209 - lisa binford - hpe sv cfd april 2016 · service virtualization source: voke market snapshot...
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HPE Service Virtualization Transform App Delivery Lifecycle with Speed and Agility
Lisa Binford – Solution Architect
HPE addresses the spectrum of ALM stakeholders
HPE ALM
Unified, Automated, Collaborative
Manual tester
Business analyst
Quality Assurance Developer (SAP/ORCL)
Developer (Java/.Net)
Mobile tester
Performance engineer
VP of AppsScrum Master
Functional test engineer
Unified Functional Testing
Sprinter
Agile ManagerIT Business Analytics
Performance Center
Mobile Center
70+ Developer Tool integrations
Requirements Management
Quality Center
Stakeholders use their tool of choice, while sharing resources with complete traceability for a unified ALM experience
Included with ALM
Separately Licensed
Business Process Testing
Network Virtualization
Service Virtualization
LoadRunner
StormRunner Load
Project and Portfolio ManagementProject Manager
Deliver amazing user experiences
Modern Application Development
Reduce costs
Increase customer attraction/retention
Increase the value of your brand
Get to market faster
* Source: “Enterprise Mobile Facts You Need to Know in 2015” by App Data Room.http://appdataroom.com/enterprise-mobile-facts-need-know-2015/
Mobile
Cloud
Dev Ops
Agile
Modern applications redefine application development
You can’t do anything until you have everything; and you never have everythingConsequences
84%QA work delayed while waiting
81%Development work delayed
while waitingSource: voke Market SnapshotTM Report: Service Virtualization – January 2015
?
Wait time Before Service Virtualization
On-demand (no wait) 0%
Seconds to minutes 0%
30 minutes to 2 hours 0%
4 to 7 hours 2%
1 day 1%
2 days 1%
3 days 9%
4 days 3%
1 week 8%
2 weeks 15%
3 weeks 27%
1 month 14%
2 months 10%
3 months 5%
4 to 6 months 3%
Never (no access ever) 2%
Source: voke Market SnapshotTM Report: Service Virtualization – January 2015
The consequence: crippling wait times
76% wait at least 2 weeks or more on systems
How long is your wait time?
What’s the impact on cost?
… lost opportunity?
Wait time Before Service Virtualization
On-demand (no wait) 0%
Seconds to minutes 0%
30 minutes to 2 hours 0%
4 to 7 hours 2%
1 day 1%
2 days 1%
3 days 9%
4 days 3%
1 week 8%
2 weeks 15%
3 weeks 27%
1 month 14%
2 months 10%
3 months 5%
4 to 6 months 3%
Never (no access ever) 2%
Source: voke Market SnapshotTM Report: Service Virtualization – January 2015
The consequence: crippling wait times
32 days
average wait time
The Solution?...Service Virtualization technology
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Service Virtualization:In software engineering, service virtualization is a method to emulate the behavior of specific components in heterogeneous component-based applications such as API-driven applications, cloud-based applications and service-oriented architectures.
Wait time Before Service Virtualization
After Service Virtualization
On-demand (no wait) 0% 27%
Seconds to minutes 0% 14%
30 minutes to 2 hours 0% 10%
4 to 7 hours 2% 17%
1 day 1% 11%
2 days 1% 10%
3 days 9% 8%
4 days 3% 1%
1 week 8% 1%
2 weeks 15% 1%
3 weeks 27% 0%
1 month 14% 0%
2 months 10% 0%
3 months 5% 0%
4 to 6 months 3% 0%
Never (no access ever) 2% 0%
From 32 days to 1 hour, by virtualizing services
1 hourmedian wait time afterservice virtualization
Source: voke Market SnapshotTM Report: Service Virtualization – January 2015
The concept: virtual services stand in when real services become inaccessible
Existing Infrastructure
Mainframe
JDBC
SOAP
RFC/IDOC
MQ/CICS
Third Party
Application Under Test
Mobile App
Web browser
Composite Application
API
SAP System
Existing database
Web service andLegacy application
RESTPay-per-transaction
Underconstruction
Service Virtualization
SOAPRESTJDBCMQRFCCICS
Simulation
Data
Perf.
Config
Agile Application Development
ApplicationBackend
Mainframe
CRM
3rd PartyPayment
RESTSVVirtual Claim
Service
SimulatedTransactions
Development/Design Documentation
Decreased Software Cycle23%
Performance Insights
3 challenges with 3 solutions in 1 simulated environmentPerformance Testing
ApplicationBackend
Mainframe
CRM
3rd PartyPayment
SVVirtual
MF
VirtualCRM
VirtualPay
Mobile App
Desktop App
Web App
NVNetworkCondit.
WiFi
3G4G
VPN
2.5G
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3
100K Users
Load Generators
Virtual users
Performance Center
Increased Test Coverage45%
Simulated, virtual reality helps to minimize the risk
Virtualize across application development and test
• Users (Virtual Users)• Network characteristics• Application Dependencies• Web services• Legacy systems
• Data everywhere Network characteristics
Constrained services/ application components
SV
NV
User behavior and load
Frontend/Backend DataDV
PCLR
UFTMC
SR
NV
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Compressing the software development lifecycle with service virtualization
Without service virtualization
With service virtualization
Development System Test Integration Performance Test UAT
Development
System
Integration
Performance
UATTime saved
Used by Participant percentagesQA – software QA or testing (functional, performance, security) 72%
Development – software developers 58%
QA – architects 39%
Development – architects 37%
Release engineering or management 32%
QA – managers 32%
Center of excellence (CoE) 27%
Consultants/professional services 23%
Development – managers 19%
IT – infrastructures 16%
IT – operations/production deployment 14%
IT – lab managers/lab engineers 13%
Training 10%
IT – patch management 6%
IT – system administrator 6%
IT - security 5%
Anyone on-demand 5%
IT - management 4%
Project management 4%
Support 4%
Sales 1%
Technical publications 1%
72%QA (functional,
performance, security)
58%Developers
Source: voke Market SnapshotTM Report: Service Virtualization – January 2015
Now, widespread adoption – far beyond QA
Easy set-up and use
“HPE SV provides ease of use and an enjoyable user experience…” – Forrester
Research, Service Virtualization Wave
Portfolio-wide integration
LOAD RUNNER*
SV SERVER
VS
SV MANAGEMENT UI
VS
Deploy Virtual Service
Open/SaveVirtual Service
UNIFIED FUNCTIONAL TESTING
VS
Deploy Virtual Service
SV DESIGNER
VS
Create, Update VS
APPLICATION UNDER TEST
SV Monitoring
Deploy/InitializeVirtual Service VS
UseVirtual Service
Open TestScript/Data
Test Call VS VS
VS
VS
VS
ALM
VS
Why customers use HPE Service VirtualizationRationale for adoption Participants
percentages
Improve time-to-market 68%
Test earlier in the lifecycle 54%
Enable continuous integration 52%
Performance testing 48%
Parallel development 47%
Scheduling constraints 40%
Restricted access to dependent services, components or applications 38%
Test data management 35%
Reduce production defects 34%
Reduce capital expenditures (CAPEX) 32%
Simulation of new software 31%
Reduce operational expenditures (OPEX) 31%
Third-party access fees 28%
Mobile developmentand testing 22%
Network constraints 18%
Simulation of hardware 14%
Replace an internally developed service virtualization solution 12%Source: voke Market Snaphot Report Service Virtualization – Jan 2015
How organizations successfully introduce Service Virtualization2015 SV Market survey shows..Nature of use Participant percentagesPilot project 22%
Project-based 38%
Departmental 31%
Available via Center of Excellence (CoE) 19%
Enterprise-wide 19%
Accessible to third party offshore teams 10%
Accessible to entire software supply chain including third-party partners and suppliers 6%
69%project and departmental
64%CoE or Enterprise-wide
16%third-party access
Typical customer use of Service Virtualization
Types of assets virtualized Participantspercentages
SOA/web service 71%
APIs 64%
Middleware 38%
Applications – legacy 36%
Mainframe 35%
Data – test data 34%
Data – databases 33%
External software – third-party software or services 33%
Applications – ERP/packaged 29%
Applications – new 29%
Architectures 17%
Lab environments 15%
Data – mobile 15%
71%SOA/web services
64%APIs
Types of assets virtualized Participantspercentages
Mobile – architectures 12%
External software – entire software supply chain 11%
Mobile – carrier networks 10%
User interfaces 10%
Mobile – devices 9%
Mobile – development platforms 8%
Mobile – user interfaces 7%
Network infrastructure 7%
Data – big data 6%
Operating systems 6%
Embedded systems 5%
Networked elements or appliances 4%
Mobile – operating systems 3%Source: voke Market Snaphot Report Service Virtualization – Jan 2015
ROI findings from latest service virtualization study 2014-15
Category Metric
Reduced defect reproduction 38% achieved a greater than 50% reduction in defect reproduction time
Reduced production defects 36% achieved a greater than 41% reduction in production defects
Reduced total defects 46% achieved greater than 41% reduction in total defects
Increased test coverage 20% achieved more than two times the test coverage
Increased test execution 26% achieved an increase of two times or greater of test execution rates
Reduced test cycle time 34% achieved a decrease of 50% or greater in test cycle time
Reduced software release cycle time 40% achieved a decrease of 40% or greater in software release cycle time
Source: voke Market Snaphot Report Service Virtualization – Jan 2015
Quick and Easy Virtual Service Creation
• Easy and Intuitive IDE• Embedded SV Runtime for local
use• Predefined Virtualization starting
points• Dialog based service creation• Data oriented Functional Modeling• Learning, Data Driving, Manual
Authoring (Request-Responses)• Performance and Scalability
Modeling• Simulation Logging and Preview• Pre-defined Technologies with
Extensibility SDK
Visual Modeling IDE Dialog Based
Wizards
Visual Data Modelling
Visual Performance
Modelling
Pre-packaged Technologies
Scalable Virtual Service Infrastructure with Shared Mgmt.
• Shared, Scalable and Secured Simulation Infrastructure • Web Based Virtual Service
Management Interface and Dashboard• Unified VS management across
multiple SV Server Nodes • Parameterized filtering and search• Provisioning and Control of Virtual
Environments• ACL management – Users/groups• Integrated to ALM/QC, VCS and
other repositories • Integrated with Enterprise Identity
System (LDAP)
Management and
Administration
Virtual Service
Provisioning and Control
Virtual Service and
ServerMetrics
SV Server Dashboard
UFT/MC/LR/PC/SR/NVDesigned for Use with HPE Testing Toolset
Virtual Services Real-
time Performance
Virtual Services Real-
time Performance
Test reports with Virtual
Service metrics stored
in ALM
Control VirtualizationFrom Inside UFT/LR/PC
Integrated with HPE ALM/QC, SVN, Jenkins
• Store Virtual Services with Development or Testing Assets• Version Control• Collaboration with Check-
out/Check-in support• Complete traceability • ALM integrated from SV
Designer, Server and SV Integration in Automation Tools
Virtual Service Projects
Versions, Revisions &
Dependencies
ALM & VCS Repositories in SV Designer
SV Management Integrated with
ALM/QC Repository
Virtual service management automationIntegrate 3rd party development tools, ides, build and Continuous integration systems
• Control Virtual Services from command line and external scripts using SVConfigurator tool
• Supports full Virtual Service lifecycle over API/Command Line
• Java based, multi OS, supporting ANT tasks• Deploy/un-deploy Virtual Services to/from any HPE SV Server
• Change mode of a Virtual Service (including Learning)
• View Virtual Service details and metrics;
• List/Export/Update deployed Virtual Services and Projects
• Unlock Virtual Service locked by another user
• JavaDoc like documentation
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Quarterly expanding Virtualization Protocols
TRANSPORT
HTTP(S)
Gateway
HTTP(S)
Proxy1
IBM WS
MQ1
JMS
JDBC1
IMS
Connect
CICS TS
TIBCO
Active
Matrix/
EMS1
SAP
NetWeave
rXI/PI,
ABAP
Oracle
AQMicroso
ft MQ
Web-
Methods
IS1
TCP/IP
JDK
(Beta)
MESSAGE
WS/SOAP ü ü ü ü ü ü ü ü ü
XML2 ü ü ü ü ü ü ü ü ü
REST (XML, JSON, Bin)
ü ü ü ü
Cobol ü ü ü ü
SQL ü ü ü ü
RFC/IDOC ü ü
Fix Length ü ü
Java Objects ü
Text ü ü ü ü ü ü ü ü ü ü ü ü ü
Binary ü ü ü ü ü ü ü ü ü ü ü ü ü
ü Protocol supported1 Non-intrusive2 All XML-based protocols supported
New Updated
SV Protocol Extensibility SDK Available
HTTP Virtu
alized Ser
vices
Invocatio
n
R&D Laptop6GB
Windows 7 x64 Windows Server 2008 R2 x64Intel® Xeon® X5660 2.8 GHz
2x6 Cores, Hyper-‐threading OFF32GB
DB Storage
Windows Server 2008 R2 x64Intel® Xeon® 5160 2.66 GHz
2x2 Cores16GB
4 x LoadRunner GeneratorWindows Server 2008 R2 x642 x Intel® Xeon® 5150 2.66GHz
8GB
Virtual Service Management
Con
nections
Storage Access
SV ServerSV Designer
Service Clients
SQL Server 2008 R2
IBM MQ 7.0.1.3Windows Server 2003 R2 x64Intel® Xeon® 5150 2.66 GHz
2x2 Cores8GB
Queue
Send./Rec.
Queue Send./Rec.
HPE Service Virtualization 3
Benchmark ObjectiveHPE Service Virtualization performance benchmark studiesmaximum number of transactions (requests) per second of HPEService Virtualization Server v3.00In this test 4*N clients are invoking two randomly chosen servicesfrom 200 deployed services in SV Server - where N is number ofSV Server CPU cores.
Figure 1. Deployment diagram.
Table 1. Benchmark characteristics.
DeploymentHPE Service Virtualization Server is installed on Intel Xeon X5660with 2x6 CPU Cores, 32GB memory machine. Database wasinstalled on Xeon 5160 with 2x2 CPU Cores, fixed 300GB 15k diskas a storage. To load virtualized services we used four HPE LoadRunner generators.
Parameter Value
Message structure 30 elements (average)
Deployed services 200
Concurrent clients 48
Service model size 1000 unique messages
Protocol HTTP/SOAP
Physical Server 1
Physical Server 2
4 x Physical Server
Laptop
Maximum Transactions Benchmark
ConclusionMaximum transactions benchmark test shows scalability of HPEService Virtualization Server. SV Server is capable of handling2900 TPS and 3700 TPS with in-memory simulation on 12 coreCPU Intel machine with linear grows of response time after fullCPU utilization.
ResultsHPE Service Virtualization Server version 3.00 simulates 2900 TPS(transactions per second) or 10,440.000 TPH (transactions per hour)in steady state with 48 parallel users (Figure 2)
In-memory and pre-loading of simulation data into memory enhancesperformance by up to 25%, depending on scenario.
HPE Service Virtualization Server performance improves to 3700TPS or 13,320.000 TPH with in-memory simulation enabled.
Figure 2. Number of transactions per second.
Figure 3. Virtual Service response time dependence on number of parallel users. Each color represents different service operation.
Figure 4. server CPU (red) and database CPU (green) utilization.
Response time is linearly dependent on number of parallel users(Figure 3) It goes down after a short warm up and l inear ly up fromabout 20 users. This linear growths is consequence of high CPUutilization This slowdown is typical for today’s server basedapplications.CPU utilization grows linearly until its 100% capacity at which pointslowdown of response time starts growing (Figure 4)SV Server CPU utilization is more significant than database CPUutilization, thus limiting factor for SV Server scalability is number ofserver CPU Cores.
2900 TPS with Database3700 TPS with In-memory Simulation
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SV core of the ADM solutionsResults delivered to you and your customers
ROLES
Agile Project Management
ALM with Requirements Definition and Management
Automated Functional and Performance Testing
Customizable Social Workflows
Network and ServiceVirtualization
DevelopmentManagement and Continuous Integration
Security Validation
Proven customer benefits:• 80% reduction of defects in production• 90% of resources focused on innovation• 100% elimination of resource wait time
SOLUTIONS
DevelopmentTest
A unified platform, fully integrated with heterogeneous DevTestOps solutions
• Extensible Repository• 360-degree 7-way traceability• End-to-end analytics• Shared data and processes• Enterprise proven results
OperationsBusiness
Enterprise System Architects
IT Security
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