin sftware and cmpute resurces fr use by the tenants. Advantages f the multi-tenancy apprach fr SAS Visual Analytics include: A centrally managed platfrm under the supervisin f a cre nucleus f emplyees. It assures lwer csts, a higher quality f SAS business services, and a better match between availability and resurces. The average cst fr the develpment f SAS Visual Analytics services prvided by central IT is lwer than individually deplyed SAS Visual Analytics envirnments. Business units can get started mre quickly because they d nt need t build and fund an entire new platfrm. Csts are lwered by using centrally lcated and maintained infrastructure and sftware t reduce up-frnt setup and nging perating csts fr each participating tenant. Higher utilizatin f the hardware results directly in lwer infrastructure csts because hardware is never idle. Building a platfrm based n standard, mdular, and reusable cmpnents prvides the capability t leverage effectiveness and efficiency by sharing best practices in terms f cncepts, applicatins, infrastructure, and services amng all tenants. Users can benefit frm the large capacity f a shared envirnment. Fr SAS Visual Analytics, this means that mre data can be laded int memry, larger data sets can be analyzed, and mre cmputing resurces are available t prvide faster results. Having a single, large enterprise infrastructure shared amng many tenants means shared benefits fr all. Sftware upgrades and patches can be applied quicker and with less effrt because there is nly ne instance f the sftware t update (as ppsed t sharing infrastructure instead f sharing the applicatin). SAS LASR ANALYTIC SERVER AS THE COMPUTATIONAL PLATFORM There are imprtant differences between building a multi-tenanted envirnment fr traditinal SAS cde executin (i.e., a SAS grid-based envirnment) and an envirnment fr SAS Visual Analytics [10] [11]. The primary difference is user activity within SAS Visual Analytics web clients includes interacting with and launching data in the SAS LASR Analytic Server. This is cntrary t the traditinal SAS grid where the SAS Wrkspace Server is the cmputatinal layer fr all user requests. Therefre, the shared cmputatinal platfrm fr users in a SAS Visual Analytics envirnment is the SAS LASR Analytic Server cluster. The standard flavr f the SAS LASR Analytic Server in a distributed envirnment supprts the Hadp Distributed File System (HDFS) fr c-lcated strage f data with replicatin and failver capabilities. The smallest cnfiguratin f the SAS LASR Analytic Server in distributed frm cntains fur ndes. The availability f lcal strage n the ndes is critical fr string large data sets in distributed frm in HDFS. The SAS LASR Analytic Server cluster cnsists f the fllwing cmpnents: A LASR rt nde. The server that cntains the LASR rt nde is als the Hadp NameNde when Hadp is yur c-lcated data prvider. LASR wrker ndes (all f the cmputatinal ndes that will hld data). Each machine that cntains a wrker nde is als an HDFS data nde when Hadp is yur c-lcated data prvider. The tplgy fr SAS Visual Analytics encmpasses the client tier, middle tier, and server tier. These tiers interact with the SAS LASR Analytic Server t enable client access t data fr perfrming analytic tasks. Any actins initiated by clients are channeled by the middle tier and the server tier befre they are sent t the SAS LASR Analytic Server. In a distributed envirnment, the LASR rt nde receives the prcessing actin request and sends it t the LASR wrker ndes. Each LASR wrker nde prcesses the actin in parallel n its prtin f the data befre sending the results back t the LASR rt nde fr the final aggregatin t bring all the wrker nde results tgether. The final result ges back t the client via the middle tier. BUSINESS UNITS AND BUSINESS DATA AREAS In this scenari, fr the multi-tenanted platfrm, the tenants can be rganized as belnging t a tw-level hierarchy: Business unit: Within the scpe f the scenari, this is defined as a tp-level divisin within an rganizatin (fr example, Retail, Marketing, Finance etc.). Business data area: Each business unit (BU) can have a number f distinct areas f data that need t be physically segregated, pssibly crrespnding t functins within a BU. Fr example, Retail can have separate data areas fr Retail Marketing and Retail Brand. These are referred t as business data areas

12 (BDAs). Each f these can have its wn data access cntrls and SAS Visual Analytics cntent. In additin, BDAs can relate t segregated prjects within the BU. BUs and BDAs can als be virtual. They d nt have t strictly fit and restrict users based n the rganizatin structure. Fr example, virtual BUs and BDAs can help fster analysis n a cmmn set f data between tenants in the same rganizatin. In terms f data and cntent access security, this can be enfrced at bth the BU level and at the BDA level. Therefre, a user wuld need t be an identified member f a BU and a BDA t be able t access data secured at the BDA level. Mving ne level up, it is pssible fr nn-restricted data within a BU t be generally available t all tenants at the BU level. Mrever, grup-wide nn-restricted data can be made available t all SAS Visual Analytics tenants if required. Finally, at all levels, rw-level data restrictins culd be enfrced. Figure 8. Business Units and Business Data Areas Hierarchy in a Multi-Tenant Envirnment METADATA SECURITY AND FOLDER STRUCTURE The SAS 9.4 platfrm prvides a metadata-based authrizatin layer that supplements prtectin frm the hst envirnment and ther systems. Yu can use this layer t manage access t almst any metadata bject (fr example, reprts, flders, data definitins, explratins, jbs, stred prcesses, and server definitins). SAS clients use a hierarchy f SAS flders t stre and rganize metadata. When yu install SAS, a set f default SAS flders is created. The flders are arranged in a structure that segregates system infrmatin frm business infrmatin, prvides persnal flders fr individual users, and prvides an area fr shared data. Within this verall structure, a custmized flder structure that meets specific infrmatin management, data sharing, and security requirements can be created. The flder structure belw is an example base template fr the SAS Visual Analytics multi-tenanted envirnment, fllwed by a descriptin f the varius subcmpnents f the structure. It represents a structure that allws fr easy implementatin f metadata security. It is a generic structure that helps script the prcess f nbarding a tenant and can be expanded r mdified depending n future needs and t accmmdate mre prjects. The flder structure shuld be prtected using a SAS metadata security mdel that uses access cntrl templates (ACTs). An ACT is a reusable, named authrizatin pattern that can be applied t multiple resurces. An ACT cnsists f a list f users and grups that indicates whether permissins are granted r denied fr each user and grup. [12] This apprach divides metadata cntent fr BDAs int three simple tp-level grupings: Data, Reprts, and Explratins. [13]

13 Figure 9. Example SAS Metadata Flder Structure Business Unit This is a cntainer fr the BDA flders. A user sees nly thse Business Data Area flders t which he r she has been given access. In additin, this can cntain nn-restricted data r cntent available t all members f the BU. Business Data Area This is an example f a BDA rt-level metadata flder. This is the strage lcatin fr relevant metadata related t the BDA. A BDA flder breaks dwn int the fllwing: Data This flder is visible nly t data administratrs fr the BDA. This is the primary strage area fr datarelated metadata that is nt LASR table metadata and therefre can be hidden frm users. HDFS includes the library and table metadata related t HDFS. Jbs includes any jbs and scheduled jbs related t the mvement f data int either HDFS r LASR. Queries cntains saved queries built frm the SAS Visual Data Builder. Surce includes library and table metadata related t data surce systems f the BDA. Explratins The strage area fr explratin-related metadata, nly users with access t the SAS Visual Analytics Explrer see this flder. The Ad hc subflder is an area where pwer users are able t save their shared versins f their wn cntent. The Standard subflder cntains explratin metadata generated thrugh a standard develpment prcess. The business data administratr is able t mve r create cntent in the Standard subflder nce it has gne thrugh a release prcess. LASR Data The strage area fr all LASR libraries assciated with the BDA SAS LASR Analytic Server and tables. Reprts The strage area fr reprt-related metadata such as reprts and images. The Ad hc subflder is an area where pwer users are able t save their shared versins f their wn cntent. The Standard subflder cntains explratin metadata generated thrugh a standard develpment prcess. The business data administratr is able t mve r create cntent in the Standard subflder nce it has gne thrugh a release prcess. Further custmer-specific subflders are nt shwn in the apprach example. Fr example, under ReprtsStandard in ne f the BDAs, yu might segregate metadata in flders that rganize the cntent by tpic r prject. The same applies t the Ad hc area, where users have the freedm t build their wn child flder structure t rganize cntent. TENANT DATA SEGREGATION Data Security Yu will likely have a requirement fr strng security cntrls between tenants. As such, any data security restrictins between the tenants have t be preserved acrss ptentially several layers f data. Operating System and DBMS SAS preserves the data security prvided by yur DBMS and perating system. It is essential t manage this physical layer access. Fr example, use hst perating system prtectins t limit access t any sensitive data n the perating system. Use DBMS security cntrls t manage access t surce data stred in the DBMS. It is imprtant that any shared data access r lad accunts acrss the user base are limited in scpe and are nt

14 shared between the tenants and BDAs (i.e., any service accunts are specific t a single BDA t prevent crssbundary data access). HDFS If Hadp is used as the c-lcated data prvider, data stred in the HDFS needs t be segregated acrss tenants. HDFS implements a permissins mdel fr files and directries that are shared with the system used in UNIX. Each file and directry is assciated with an wner and a grup. The file r directry has separate permissins fr the user that is the wner, fr ther users that are members f the grup, and fr all ther users. Fr files, the Read permissin is required t read the file and the Write permissin is required t write r append t the file. Fr directries, the Read permissin is required t list the cntents f the directry, the Write permissin is required t create r delete files r directries, and the Execute permissin is required t access a child f the directry. Cllectively, the permissins f a file r directry are its mde. Each client prcess that accesses HDFS has a tw-part identity cmpsed f the user name and grups list. Whenever HDFS des a permissins check n a file r directry accessed by a client prcess, the fllwing happens: If the user name matches the wner, the wner permissins are tested. Else, if the grup matches any member f the grups list, grup permissins are tested. Otherwise, ther permissins are tested. If a permissins check fails, the client peratin fails. SAS LASR Analytic Server The SAS LASR Authrizatin Service resides in the middle tier. When a user perfrms an actin within SAS Visual Analytics that requires access t a LASR table in the SAS LASR Analytic Server, the SAS LASR Authrizatin Service prvides a signed grant t the applicatin. This signed grant is generated based n the user s SAS metadata permissins n the resurces invlved. SAS Visual Analytics submits the signed grant t the SAS LASR Analytic Server. When the SAS LASR Analytic Server receives the signed grant, it perfrms the fllwing actins: Validates the signature n the grant. Prcesses the request based n the cnstraints f the grant. Fr example, this might include limiting a user s access t rws via the permissin cnditins that apply t the data surce. The SAS LASR Authrizatin Service manages bth table-level access and rw-level access t data laded int the SAS LASR Analytic Server. In additin, fr each SAS LASR Analytic Server, server signature files and table signature files are created n the perating system file system. These files need t be secured with OS cntrls, especially when data lading and unlading is allwed t happen utside f the SAS Visual Analytics web clients. It is pssible fr data administratrs t circumvent the SAS LASR Authrizatin Service and SAS metadata permissins thrugh cde if these files are nt secured. The apprach t secure these signature files is exactly the same as with any peratin system files. It invlves creating specific directries within a system path and then securing each directry apprpriately using OS security cntrls. When a LASR library is defined in metadata r a SAS LASR Analytic Server is started thrugh cde, the signature lcatin n the server can be cnfigured. All activity fr that particular server instance uses the specified subdirectry. LOADING DATA INTO THE SAS LASR ANALYTIC SERVER A simple way t cllectively handle the security nuisances at each step in the data flw prcess is t fllw these rules: Each BDA has its wn SAS LASR Analytic Server defined that runs acrss the cluster. Each BDA has a unique service accunt identity that manages activities fr the BDA s SAS LASR Analytic Server. (This allws OS and HDFS cntrls t be applied against the service accunt identity.) In SAS Visual Analytics, passwrdless SSH is used fr internal cmmunicatin and prcess executin between the machines in the SAS LASR Analytic Server cluster. Therefre, a wrking passwrdless SSH cnfiguratin is required fr the user accunt that cmpletes the fllwing actins: Start r stp a SAS LASR Analytic Server. Lad, unlad, append, r update data in a table in the SAS LASR Analytic Server.

15 Using Tken Wrkspace t Link Service Accunts t SAS LASR Analytic Servers SAS Wrkspace Servers interact with SAS by creating a server prcess fr each client cnnectin. Each SAS Wrkspace Server prcess enables client prgrams t access SAS libraries, perfrm tasks by using the SAS language, and retrieve the results. In the SAS Visual Analytics envirnment, tasks executed thrugh the SAS Wrkspace Server are respnsible fr interacting with HDFS and the SAS LASR Analytic Server itself. T facilitate these rules, specific BDA applicatin server cntexts need t be cnfigured. These cntexts cntain a single SAS Wrkspace Server cnfigured t use tken authenticatin. The figure belw shws what the generic setup wuld lk like when turned int SASAppBDA applicatin server cntext. In the actual implementatin fr a specific BDA, the BDA prtin wuld be renamed t a BDA-specific identifier. Figure 10. Applicatin Server Cntext Cnfiguratin with a BDA-Specific SAS Wrkspace Server Tken authenticatin is when the SAS Metadata Server generates and validates a single-use identity tken fr each authenticatin event. This has the effect f causing participating SAS servers t accept users wh are cnnected t the SAS Metadata Server. When used against a SAS Wrkspace Server, this means that n individual external accunts are required, n user passwrds are stred in the metadata, and n reusable credentials are transmitted. S, as lng as a user can lg in t the SAS Metadata Server and has the required metadata access t resurces, he r she can use tken authenticatin t execute SAS prcesses and tasks using the BDA service accunts. As a result, service accunts assciated with individual BUs r departments can be used behind the scenes. Figure 11. SAS Tken Authenticatin The BDA service accunts are used as the launch credentials f the tken identity fr each f their crrespnding SAS Wrkspace Servers. These are made available t data administratr users (business and IT) t facilitate the interactive lading and unlading f data frm surce data sets int HDFS, starting and stpping in-memry LASR prcesses, and lading, unlading, appending, and updating data in the SAS LASR Analytic Server. Fr batch interactins in data in this scenari, the respnsibility rests with central IT, wh has access t all f the underlying service accunts and schedules thrugh the nrmally invked batch server belnging t the grupwide SASApp cntext. These service accunts are als the prcess wners f each BDA SAS LASR Analytic Server instance.

16 After this cnfiguratin has been put in place, users with the required metadata privileges and rles t administer data are allwed t select the relevant applicatin server cntext fr their tasks interacting with data in HDFS and the SAS LASR Analytic Server t execute in. This allws the security design t have the individual service accunts tightly integrated with OS permissins and Hadp permissins t prevent crss-bda data access. Access t launch prcesses belnging t the BDA service accunts is cntrlled thrugh SAS metadata security and rles. Therefre, it is pssible t have bth business data administratrs wh can interact with their wn BDA data and prcesses as well as IT-based data administratrs wh can interact with all BDAs. The SAS Wrkspace Server, under the default SASApp applicatin server cntext, has default hst authenticatin, meaning that users cannt use this applicatin server cntext t bypass security put in place due t the lack f any individual hst accunts t launch prcesses. Even thugh SASApp can t be used fr data lading, this applicatin server cntext is left visible t facilitate SAS Visual Analytics standard functinality (i.e., retrieving map tiles fr ge maps that use nnstandard SAS Wrkspace Servers like the SAS Pled Wrkspace Server, which is just a platfrm-wide shared resurce running under the ut-f-the-bx SAS General Servers accunt). Lightweight Authenticatin Mdel Thanks t the shared nature f the BDA service accunt that manages the flw f data int the SAS LASR Analytic Server in this distributed envirnment, a lightweight authenticatin mdel can keep the verhead f managing accunts between tenants lw. The basic premise f the authenticatin mechanism in this scenari is: Active Directry/LDAP Server is the authenticatr fr all standard users (n hst accunts are required n the SAS LASR Analytic Server cluster). Hst accunts that d exist are BDA service accunts. During the nbarding prcess fr a new tenant, the BDA service accunt s passwrdless SSH is cnfigured and invked behind the scenes thrugh the use f tken-based SAS Wrkspace Servers assciated with thse accunts. Access t invke these tken-based SAS Wrkspace Servers is limited t the central IT administratin team t manage batch lading f data and t selected individuals within a BDA wh have been designated as data administratrs and allwed t lad data interactively. In SAS Visual Analytics distributed mde, all f the servers in the cluster are using an perating system based n Linux, s all servers share an authenticatin prvider by default (the Linux hst). The SAS LASR Analytic Server and the SAS Wrkspace Server always authenticate against the hst, using whatever prvider the hst is cnfigured t use. The SAS Metadata Server des the same by default, but can be cnfigured t use an alternate mechanism. T better handle the smth additin and synchrnizatin f business users t the SAS Visual Analytics envirnment, sme f the authenticatin verhead, in terms f users and grups, are managed in an external authenticatr. SAS Visual Analytics can be integrated with the cmpany s Active Directry/LDAP Server t manage this. The integratin level fr SAS Visual Analytics will be the mst lightweight thrugh cnfiguring the SAS Metadata Server t use direct LDAP authenticatin. In this case, the SAS Metadata Server validates users against an LDAP prvider such as Active Directry. Direct LDAP enables the SAS Metadata Server t recgnize accunts that aren't knwn t its hst. Direct LDAP desn't mdify the hst s behavir.

17 Figure 12. Direct LDAP Authenticatin The benefits f this apprach are: Using existing user identities in Active Directry/LDAP Server t authenticate users against the envirnment. With direct LDAP, yu d nt have t intrduce r synchrnize these identities acrss the SAS Visual Analytics cluster. Limiting the amunt f hst accunts and passwrdless SSH cnfiguratins that need t be set up. Decreasing the maintenance required fr standard users. Using existing IT prcesses fr adding users t Active Directry/LDAP Server grups and ptentially keeping SAS Metadata Server users synchrnized with the central authenticatr mechanism. The actual setup f this integratin needs t happen nly n the SAS Metadata Servers f the SAS Visual Analytics cluster. SCRIPTING ONBOARDING TENANTS All f these varius steps t accmmdate and segregate tenants are scriptable. This is an imprtant aspect f minimizing the verhead and length f time t nbard a new tenant. Operating system scripts are used t: Create the BDA service accunt n the hst OS. Prpagate accunt and SSH keys acrss the cluster. Create OS area fr LASR signature files and secure it t BDA service accunt. Create the HDFS path fr the BDA and secure it t the BDA service accunt. Launch the SAS Deplyment Wizard t create a BDA SAS Applicatin Server and BDA SAS Wrkspace Server. SAS Open Metadata Interface and SAS Platfrm Object Framewrk are used t: Cnvert the BDA SAS Wrkspace Server t tken authenticatin and assciate it with the BDA service accunt. Define a BDA SAS LASR Analytic Server and library. Create the BDA flder structure based n the flder template. Create ACTs t secure the BDA flder structure. Apply the ACTs t the BDA flder structure and ther resurces. SAS scripts are als used t manage the additin and synchrnizatin f users and grups frm the Active Directry/LDAP Server.

18 SCALABILITY Scale Up T scale up hardware is t add hardware resurces such as CPU and memry t a nde in the envirnment. In a VMware vclud envirnment, this is relatively easy t achieve and can be dne n all f the majr cmpnents f a SAS Visual Analytics envirnment (SAS Metadata Server, SAS Cmpute Server, SAS middle tier, and LASR ndes). This is a little bit trickier in a cmmdity blade-based infrastructure because f the ptentially limited expansin ptins available. It is wrth nting that if scaling up is dne n the LASR wrker ndes, yu shuld be aware that, assuming unifrm data distributin and prcessing, the cluster perfrms at the speed f the slwest nde. Any scaling up has t happen acrss all ndes r yu will have unbalanced prcessing. Scale Out A mre ideal ptin, especially fr blade infrastructure, is scaling ut. This is bringing in mre ndes t increase wrklad. With SAS 9.4, all majr cmpnents f the SAS Visual Analytics envirnment can be scaled ut (with clustered metadata, SAS middle-tier, and lad-balanced SAS Cmpute Server ndes). The same applies t SAS LASR Analytic Server ndes [14]. Adding additinal hardware increases the verall data and cmputatinal capacity f the cluster. Fr the SAS LASR Analytic Server, this is a fairly linear (i.e., yu duble the amunt f LASR wrker ndes, yu duble the data and cmputatinal capacity). CHARGEBACK AND COST MODEL An imprtant aspect f the prvisining f the private clud is hw t measure and chargeback usage t the tenants. There are generally tw main ptins: Subscriptin a recurring fee ver a length f time. Usage based track what is used with specific metrics that drive the bill. Fr the custmer scenari, a subscriptin mdel was chsen. The reasn fr aviding a usage-based system was the likelihd that with pure usage-based csting (fr example, direct CPU usage), there wuld be a determent f a wider adptin f the platfrm (i.e., if using smething less directly means less cst, then this becmes the main driver f defining an everyday value fr the adptin f the technlgy). What can be used t determine the subscriptin cst in a SAS Visual Analytics envirnment fr a tenant? Primarily, yu must determine the capacity f the cluster and the selling prtins f it t the tenants. These prtins can be brken dwn as: Physical data strage The platfrm has a finite amunt f physical disk fr the HDFS. In-memry data strage The platfrm has a finite amunt f memry t hld memry in the SAS LASR Analytic Server. Users Based n the size f the cluster and the vlume f the data, nly a certain number can be supprted at a time (fr example, named and cncurrent users). (This can be seen a quasi way f selling CPU capacity withut actually tracking it.) On the ne hand, yu have all f the platfrm csts hardware, sftware, services, and maintenance. On the ther hand, yu have platfrm capacity. A base value can then be calculated and brken dwn int charge per GB fr physical data strage, charge per GB fr data in memry, and charge per user. The tenant chses hw much GB he r she wuld like t reserve n the physical disk, memry, and the number f users t prduce a relevant subscriptin cst. The mdel des nt have t be ttally cst neutral, it just needs t cver csts. A small verhead can be added, meaning that mney culd be accrued int an investment fund t start the prcess f scaling ut the platfrm befre capacity is cmpletely full. This prevents prblems such as when nce capacity is reached, the next tenant has t effectively subsidize the scaling ut and is expected t wait fr the prcess t cmplete befre nbarding. CONCLUSION SAS 9.4 and SAS Visual Analytics supprt many clud deplyment ptins t match yur specific needs. The paper demnstrates the prcesses and design that g int building a truly enterprise clud platfrm that can reveal the value lcked in data t all areas f yur business. REFERENCES [1] Mell, Peter, and Grance,Timthy The NIST Definitin f Clud Cmputing. Natinal Institute f Standards and Technlgy. Available at:

19 [2] SAS 9.4 Supprt fr Web Brwsers and Plug-Ins. Available at: [3] SAS Visual Analytics fr SAS Clud: Quick-Start Guide. Available at: [4] Tagging Yur Amazn EC2 Resurces. Available at: [5] Deplyment and administratin dcumentatin. Available at: [6] VMware vclud Architecture Tlkit (vcat) at: [7] Enabling a CPUfreq Gvernr. Available at: https://access.redhat.cm/site/dcumentatin/en- US/Red_Hat_Enterprise_Linux/6/html/Pwer_Management_Guide/cpufreq_setup.html#enabling_a_cpufreq_gv ernr. [8] Make the Mst f Yur Analytical Talent. Available at: [9] Jhn R. Rymer s Blg. Understanding Clud's Multitenancy. Available at: [10] Macfarlane, Andrew, and Schneider, Frank Pi in the Sky: Building a Private SAS Clud. Prceedings f the SAS Glbal Frum 2013 Cnference. Cary, NC: SAS Institute Inc. Available at [11] Gahagan, Ken SAS Grid Manager, SAS Visual Analytics, and SAS High-Perfrmance Analytics: Sharing Hardware and Mre. Prceedings f the SAS Glbal Frum 2014 Cnference. Cary, NC: SAS Institute Inc. [12] SAS 9.4 Intelligence Platfrm: Security Administratin Guide, Secnd Editin. Available at: [13] Chitale, Anand, and Redpath, Christpher Whirlwind Tur Arund SAS Visual Analytics. Prceedings f the SAS Glbal Frum 2013 Cnference. Cary, NC: SAS Institute Inc. Available at [14] SAS LASR Analytic Server 2.2: Reference Guide. Available at: ACKNOWLEDGMENTS We wuld like t acknwledge the great wrk and cntributins t this paper by Rb Stephens and Erwan Granger. CONTACT INFORMATION Yur cmments and questins are valued and encuraged. Cntact the authrs at: Christpher Redpath SAS Institute Inc. 100 SAS Campus Drive Cary, NC Nichlas Eayrs SAS Institute Inc. 100 SAS Campus Drive Cary, NC SAS and all ther SAS Institute Inc. prduct r service names are registered trademarks r trademarks f SAS Institute Inc. in the USA and ther cuntries. indicates USA registratin. Other brand and prduct names are trademarks f their respective cmpanies.

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