Cost/Benefit Case for SAP HANA Deployment Comparing Costs and Effectiveness of IBM and Competitive...

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February2013

MANAGEMENTBRIEF

International Technology Group609 Pacific Avenue, Suite 102

Santa Cruz, California 95060-4406Telephone: 831-427-9260

Email: [email protected]

Website: ITGforInfo.com

Cost/Benefit Case for SAP HANA DeploymentComparing Costs and Effectiveness of

IBM and Competitive Solutions



Copyright © 2013 by the International Technology Group. All rights reserved. Material, in whole or part, contained in this document may not be

reproduced or distributed by any means or in any form, including original, without the prior written permission of the International TechnologyGroup (ITG). Information has been obtained from sources assumed to be reliable and reflects conclusions at the time. This document wasdeveloped with International Business Machines Corporation (IBM) funding. Although the document may utilize publicly available material from

various sources, including IBM, it does not necessarily reflect the positions of such sources on the issues addressed in this document. Materialcontained and conclusions presented in this document are subject to change without notice. All warranties as to the accuracy, completeness or

adequacy of such material are disclaimed. There shall be no liability for errors, omissions or inadequacies in the material contained in this

document or for interpretations thereof. Trademarks included in this document are the property of their respective owners.

TABLE OF CONTENTS EXECUTIVE SUMMARY 1

SOLUTIONS 5 SAP HANA 5

Overview 5 Applications 5 Evolution 6

Appliances 7 Overview 7

Single-node Configurations 7 Scale-out Configurations 9 Performance Issues 10 High Availability and Disaster Recovery 11

DETAILED DATA 13 Basis of Calculations 13 Cost Breakdowns 13

List of Figures 1. Three-year Appliance Costs for SAP HANA Deployment

– Averages for All Scale-out Configurations 2

2. Largest SAP HANA Test Systems 3

3. SAP HANA Applications 5

4. Planned SAP HANA Enhancements 6

5. SAP HANA Single-Node Appliance Configurations 8

6. SAP HANA Scale-Out Appliance Configurations 9

7. SAN-based and IBM GPFS Configurations 10

8. HP Disaster Recovery Solution for HP AppSystems for SAP HANA 11

9. Cost Breakdowns (1) 13

10. Cost Breakdowns (2) 14



International Technology Group 1

EXECUTIVE SUMMARY

In barely two years, SAP HANA has gone from an ambitious design to a major force in the IT world.

SAP investments in architecture and technology, and in creating an ecosystem of applications, skills and

third-party support, have encouraged rapid adoption worldwide.

HANA deployment is at an early stage in most organizations. In January 2013, SAP reported that it had

1000 customers, with at least 200 in production and approximately 500 projects underway. Customer

experiences have been overwhelmingly positive.

Among 23 HANA users surveyed for this report, for example, improvements in query response and/or

report generation throughput that ranged from 20 to more than 800 times were reported. Data loading was

accelerated by 3 to more than 10 times. Users expected that performance would increase as tuning and

workload management improved.

A further result should be highlighted. In 17 cases (74 percent), users planned or expected to add

additional HANA applications. In five cases, worldwide deployments were planned following successful

experiences in individual business units or local subsidiaries.

HANA analytical usage will clearly expand, and will increasingly extend to new applicationsincorporating “big data” content. The SAP portfolio of transactional systems will also progressively move

to this platform. Availability of SAP Business Suite 7 for HANA was announced in January 2013.

Realizing the potential of HANA will mean addressing many business and technical challenges. One of

these challenges – which is the subject of this report – will be to put in place appliance infrastructures that

are capable of handling massive, sustained growth in workloads and data volumes over multi-year periods.

There are currently seven SAP-certified appliance vendors – Cisco Systems, Dell, Fujitsu, Hewlett-

Packard (HP), Hitachi Data Systems (HDS), IBM and NEC. All offer single-node configurations, while

only Cisco, Dell, Fujitsu, HP and IBM offer scale-out solutions.

There is a striking disparity between scale-out architectures. With the exception of IBM, all scale-outvendors offer variants of Network File System (NFS) and storage area networks (SANs), including

external disk arrays. IBM employs General Parallel File System (GPFS), which offers higher performance

and does not require SANs or disk arrays.

There are implications in three main areas:

1. Costs of ownership. Three-year costs of ownership are lower for use of the GPFS-based IBM

Systems solution for SAP HANA. For 4-, 8-, 12- and 16-node configurations, costs for the IBM

solution average 27 percent less than those for HP AppSystems for SAP HANA, and 21 and 23

percent less than those for Cisco Systems equivalents using EMC VNX and NetApp FAS disk

arrays respectively.

Figure 1 illustrates these results.

Costs include hardware, maintenance, licenses and support for vendor-supplied software tools,

and facilities including data center occupancy and power consumption. Costs of SAP software

and implementation are not included.




Figure 1: Three-year Appliance Costs for SAP HANA Deployment – Averages for All Scale-out Configurations

In this presentation, infrastructure costs are for switches, racks and related components.

Calculations are based on configurations as described by vendors, and on discounted prices for

individual components. Actual vendor prices may differ in practice.

Costs for Cisco and HP systems, which are blade-based, include chassis and fabrics. Costs for

IBM systems are for System x3950-based rack mount servers equipped with internal storage, and

include GPFS licenses and support. Additional information on configurations and costs may be

found in the Detailed Data section of this report.

2. Performance and scalability. Because of the early state of HANA deployment, potential

differences between appliances in performance and scalability may not yet be visible. However,

they will become a great deal clearer as systems expand.

The main challenge vendors have faced is that HANA scale-out architecture requires technologies

that are closer to the supercomputing world than to conventional commercial IT environments.

IBM GPFS was designed as a parallel file system, and has been widely employed in

supercomputing for more than a decade. It has shown near-linear scalability in extremely large

configurations – systems with 1,000+ nodes are common, and the largest exceed 5,000 nodes.

Data volumes often run to hundreds of terabytes, and petabyte-scale systems have been deployed.

NFS variants have shown more limited results. For example, academic users have reported that

GPFS outperforms conventional NFS by 5 to 10 times, while disparities between Multi Path File

System (MPFS) – employed in Cisco scale-out configurations using EMC disk arrays – and

parallel NFS (pNFS) appear to be in the two to four times range.

It can be expected that, in large SAN configurations, disk array and switching latencies will cause

further performance degradation relative to GPFS.

In terms of scalability, all vendors offer SAP-certified 16-node scale-out configurations –

although there is little production experience with these – and Cisco claims that 48-node

configurations may be “certified on request.” However, IBM servers and GPFS formed the basis

of the three highest-performing HANA systems demonstrated to date.

HPAppSystems

Cisco&NetApp

Cisco&EMC

IBMSystemssoluon

Servers Storage Infrastructure Facilies$Thousands

1,005.45

1,274.99

1,307.09

1,386.19




During 2012, SAP and IBM demonstrated a 16-node HANA system processing 100 terabytes (TB)

of raw data. It was followed by a 100-node system – described by SAP as “the world's largest in-

memory database system ever assembled” – handling 1,000 TB (one petabyte) of raw data. In this

test, a 25 times increase in query volume resulted in negligible performance degradation.

An expanded version of this cluster with 150 nodes was demonstrated at SAPPHIRE Madrid in

November 2012.

Figure 2 summarizes workloads and results for these tests.

TestDate April2012 October2012 November2012

Rawdatasize 100TB 1,000TB 1,000TB

Compresseddatabase 3.78TB 49.2TB 49.2TB

Numberofrecords 120billion 1,200billion 1,200billion

Configuration 16xIBMeX5

4/40xE7-8870

512GBRAM/node

3.3TBdisk/node

100xIBMeX5

4/40xE7-8870

1TBRAM/node

3.3TBdisk/node

150xIBMeX5

4/40xE7-8870

1TBRAM/node

3.3TBdisk/node

Figure 2: Largest SAP HANA Test Systems

Query workloads, according to SAP, were modeled on those of HANA users. The 1.2 trillion

records employed in the one-petabyte demonstration corresponded to ten years of data for a large

corporation generating an average of 330 million transactions per day. In most cases, complex

queries were processed in under a second. The largest was processed in under 3.2 seconds.

IBM employs GPFS for single-node as well as scale-out configurations. For single-node

applications, GPFS offers performance advantages over the standard Linux-based file systems,

ext3 and hfs, employed by other vendors. The I/O strengths of IBM X-Architecture in System x

servers further boost high-volume throughput relative to conventional x86 servers.

3. High availability and disaster recovery. Real-time analytics are inevitably sensitive to downtime.

Even brief interruptions of service may impair decision-making processes throughout

organizations. Recovering extremely large data volumes in the event of a serious outage will be a

challenging process.

As organizations deploy SAP transactional as well as analytical applications, vulnerability will

increase. A serious outage, or a protracted delay in recovering from one, may grind the entire

business to a halt. Maintaining continuous availability will be even more critical – and more

difficult – than with today’s enterprise resource planning (ERP) systems and data warehouses.

The SAP HANA design incorporates extensive high availability and disaster recovery features,

although the manner in which they are implemented varies between appliance vendors. There is,

again, a striking disparity between SAN-based approaches and IBM GPFS.

SAN-based approaches require synchronization of server- and array-based failover and recovery

mechanisms. Configuration complexity is materially increased, and there are more potential

points of failure. It will, moreover, clearly take time for the mainstream commercial solutions

employed by most vendors to become stable in a HANA environment.

In comparison, GPFS requires only failover of disk-equipped servers, and volumes of data that

must be recovered are significantly less.




A four-node GPFS cluster, for example, contains 4.8 TB of disk capacity, compared to 25.5 TB

for Cisco clusters with EMC arrays, and 28.8 TB for HP AppSystems and Cisco clusters with

NetApp arrays. In 16-node configurations, capacities are 19.2 TB, 102 TB and 115.2 TB

respectively.

GPFS also employs a stable and widely used failover and recovery architecture built around

Failure Groups. Data may be replicated to multiple standby nodes in real-time. For example, in

the one-petabyte HANA test discussed above, 95 active and 5 standby nodes were employed. In practice, ratios of active to standby nodes vary according to user requirements.

For SAP users undertaking POCs and pilots, the choice of hardware platform may appear to be a

secondary issue. But usage will evolve rapidly, and later changes not only in hardware platforms, but also

in distributed file systems – which will be closely entwined with SAP software – will be disruptive. It

may be necessary to interrupt service at a time when organizations are beginning to realize the full

business value of HANA applications.

Among appliance vendors, IBM appears to be the early market share leader. It would be surprising if this

were not the case. The architecture and technology underlying the company’s appliance offerings are – by

a wide margin – better equipped to deal with the long-term challenges of HANA deployment than any

competitive platform.




SOLUTIONS

SAP HANA

Overview

SAP HANA first appeared in 2010 as a new SAP-optimized architecture combining in-memorycomputing, columnar database, massively parallel processing (MPP) and advanced data compression

technologies. The HANA package also includes data modeling and development, analytics, replication,

loading and other tools.

Early functionality and customer adoption have focused on high-performance analytical applications.HANA has typically been employed to accelerate SAP Business Warehouse (BW) output. This was, for

example, the case for all but two of the HANA users surveyed for this report.

New data compression algorithms employed by SAP have proved highly effective, with users reporting

levels of four to eight times in early deployments. HANA is in principle capable of up to 20 times

compression, and some users expect to achieve this

Adoption has been facilitated by aggressive SAP efforts to develop the portfolio of applications for this platform, and to accelerate the emergence of an ecosystem of third-party tools, skills and support.

Applications

To date, SAP has adapted most of its major application offerings to run on HANA. Figure 3 lists these.

§ SAP360Customer

§ SAPAcceleratedTradePromotion

§ SAPBusinessObjectsBusinessIntelligence

§ SAPBusinessObjectsBusinessIntelligenceOnDemand

§ SAPBusinessOne

§ SAPBusinessPlanning&Consolidation

§ SAPBusinessSuite7

§ SAPCashForecasting

§ SAPCollectionInsights

§ SAPCO-PAAccelerator

§ SAPCustomerSegmentationAccelerator

§ SAPCustomerUsageAnalyticsforFinancialServices

§ SAPCustomerUsageAnalyticsforHigh-Tech

§ SAPCustomerUsageAnalyticsforTelecommunications

§ SAPDemandSignalManagement

§ SAPFinance&ControllingAccelerator

§ SAPGridInfrastructureAnalytics

§ SAPLiquidityRiskManagement

§ SAPNetWeaverBusinessWarehouse

§ SAPOperationalProcessIntelligence

§ SAPPOSDataManagementforReal-TimeRetailing

§ SAPPrecisionRetailing

§ SAPSalesAnalysisforRetail

§ SAPSales&OperationalPlanning

§ SAPSalesPipelineAnalysis

§ SAPSituationalAnalysis

§ SAPSmartMeterAnalytics

§ SAPSupplierInfoNet

§ Consumerapps(RecallsPlus,MyRunway)

Figure 3: SAP HANA Applications

SAP has aggressively sought to expand the base of third-party and user-developed applications through

its global Co-Innovation structures. Partnerships with key independent software vendors (ISVs), systems

integrators and appliance vendors have contributed to the company’s recruitment efforts. A program for

certification of third-party extract, transform and load (ETL) tools was recently launched.

SAP has launched a Real-Time Fund to encourage “early stage” companies to develop HANA

applications. As of November 2012, investments had been made through the Fund in 153 small and

midsize companies. In addition, SAP has made a limited version, SAP HANA One, available through

Amazon Web Services, and offers the full version through its own SAP NetWeaver Cloud service.




Evolution

The next stage of HANA evolution will, according to SAP, move this platform in multiple directions.

Analytical enhancements will continue. In-memory functions will expand (ETL services will, for

example, be implemented in RAM). Optimized support for ERP systems will introduced, and “big data”

capabilities will be enhanced.

This evolution has begun with SAP HANA support pack stack 5 (SPS5), announced in November 2012.

SPS5 creates a foundation for converged data and application serving, and for integration of online

analytical processing (OLAP) and online transaction processing (OLTP). Plans were also announced for

the enhancements summarized in figure 4.

§ SingleOLTP&OLAPenvironment willcombineread-optimized&write-optimizeddatastoressupportingtransactional&

OLAPapplicationsrespectively.

§ Naturallanguage-basedtextanalyticswillbesupportedacross31languages.

§ Expandedpredictiveanalyticswillenablecustomersegmentationandclustering.PredictiveModelMarkupLanguage

(PMML)willalsobesupported.

§ Extendedapplicationservicesenhancementswillinclude:

(1) Nativeapplicationserversupportfortwo-&21/2tierapplicationsusingHTLM5,JavaScript,JavaScriptObject

Notation

(JSON),SQLScript,XML/A&OpenDataProtocol(OData).

(2) Businessrulesmanagementwillbecomeacorecomponent.

(3) Applicationfunctionallibraryframeworkwillprovidedeveloperaccesstoembeddedalgorithms&business

functionsthroughSQLScriptorJavaScript.

§ Real-timestreamprocessingsupportthroughSybaseEventStreamProcessor(ESP).SybaseESPwillalsoexploitHANA

real-timeanalyticfeaturesforevaluationofcomplexdecisions.

§ CommonmodelingenvironmentacrossdatastoresthroughSybasePowerDesigner .

§ High-performancebulkloadingofdatafromnewsources,includingApacheHadoop.

§ Enhanceddataprofilingwillenablebusinessuserstomanagemetadatadirectlyinbusinessterms,monitordata

qualityscorecards&identifydataqualityissuesusingSAPInformationStewardsoftware.

§ SAPHANAPlatformforLarge-ScaleDataCenterDeployment willinclude:

(1) Supportforhot&warmstandbyservers;integrationwiththird-partybackup&failover/recoverysolutions;&

securityenhancementsincludingencryption&expandedauthentication,accessauthorization&auditlogging.(2) Supportformultipledevelopment,test&sandboxinstancesonsingleSAPHANAappliances.

Figure 4: Planned SAP HANA Enhancements

SAP has moved aggressively to position HANA in the ERP space. HANA support for SAP Business

Suite 7 was announced in January 2013.

It can be expected that HANA will become a major player in emerging “big data” applications. Many

HANA users surveyed for this report saw opportunities to expand applications to address unstructured as

well as structured data, and some were actively planning to do so.

In these and other SAP user organizations, investments in ERP, BW, BusinessObjects and other SAPsystems mean that large segments of data infrastructures are already SAP-based. In such organizations,

HANA is a prime candidate for applications that integrate conventional and new data types.




Appliances

Overview

In enabling hardware support, SAP defines specifications for and certifies hardware platforms, whileselected vendors implement, install and support these.

In HANA deployments, resources are dedicated; i.e., they may not be shared with other applications.

Currently, SAP recommends use of separate appliances for production, development and quality

assurance (QA), although this picture will change in 2013. The company has indicated that it will support

multiple non-production instances on a single physical HANA platform.

HANA runs only on certified hardware configurations with SUSE Linux Enterprise Server (SLES) for

SAP Applications. Key hardware requirements currently include use of platforms based on Intel Xeon

processor E7 family with up to 128 GB RAM per socket, and 10 Gbps Ethernet connections. According

to SAP, these specifications will evolve over time.

SAP expanded third-party support in November 2012 with the announcement that VMware vSphere will

be supported as the company’s “preferred way to virtualize HANA databases for non-production

instances.” Several appliance vendors have indicated plans to support VMware for HANA in early 2013,and the remainder are expected to follow suit.

The most likely VMware role, at least initially, will be to support multiple non-production instances in

comparatively small single-node installations. It is unlikely that VMware will be employed in a

production role, or for scale-out systems for some time.

Single-node Configurations

Single-node configurations are defined by SAP in “T-shirt” sizes (e.g., Extra Small, Small, Medium,

Large) with two, four or eight processors and up to 1 TB of main memory.

All processors are 10-core Intel E7 2.4 GHz models with Intel Hyper-Threading Technology; i.e., twothreads per core are supported. For the principal players – Cisco, Fujitsu, HP and IBM – there are some

variations in configurations that are illustrated in figure 5.

In terms of technology, the largest differences in single-node configurations are in two areas:

1. File systems. For single node HANA configurations, all vendors other than IBM have adopted

third extended filesystem (ext3) and/or XFS. These are journaled file systems forming part of the

Linux kernel. IBM employs GPFS, which typically delivers higher levels of performance –

general industry experience is 20 to 50 percent higher – than standard Linux-based equivalents.

2. X-Architecture. This is an Intel processor-based design employed in IBM System x3690 X5 and

x3950 X5 servers. Currently in its fifth generation (eX5), X-Architecture offers higher I/Omemory and performance than conventional x86 servers. It has proved highly synergistic with

HANA workloads.

X-Architecture is implemented through extensions to Intel Xeon processor E7 family that

conform to the company’s QuickPath Interconnect (QPI) specification.




XS

128GB

S

256GB

S+

256GB

M

512GB

M+

512GB

L

1TB

CiscoSAPHANAAppliances

UCSC260M2

2/20x2870

2.4GHz

128GBRAM

6x100GBSSDlog

10x600GBSAS

data

UCSC260M2

2/20x2870

2.4GHz

256GBRAM

6x100GBSSDlog

10x600GBSAS

data

UCSC460M2

4/40x4870

2.4GHz

512GBRAM

2xFusion-io365GBlog

12x300GBSAS

data

FujitsuSAPHANAAppliances

RX600S6

2/20xE74870

2.4GHz

128GBRAM

2xFusionIO

320GBlog

8x600GBSASdata

RX600S6

2/20xE74870

2.4GHz

256GBRAM

2xFusionIO

320GBlog

8x600GBSASdata

RX600S6

4/40xE74870

2.4GHz

512GBRAM

2xFusionIO

320GBlog

8x600GBSASdata

RX900S2

8/80xE78870

2.4GHz

1TBRAM

2xFusionIO

1.2TBlog

8x900GBSASdata

HPAppSystemsforSAPHANA

DL580G7

2xE7-4870

2.4GHz

128GBRAM

1x320GB

Fusion-iolog

2x300GBSAS+24

x146GBSASdata

DL580G7

2xE7-4870

2.4GHz

256GBRAM

1x320GB

Fusion-iolog

2x300GBSAS+24

x146GBSASdata

DL580G7

4xE7-4870

2.4GHz

512GBRAM

2x320GB

Fusion-iolog

2x300GBSAS+24

x146GBSASdata

DL980G7

4/40x4870

2.4GHz

512GBRAM

2x320GB

Fusion-iolog

2x300GBSAS+24

x300GBSASdata

DL980G7

8/80x4870

2.4GHz

1TBRAM

4x320GB

Fusion-iolog

2x300GBSAS+24

x300GBSASdata

IBMSystemsolutionforSAPHANA

x3690X5

2/20x2870

2.4GHz

128GBRAM

10x200GBSSD

(log&data)

x3690X5

2/20x2870

2.4GHz

256GBRAM

10x200GBSSD

(log&data)

x3950X5

2/20x8870

2.4GHz

256GBRAM

1.2TBFusion-iolog

8x900GBSAS

x3950X5

4/40x8870

2.4GHz

512GBRAM

1.2TBFusion-iolog

8x900GBSASdata

x3950X5

8/80x8870

2.4GHz

1TBRAM

2x1.2TBFusion-io

log

16x900GBSASdata

Figure 5: SAP HANA Single-Node Appliance Configurations

Upgrade paths vary. Cisco and HP require two platform changes in moving from entry-level to scale-out

configurations and, in HP’s case, users must transition from DL series rack-mount to BL series blade

servers. Fujitsu and IBM allow users to upgrade from four-way rack-mount servers with 512 GB to scale-

out configurations built around the same platform.

In addition, all vendors offer SAP Business Warehouse Accelerator (BWA) appliances, which employ a

HANA-like server architecture to offload compute-intensive processing from BW systems. Although

SAP HANA will replace BWA, it is expected that the latter will remain in use among organizations that

do not require full HANA functionality, or who do not yet wish to undergo the software changes required

by this solution.

Cisco also offers the SAP-certified Cisco Bridge to SAP HANA, which is designed to act as a BWA

appliance while enabling future migration to a full Cisco HANA platform. The Bridge appliance is built

around dual-socket UCS B200 M3 blades with Intel E5-2670 processors. It would be necessary to replace

blades to meet HANA specifications.




Scale-out Configurations

Scale-out configurations show greater hardware variations than single-node equivalents. Current offerings

for the same vendors are summarized in figure 6.

IBMSystemsolutionfor

SAPHANA

CiscoSAPHANA

Appliances

HPAppSystemsfor

SAPHANA

FujitsuSAPHANA

Appliances

4-16nodescertified

100nodestested

4-16nodescertified

Upto48nodes“certifiedon

request”

4-16nodescertified 4-16nodescertified

Servers&Storage

x3950X5

4/40xE788702.4GHz

512GBor1TBRAM

1.2TBFusion-io

8x900GBSAS

or

x3690X5

2/20xE728702.4GHz

256GBRAM

10x200GBSSDSwitches

2xRackSwitchG8264

Servers

UCSB440M2

4/40xE748702.4GHz

512GBRAM

UCS6248Fabric

Interconnect

Nexus2224FabricExtender

UCSC220

ManagementServer

Storage

upto4x

EMCVNX5300

2storageprocessors

16GBRAM

75x300GBSAS

or

NetAppFAS3240HA

2controllers

16GBRAM

1TBflashcache

48x600GBSAS

DataONTAP

Switches

2xNexus5596UP(EMC)2xNexus5548UP(NetApp)

2911Router

Servers

c7000BladeSystem

5108chassis(upto4x)

BL680G7(upto16x)

4/40xE748702.4GHz

512GBRAM

VirtualConnectFabric

X3400StorageManagement

Server

Storage

upto4x

EVAP6500

2controllers

16GBRAM

48x600GBSAS

XCS,CommandView

2xX9300IBRIXgateway

nodes–NFS

Switches

2xProCurve6600LAN

2xStorageWorksSAN

2910ProCurveswitch

Servers

RX600S6

4/40xE748702.4GHz

512GBRAM

PrimergyRX100

ManagementServer

Storage

upto4x

EternusNR1000

(NetAppFAS3240)

2controllers16GBRAM

1TBflashcache

48x600GBSAS

Switches

2xBrocadeVDX

Figure 6: SAP HANA Scale-Out Appliance Configurations

Scale-out configurations are built around 512 GB or (for the IBM System x3950 X5) 1 TB RAM. In a 16-

node configuration, IBM System solution for SAP HANA supports 16 TB RAM compared to 8 TB for

competitive platforms.

Most vendors have adopted variants of NFS. Since its introduction by Sun Microsystems in 1983, NFS

has become the industry’s most widely used distributed file system for commercial applications, and is

the de facto standard for network-attached storage (NAS). Since 2000, NFS has been an open source product whose development is managed by the Internet Engineering Task Force (IETF).

NFS is, however, generally recognized to generate comparatively high levels of server overhead, and

commonly experiences performance bottlenecks in high-volume, I/O-intensive environments. Its

popularity among HPC users has declined in favor of parallel file systems.

A number of attempts have been made to adapt NFS for higher performance. MPFS, for example, was

originally developed by EMC and introduced in 2002 for its Celerra NAS platform. It was initially

marketed by the company as a means of addressing conventional NAS scalability limitations, but was

later adopted by some supercomputer users.




MPFS was adopted by Cisco for scale-out configurations incorporating EMC VNX systems. EMC has

claimed that MPFS is “three to four times” faster than conventional NFS, and Cisco has claimed “three

times” faster in a HANA environment. Actual disparities depend upon workloads.

A second initiative has involved Parallel NFS (pNFS), which forms part of NFS Version 4.1 released by

the IETF in January 2010. Although pNFS has experienced some initial stability problems, it is expected

that it will eventually become a feature of HANA scale-out deployments. Early experiences suggest that

performance disparities relative to conventional NFS are approximately the same as for MPFS.

NFS is coupled with external disk arrays and SANs. These add to configuration complexity and, because

of the higher latencies involved, retard performance. In Cisco as well as HP configurations, blade fabrics

compound these effects. This is particularly the case for HP’s scale-out solution, which combines blade

fabrics, disk arrays (EVA P6500), NAS gateways (X9300 IBRIX) and dual LAN and SAN switches.

There are a number of differences between GPFS and NFS variants employed to support SAP HANA.

The most important is that GPFS stripes data across all disks on all nodes, and reads and writes to these in

parallel. External disk arrays and SANs are not required. Figure 7 illustrates this distinction.

Figure 7: SAN-based and IBM GPFS Configurations

GPFS also incorporates a distributed metadata structure, policy-driven automated storage tiering,

managed high-speed replication, information lifecycle management (ILM) tooling and other features.

Performance Issues

There are currently no generally recognized HANA-specific benchmarks. The SAP BW Enhanced Mixed

Load (BW-EML) Benchmark, which has been publicized by HP, is not an exception. The only benchmark result to date, certified by SAP in May 2012, was for an HP configuration. Although a HANA database

was employed, the benchmark is BW- rather than HANA-specific.

The configuration for which the tests were run – a three-tier structure including dedicated DL580 G7-

based database and BL680c G7-based application servers – is not standard for HANA appliances,

although it might be appropriate for generic BW environments. The configuration does not include disk

arrays, NFS gateways or switches.

Apart from HP, no other appliance vendors have to date published BW-EML results.

SAN-based

LAN

ServerNodes

SAN

DiskArrays

IBMGPFS

LAN

ServerNodes

Duplexedswitch




High Availability and Disaster Recovery

High Availability (HA) and Disaster Recovery (DR) features form part of the SAP HANA design,

although these had not been fully implemented when this report was written. DR was formerly named

Disaster Tolerance (DT).

The overall HANA design allows for use of failover to cold, warm and hot standby servers (“cold,” in this

context, means that a standby server receives periodic scheduled backups, but is not activated until the

primary server fails; “warm” means that data is copied periodically between active servers; and “hot”

means that data is replicated continuously, in real time).

Synchronous replication for disaster recovery is supported for local or regional replication, typically up to

50 kilometers (c. 30 miles). Asynchronous capability, supporting distances of over 500 kilometers (or 300

miles) is planned for 2013. SAP expects that, in practice, appliance vendors will play the central role in

implementing HA and DR solutions.

All vendors duplex key components and offer extensive reliability, availability and serviceability (RAS)

features. Server- and, where these are employed, array-based disks are configured as RAID systems.

There are, however, significant differences between SAN-based approaches and IBM GPFS. The former

will tend to result in more complex configurations requiring synchronization of server- as well as array-level failover and recovery processes.

HP, for example, recently announced that SAP had certified its disaster recovery solution for HP

AppSystems for SAP HANA (Scale-Out), which employs the company’s EVA-based Continuous Access

remote replication and recovery software. Server-level clustering, which will use HANA native

mechanisms, has not yet been implemented.

This solution, which is currently supported only for synchronous replication, is illustrated in figure 8.

Figure 8: HP Disaster Recovery Solution for HP AppSystems for SAP HANA

It can be expected that other vendors’ SAN-based disaster recovery solutions will be generally similar.

All vendors plan support for asynchronous replication for 2013.

NFS

Servers

SAN

EVA6500

X9300

IBRIXCluster

NFS

Servers

SAN

EVA6500

X9300

IBRIXCluster

Continuous

Access




DETAILED DATA

Basis of Calculations

The cost comparisons presented in this report are for the scale-out configurations shown in figure 6.

Calculations were based on hardware and, where appropriate, systems software stacks as described in

SAP and/or vendor documentation, and are based on discounted list prices as reported by users.

In some cases, vendors charged a single price for hardware, maintenance and software licenses and/or

support. Where this was the case, costs were counted as hardware. Calculations do not include costs for

Linux operating systems, which would normally be the same for all platforms.

Facilities costs for data center occupancy are for space occupied by racks, including allowance for service

clearances and inactive areas, and are based on a conservative assumption of average cost per square foot

for existing Tier I facilities (i.e., costs do not include new construction). Costs also include energy

consumption by IT equipment and by cooling, power distribution and other data center systems supporting

these. Costs were calculated based on 24-hour, 365 days per year utilization over a three-year period.

All cost values were for the United States.

Cost Breakdowns

Detailed breakdowns of costs are presented in figures 9 and 10.

4nodes 8nodes 12nodes 16nodes

IBMSystemssolutionforSAPHANA

Servers 363,121 688,292 1,013,464 1,338,635

Infrastructure 50,628 50,898 53,257 53,257

Facilities 41,012 82,024 123,036 164,168

TOTAL($) 454,761 821,214 1,189,757 1,556,060

CiscoAppliances+EMCArraysServers 218,290 434,736 651,182 867,629

Storage 187,219 374,438 561,658 748,877

Infrastructure 76,374 77,267 77,267 78,161

Facilities 74,687 149,374 224,061 298,748

TOTAL($) 556,570 1,035,815 1,514,168 1,993,415

CiscoSAPHANAAppliances+NetAppArrays

Servers 218,290 434,736 651,182 867,629

Storage 214,290 428,580 642,870 857,160

Infrastructure 61,746 61,746 62,639 62,639

Facilities 66,486 132,972 199,458 265,944

TOTAL($) 560,812 1,058,034 1,556,149 2,053,372


Servers 254,833 509,666 764,590 1,019,453

Storage 218,467 364,110 509,764 655,460

Infrastructure 82,487 86,399 90,312 106,223

Facilities 88,301 176,602 264,903 353,204

TOTAL($) 644,088 1,136,777 1,629,569 2,134,340

Figure 9: Cost Breakdowns (1)




4nodes 8nodes 12nodes 16nodes

IBMSystemssolutionforSAPHANA

Hardware&maintenance 250,399 450,440 652,571 852,342

Softwarelicenses&support 163,350 288,750 414,150 539,550

Facilities 41,012 82,024 123,036 164,168

TOTAL($) 454,761 821,214 1,189,757 1,556,060

CiscoAppliances+EMCArrays

Hardware&maintenance 471,678 866,032 1,259,493 1,653,847


Facilities 74,687 149,374 224,061 298,748

TOTAL($) 556,570 1,035,816 1,514,168 1,993,414

CiscoSAPHANAAppliances+NetAppArrays



Facilities 66,486 132,972 199,458 265,944

TOTAL($) 560,812 1,058,034 1,556,150 2,053,372




Facilities 88,301 176,602 264,903 353,204

TOTAL($) 644,088 1,136,778 1,629,568 2,134,341

Figure 10: Cost Breakdowns (2)



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