The document describes plans to establish an ArcGIS Server development environment to help the e2M GIS team develop web mapping applications and pursue client projects. It discusses selecting a two-tier or three-tier system architecture based on bandwidth, hardware, and licensing requirements. The development environment will not be a production environment due to limitations but will provide experience. A phased deployment approach is recommended to reduce risks.
Collaboration by individuals, organizations, and communities with the right tools and resources is essential in achieving success with data science. Join us for a live demonstration of how you can leverage a data science platform, an open-source model, internal and external data, analytics tools, and visualization using Hadoop. See how unprecedented access to data scientists can deliver entirely new levels of insight to push the boundaries of what’s possible. Find out what you can do NOW to move your data science efforts forward.
Kumaran Systems Inc.
Abstract:
In the present situation most of the application are being developed or migrated to the web. With this scenario in mind, Kumaran Systems Inc., ventures into R&D on migrating COBOL application to J2EE Framework. This paper describes the preliminary work done to achieve this conversion.
Collaboration by individuals, organizations, and communities with the right tools and resources is essential in achieving success with data science. Join us for a live demonstration of how you can leverage a data science platform, an open-source model, internal and external data, analytics tools, and visualization using Hadoop. See how unprecedented access to data scientists can deliver entirely new levels of insight to push the boundaries of what’s possible. Find out what you can do NOW to move your data science efforts forward.
Kumaran Systems Inc.
Abstract:
In the present situation most of the application are being developed or migrated to the web. With this scenario in mind, Kumaran Systems Inc., ventures into R&D on migrating COBOL application to J2EE Framework. This paper describes the preliminary work done to achieve this conversion.
A Multi Criteria Recommendation Engine Model for Cloud Renderfarm Services IJECEIAES
Cloud services that provide a complete platform for rendering the animation files using the resources in the cloud are known as cloud renderfarm services. This work proposes a multi criteria recommendation engine model for recommending these Cloud renderfarm services to animators. The services are recommended based on the functional requirements of the animation file to be rendered like the rendering software, plug-in required etc and the non functional Quality of Service (QoS) requirements like render node cost, time taken to upload animation files etc. The proposed recommendation engine model uses a domain specific ontology of renderfarm services to identify the right services that could satisfy the functional requirements of the user and ranks the identified services using the popular Multi Criteria Decision Analysis method like Simple Additive Weighting (SAW). The ranked list of services is provided as recommended services to the animators in the ranking order. The Recommendation model was tested to rank and recommend the cloud renderfarm services in multi criteria requirements by assigning different QoS criteria weight for each scenario. The ranking based recommendations were generated for six different scenarios and the results were analyzed. The results show that the services recommended for each scenario were different and were highly dependent on the weights assigned to each criterion.
Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...Prolifics
Abstract: Recent projects have stressed the "need for speed" while handling large amounts of data, with near zero downtime. An analysis of multiple environments has identified optimizations and architectures that improve both performance and reliability. The session covers data gathering and analysis, discussing everything from the network (multiple NICs, nearby catalogs, high speed Ethernet), to the latest features of extreme scale. Performance analysis helps pinpoint where time is spent (bottlenecks) and we discuss optimization techniques (MQ tuning, IIB performance best practices) as well as helpful IBM support pacs. Log Analysis pinpoints system stress points (e.g. CPU starvation) and steps on the path to near zero downtime.
E-Gas Sewa is always committed to provide high quality supply of LPG gas and continuously develop its facilities .
This helps to achieve the excellence in securing reliable services that would meet the growing demand and exceed the future needs and expectations of our customers.
Several e-facilities such as the on-line bill payments and payments through the banks were introduced to provide more convenient services for the customers.
This is the research paper on the recent innovation on realtime system. realtime operating system is the system in which deadline is also considered along with the logical correctness. It can be beneficial for the students of realtime operating system
Developing multithreaded database application using java tools and oracle dat...csandit
In many business organizations, database applicatio
ns are designed and implemented using
various DBMS and Programming Languages. These appli
cations are used to maintain
databases for the organizations. The organization
departments can be located at different
locations and can be connected by intranet environm
ent. In such environment maintenance of
database records become an assignment of complexity
which needs to be resolved. In this paper
an intranet application is designed and implemented
using Object-Oriented Programming
Language Java and Object-Relational Database Manage
ment System Oracle in multithreaded
Operating System environment.
DEVELOPING MULTITHREADED DATABASE APPLICATION USING JAVA TOOLS AND ORACLE DAT...cscpconf
In many business organizations, database applications are designed and implemented using
various DBMS and Programming Languages. These applications are used to maintain
databases for the organizations. The organization departments can be located at different
locations and can be connected by intranet environment. In such environment maintenance of
database records become an assignment of complexity which needs to be resolved. In this paper
an intranet application is designed and implemented using Object-Oriented Programming
Language Java and Object-Relational Database Management System Oracle in multithreaded
Operating System environment
A Multi Criteria Recommendation Engine Model for Cloud Renderfarm Services IJECEIAES
Cloud services that provide a complete platform for rendering the animation files using the resources in the cloud are known as cloud renderfarm services. This work proposes a multi criteria recommendation engine model for recommending these Cloud renderfarm services to animators. The services are recommended based on the functional requirements of the animation file to be rendered like the rendering software, plug-in required etc and the non functional Quality of Service (QoS) requirements like render node cost, time taken to upload animation files etc. The proposed recommendation engine model uses a domain specific ontology of renderfarm services to identify the right services that could satisfy the functional requirements of the user and ranks the identified services using the popular Multi Criteria Decision Analysis method like Simple Additive Weighting (SAW). The ranked list of services is provided as recommended services to the animators in the ranking order. The Recommendation model was tested to rank and recommend the cloud renderfarm services in multi criteria requirements by assigning different QoS criteria weight for each scenario. The ranking based recommendations were generated for six different scenarios and the results were analyzed. The results show that the services recommended for each scenario were different and were highly dependent on the weights assigned to each criterion.
Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...Prolifics
Abstract: Recent projects have stressed the "need for speed" while handling large amounts of data, with near zero downtime. An analysis of multiple environments has identified optimizations and architectures that improve both performance and reliability. The session covers data gathering and analysis, discussing everything from the network (multiple NICs, nearby catalogs, high speed Ethernet), to the latest features of extreme scale. Performance analysis helps pinpoint where time is spent (bottlenecks) and we discuss optimization techniques (MQ tuning, IIB performance best practices) as well as helpful IBM support pacs. Log Analysis pinpoints system stress points (e.g. CPU starvation) and steps on the path to near zero downtime.
E-Gas Sewa is always committed to provide high quality supply of LPG gas and continuously develop its facilities .
This helps to achieve the excellence in securing reliable services that would meet the growing demand and exceed the future needs and expectations of our customers.
Several e-facilities such as the on-line bill payments and payments through the banks were introduced to provide more convenient services for the customers.
This is the research paper on the recent innovation on realtime system. realtime operating system is the system in which deadline is also considered along with the logical correctness. It can be beneficial for the students of realtime operating system
Developing multithreaded database application using java tools and oracle dat...csandit
In many business organizations, database applicatio
ns are designed and implemented using
various DBMS and Programming Languages. These appli
cations are used to maintain
databases for the organizations. The organization
departments can be located at different
locations and can be connected by intranet environm
ent. In such environment maintenance of
database records become an assignment of complexity
which needs to be resolved. In this paper
an intranet application is designed and implemented
using Object-Oriented Programming
Language Java and Object-Relational Database Manage
ment System Oracle in multithreaded
Operating System environment.
DEVELOPING MULTITHREADED DATABASE APPLICATION USING JAVA TOOLS AND ORACLE DAT...cscpconf
In many business organizations, database applications are designed and implemented using
various DBMS and Programming Languages. These applications are used to maintain
databases for the organizations. The organization departments can be located at different
locations and can be connected by intranet environment. In such environment maintenance of
database records become an assignment of complexity which needs to be resolved. In this paper
an intranet application is designed and implemented using Object-Oriented Programming
Language Java and Object-Relational Database Management System Oracle in multithreaded
Operating System environment
1. System Architecture Design Strategies for the e²M GIS Business Area’s
ESRI ArcGIS Server Development Environment
Purpose
The ArcGIS Server development environment will provide the e2M GIS team with the appropriate
development platform and the necessary experience to effectively and efficiently develop web mapping
application prototypes. In addition, this development environment will facilitate the building and
improvement of the skill sets required to pursue and win projects involving the design, development,
implementation, and maintenance of ArcGIS Server web sites for client use. The development
environment is designed based on recommendations from ESRI and represents the building blocks
required to deploy applications at client sites.
Business Case
In an effort to increase e2M’s marketability and service line offerings, the GIS Business Area has
developed system architecture strategies and virtualized the network infrastructure required to house
an ArcGIS Server development environment. ESRI’s ArcGIS Server is a leading geospatial technology that
allows organizations to distribute maps and GIS capabilities via Web mapping applications and services
to improve internal workflows, communicate vital issues, and engage stakeholders.
Many government and/or private sector entities are either converting to ArcGIS Server from previous
web mapping software (e.g. ArcIMS) or developing new web applications to display and analyze their
geospatial data and other web services (e.g. Google Maps, MS Virtual Earth). ArcGIS Server allows for
better internal coordination by providing users with the ability to view pertinent geospatial data in a
web based tool rather than accessing data through a more complex desktop application. The proposed
ArcGIS Server system design would support the ability to create optimized web mapping applications
that provide clients access to vital project-specific geospatial information.
System Architecture Design
The GIS team worked through a system design methodology aided by ESRI’s Capacity Planning Tool to
identify current issues and necessary changes to implement the proposed development environment.
The system design methodology recognizes that people, application technology, and selected data
sources are equally important in determining the optimum hardware solution. Figure 1 shows the basic
flow of the methodology and its primary components.
Knowlton, Richardson, Smiley June 17, 2009 Page 1
2. Figure 1: System architecture design process.
Process Components
The process components of system architecture design are people, applications, data resources, and
hardware. After determining the network limitations, the GIS team identified software and hardware
requirements for system architecture scenarios. Investment in hardware and network components
based on a balanced system design model provides the highest possible system performance at the
lowest overall cost.
• People: comprised of internal (e2M employees) and external (clients) users. Understanding user
needs, information products, and the procedures for making them establish a rationale for
establishing peak system workflow loads.
• Applications: software technology determines processing requirements (system loads) that must
be handled by the hardware solution.
• Data: the type of data source and what’s required to access it (data access requirements) shows
you how the processing load is distributed across the system architecture.
Knowlton, Richardson, Smiley June 17, 2009 Page 2
3. Balanced System Design
In Figure 2, the chain represents the factors that are linked in a system and therefore affect the system
performance. User workflows must be designed to optimize interactive client productivity. Work
request queues should be established to manage heavy batch processing loads and enhance user
productivity. The overall intent is to develop an efficient system that increases user productivity through
time and cost savings. The proposed development environment is designed with this intent in mind.
Figure 2: A balanced system load model leads to the highest system performance at the lowest overall cost.
Current Limitations
The primary limitation to the proposed infrastructure design is the network bandwidth, with the Fairfax
office being the biggest concern. The current bandwidth available for Fairfax is 1.5 Mbps and this is not
adequate for hosting GIS web services. The low bandwidth causes increased latency and display times,
essentially making web services very slow. The Denver office has 6 Mbps of available bandwidth which is
adequate for web service hosting. The bandwidth limitations require that the ArcGIS Server GIS web
services be hosted on Denver servers until bandwidth limitation are resolved in Fairfax.
ESRI ArcGIS Server System Architectures
Two-Tier Solution
The two-tier architecture (Figure 3) provides the best solution for an environment with a separate
database server. The two-tier solution includes GIS server and data server platforms. The Web server
and GIS server components are located on the GIS server platform, while the data server is on a
separate data server platform. This is a good configuration for sites with large volumes of data resources
or existing data servers.
Knowlton, Richardson, Smiley June 17, 2009 Page 3
4. Figure 3: Two-tier platform configuration
Three-Tier Solution
The three-tier configuration includes a single Web server with a separate map server/container machine
layer and a separate data server. The map server/container machine layer can be a single platform or
can be expanded to include several platforms, depending on the required site capacity. SOM load
balancing is provided by the GIS Web server service manager and multiple SOC instances can be
deployed to support optimum capacity requirements.
Figure 4: Three-tier platform configuration
Knowlton, Richardson, Smiley June 17, 2009 Page 4
5. Licensing Requirements
e2M’s current licensing stack is built using a host of development licenses. Specifically, an ESRI EDN
license consisting of development licenses for ArcGIS Server Advanced Enterprise Edition with 3D and
Image Server extensions, ArcSDE, and ArcGIS Info Workstation. In addition, e2M is also utilizing a
Microsoft Enterprise Agreement allowing use of development licenses for SQL Server 2008, Visual Studio
2008, and Microsoft Virtualization.
Conclusions
1. The e²M development environment is not designed and should not be implemented as a
production environment because of 1) hardware and bandwidth limitations, 2) licensing
restrictions, and 3) low availability and backup systems.
2. The e²M ArcGIS Server working group recommends entering a three-phase deployment strategy
consisting of 1) a pilot phase, 2) an initial production phase, and 3) a final implementation phase
in order to reduce implementation risk and allow for adequate skill development.
3. The goals and objectives of a web mapping site (i.e. process versus data) should dictate the
appropriate API when designing and developing an ArcGIS Server application, e.g. SOAP (.NET,
Java) versus REST (Flex, Javascript API’s).
4. A large-scale deployment of ArcGIS Server that is intended to support significant numbers of
users with numerous displays per minute requires large bandwidths typically only available
through data centers, i.e. colocation facilities.
5. The e²M ArcGIS Server working group recommends a three-tier stack consisting of Microsoft
SQL Server 2008 (database server), ArcGIS Server 9.3.1 (container machine/SOC), and a web
application server running IIS.
6. The e2M GIS team currently has the know-how and skill sets required to stand-up and support
LAN-based web applications capable of supporting company-wide project efforts.
Action Items
1. Install ArcGIS Server 9.3.1, ArcSDE, and MS SQL Server to establish our development
environment in Denver.
2. Procure a license and develop a proficiency in Adobe Flex so we can develop RESTful ArcGIS
Server applications.
3. Catalog and price a number of deployment scenarios in order to have an understanding of the
range of magnitude associated with specific ArcGIS Server configurations, e.g. two versus three-
tier deployments with and without redundancies and backups.
4. Demonstrate balanced system design by implementing best practices by following
recommended ESRI system performance tuning criteria.
5. Determine hardware specifications and bandwidths available at HDR’s Innovation Center.
6. Investigate current available hardware to support LAN-based web applications in a production
level environment.
Knowlton, Richardson, Smiley June 17, 2009 Page 5
6. Capacity Planning Tool (CPT) Analysis
SOAP vs REST
Requirements Analysis WEB TPH => WEB Users Network Unique 4 2
Types of Workflows
User Requirements 12,000 33 Bandwidth Display User Workflow Blink
Performance Summary Adjusted Platform and Network service times
Peak Workflow Network Mbps Traffic Think R_time Batch 0.01 Desktop Network Desktop Web Server Data
Workflow Data Source Users DPM/Client DPM Mbps DPH 12
3.5 Time sec DPM Latency Client Latency NWQ Xport WTQ WTS WAQ WA SSQ SS DBQ DBMS
Client
Local Clients 3 sec 1,000.0 Mbpd 100000 0.00001 0.010
1_ArcGIS Desktop Desktop SDE DC/DBMS 10
3.0 6.0 1000.0 10.01
NW Latency
2b_ArcGIS WTS/Citrix (w/image) WTS SDE DC/DBMS 10 6.0 1000.0 10.01 NWQ
3a_AGS9.3 AJAXlight Dynamic Server SDE DC/DBMS 6 2.5 10.0 1000.0 6.01 Network
WAN Clients 1.500 0.00002 0.000
Performance (sec)
WTSQ
1_ArcGIS Desktop Desktop SDE DC/DBMS 10 2.0 6.00 1000.0 10.01
WTS
3b_AGS9.3 AJAXMedium Dynamic Server SDE DC/DBMS 6 10.0 1000.0 6.01
3d_AGS9.3 AJAXlight Fully Cache Server SDE DC/DBMS 6 1.5 10.0 1000.0 6.01 Web Svr Q
Internet Clients Internet Clients = 4 Internet =0.8 Mbps 1.500 0.53 0.00003 0.000 0.000 Web Server
3b_AGS9.3 AJAXMedium Dynamic Server SDE DC/DBMS 4 6 24 0.800 1,440 1.0 6.7 3.29 18.2 6.01 0.089 1.333 1.680 0.187
App Svr Q
3c_AGS9.3 (Web Service) Server SDE DC/DBMS 6 10.0 1000.0 6.01
3e_AGS9.3 (REST API) Server SDE DC/DBMS 6 0.5 10.0 1000.0 6.01 App Svr
7a_ArcGIS Image Server Medium (preliminary) Server SDE DC/DBMS 6 10.0 1000.0 6.01 DBQ
8_AGS9.3 Globe Server (preliminary) Server SDE DC/DBMS 6 0.0 10.0 1000.0 6.01
Database
9_AGS9.3 Mobile ADF AGS Services Server SDE DC/DBMS 6 10.0 1000.0 6.01
3.0.11
Internet
1.0.1
1.0.2
1.0.3
2.0.4
2.0.5
2.0.6
3.0.7
3.0.8
3.0.9
3.0.10
3.0.12
3.0.13
LAN --
WAN --
2_Batch Reconcile and Post Process Server SDE DC/DBMS 6 10.0 1000.0 6.01
1.500 1.11E-05 0.000
Total Workload 4 0.800
1 Mbps Cores = 4 Minimal
Container Machines Intel Platform 8 GB RAM / Node SRint2006 WEBtier+SOC 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
WEBtier+SOC Arc08 Xeon E5335 4 core (1 chip) 2000(8) MHz 36.0 SOC Container Machines
Average DPM/client .................................................. 6.0 DPM 0.864 Cores = 4 Chips = 1 4 9.0/Core
Total Required .......................................................... 24 DPM Service Times Capacity Multiple Platforms Fix
CPU Utilization ...................................................... 17% CPU 4 Users Seconds Display/Min Nodes Capacity Nodes
Peak Users .............................................................. 23.8 23.8 / Node 1.680 143 1 143 1.0
Recommend 64bit Operating System 2 Mbps 2 Mbps 8,571 TPH 8,571 TPH 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Database Server Intel Platform 12 GB RAM / Node SRint2006
Arc08 Xeon E5335 4 core (1 chip) 2000(8) MHz 36.0 Database Server
Average DPM/client .................................................. 6.0 DPM 0.096 Cores = 4 Chips = 1 4 9.0/Core
Total Required .......................................................... 24 DPM Service Times Capacity Multiple Platforms Fix
CPU Utilization ........................................................ 2% CPU 4 Users Seconds Display/Min Nodes Capacity Nodes
Peak Users ............................................................ 214.3 214.3 / Node 0.187 1,286 1 1,286 1 1.0
Recommend 64bit Operating System 77,143 TPH 77,143 TPH 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
SOAP w 4 core, 1chip, 2 Ghz
Bandwidth (Mbps): 1.5
CPU Utilization: 17%
Performance: 3.3 sec
Maximum Users: 4
Requirements Analysis WEB TPH => WEB Users Network Unique 4 2
Types of Workflows
User Requirements 12,000 33 Bandwidth Display User Workflow Blink
Performance Summary Adjusted Platform and Network service times
Peak Workflow Network Mbps Traffic Think R_time Batch 0.01 Desktop Network Desktop Web Server Data
Workflow Data Source Users DPM/Client DPM Mbps DPH 12
2.5 Time sec DPM Latency Client Latency NWQ Xport WTQ WTS WAQ WA SSQ SS DBQ DBMS
Client
Local Clients 3 sec 1,000.0 Mbpd 100000 0.00001 0.010
NW Latency
1_ArcGIS Desktop Desktop SDE DC/DBMS 10 6.0 1000.0 10.01
2.0
2b_ArcGIS WTS/Citrix (w/image) WTS SDE DC/DBMS 10 6.0 1000.0 10.01 NWQ
3a_AGS9.3 AJAXlight Dynamic Server SDE DC/DBMS 6 10.0 1000.0 6.01 Network
WAN Clients 1.500 0.00002 0.000
Performance (sec)
1.5 WTSQ
1_ArcGIS Desktop Desktop SDE DC/DBMS 10 6.00 1000.0 10.01
WTS
3b_AGS9.3 AJAXMedium Dynamic Server SDE DC/DBMS 6 10.0 1000.0 6.01
3d_AGS9.3 AJAXlight Fully Cache Server SDE DC/DBMS 6
1.0 10.0 1000.0 6.01 Web Svr Q
Internet Clients Internet Clients = 4 Internet =0.8 Mbps 1.500 0.53 0.00003 0.000 0.000 Web Server
3b_AGS9.3 AJAXMedium Dynamic Server SDE DC/DBMS 6 10.0 1000.0 6.01
App Svr Q
3c_AGS9.3 (Web Service) Server SDE DC/DBMS 6 0.5 10.0 1000.0 6.01
3e_AGS9.3 (REST API) Server SDE DC/DBMS 4 6 24 0.800 1,440 7.8 2.22 27.0 6.01 0.089 1.333 App Svr 0.709 0.093
7a_ArcGIS Image Server Medium (preliminary) Server SDE DC/DBMS 6 10.0 1000.0 6.01 DBQ
8_AGS9.3 Globe Server (preliminary) Server SDE DC/DBMS 6 0.0 10.0 1000.0 6.01
Database
9_AGS9.3 Mobile ADF AGS Services Server SDE DC/DBMS 6 10.0 1000.0 6.01
3.0.11
Internet
1.0.1
1.0.2
1.0.3
2.0.4
2.0.5
2.0.6
3.0.7
3.0.8
3.0.9
3.0.10
3.0.12
3.0.13
LAN --
WAN --
2_Batch Reconcile and Post Process Server SDE DC/DBMS 6 10.0 1000.0 6.01
1.500 1.11E-05 0.000
Total Workload 4 0.800
1 Mbps Cores = 4 Minimal
Container Machines Intel Platform 8 GB RAM / Node SRint2006 WEBtier+SOC 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
WEBtier+SOC Arc08 Xeon E5335 4 core (1 chip) 2000(8) MHz 36.0 SOC Container Machines
Average DPM/client .................................................. 6.0 DPM 0.365 Cores = 4 Chips = 1 4 9.0/Core
Total Required .......................................................... 24 DPM Service Times Capacity Multiple Platforms Fix
CPU Utilization ........................................................ 7% CPU 4 Users Seconds Display/Min Nodes Capacity Nodes
Peak Users .............................................................. 56.4 56.4 / Node 0.709 338 1 338 1.0
Recommend 64bit Operating System 2 Mbps 2 Mbps 20,301 TPH 20,301 TPH 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Database Server Intel Platform 12 GB RAM / Node SRint2006
Arc08 Xeon E5335 4 core (1 chip) 2000(8) MHz 36.0 Database Server
Average DPM/client .................................................. 6.0 DPM 0.048 Cores = 4 Chips = 1 4 9.0/Core
Total Required .......................................................... 24 DPM Service Times Capacity Multiple Platforms Fix
CPU Utilization ........................................................ 1% CPU 4 Users Seconds Display/Min Nodes Capacity Nodes
Peak Users ............................................................ 428.6 428.6 / Node 0.093 2,571 1 2,571 1 1.0
Recommend 64bit Operating System 154,286 TPH 154,286 TPH 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
REST w 4 core, 1chip, 2 Ghz
Bandwidth (Mbps): 1.5
CPU Utilization: 7%
5/28/2009 Performance: 2.2 sec pg 1
Maximum Users: 4
7. Capacity Planning Tool (CPT) Analysis
SOAP vs REST
Requirements Analysis WEB TPH => WEB Users Network Unique 4 2
Types of Workflows
User Requirements 12,000 33 Bandwidth Display User Workflow Blink
Performance Summary Adjusted Platform and Network service times
Peak Workflow Network Mbps Traffic Think R_time Batch 0.01 Desktop Network Desktop Web Server Data
Workflow Data Source Users DPM/Client DPM Mbps DPH 12
2.5 Time sec DPM Latency Client Latency NWQ Xport WTQ WTS WAQ WA SSQ SS DBQ DBMS
Client
Local Clients 3 sec 1,000.0 Mbpd 100000 0.00001 0.010 0.000
NW Latency
1_ArcGIS Desktop Desktop SDE DC/DBMS 10 6.0 1000.0 10.01
2.0
2b_ArcGIS WTS/Citrix (w/image) WTS SDE DC/DBMS 10 6.0 1000.0 10.01 NWQ
3a_AGS9.3 AJAXlight Dynamic Server SDE DC/DBMS 6 10.0 1000.0 6.01 Network
WAN Clients 1.500 0.00002 0.000 0.000
Performance (sec)
1.5 WTSQ
1_ArcGIS Desktop Desktop SDE DC/DBMS 10 6.00 1000.0 10.01
WTS
3b_AGS9.3 AJAXMedium Dynamic Server SDE DC/DBMS 6 10.0 1000.0 6.01
3d_AGS9.3 AJAXlight Fully Cache Server SDE DC/DBMS 6
1.0 10.0 1000.0 6.01 Web Svr Q
Internet Clients Internet Clients = 12 Internet =2.4 Mbps 5.000 0.48 0.00003 0.000 Web Server
0.000
3b_AGS9.3 AJAXMedium Dynamic Server SDE DC/DBMS 12 6 72 2.400 4,320 7.7 2.28 26.3 6.01 0.400 0.013 1.680 0.187
App Svr Q
3c_AGS9.3 (Web Service) Server SDE DC/DBMS 6 0.5 10.0 1000.0 6.01
3e_AGS9.3 (REST API) Server SDE DC/DBMS 6 10.0 1000.0 6.01 App Svr
7a_ArcGIS Image Server Medium (preliminary) Server SDE DC/DBMS 6 10.0 1000.0 6.01 DBQ
8_AGS9.3 Globe Server (preliminary) Server SDE DC/DBMS 6 0.0 10.0 1000.0 6.01
Database
9_AGS9.3 Mobile ADF AGS Services Server SDE DC/DBMS 6 10.0 1000.0 6.01
3.0.11
Internet
1.0.1
1.0.2
1.0.3
2.0.4
2.0.5
2.0.6
3.0.7
3.0.8
3.0.9
3.0.10
3.0.12
3.0.13
LAN --
WAN --
2_Batch Reconcile and Post Process Server SDE DC/DBMS 6 10.0 1000.0 6.01
1.500 1.11E-05 0.000 0.000
Total Workload 12 2.400
2 Mbps Cores = 4 Minimal
Container Machines Intel Platform 8 GB RAM / Node SRint2006 WEBtier+SOC 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
WEBtier+SOC Arc08 Xeon E5335 4 core (1 chip) 2000(8) MHz 36.0 SOC Container Machines
Average DPM/client .................................................. 6.0 DPM 0.864 Cores = 4 Chips = 1 4 9.0/Core
Total Required .......................................................... 72 DPM Service Times Capacity Multiple Platforms Fix 50%
CPU Utilization ...................................................... 50% CPU 12 Users Seconds Display/Min Nodes Capacity Nodes +
Peak Users .............................................................. 23.8 23.8 / Node 1.680 143 1 143 1.0
Recommend 64bit Operating System 6 Mbps 6 Mbps 8,571 TPH 8,571 TPH 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Database Server Intel Platform 12 GB RAM / Node SRint2006
Arc08 Xeon E5335 4 core (1 chip) 2000(8) MHz 36.0 Database Server
Average DPM/client .................................................. 6.0 DPM 0.096 Cores = 4 Chips = 1 4 9.0/Core
Total Required .......................................................... 72 DPM Service Times Capacity Multiple Platforms Fix
CPU Utilization ........................................................ 6% CPU 12 Users Seconds Display/Min Nodes Capacity Nodes
Peak Users ............................................................ 214.3 214.3 / Node 0.187 1,286 1 1,286 1 1.0
Recommend 64bit Operating System 77,143 TPH 77,143 TPH 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
SOAP w 4 core, 1chip, 2 Ghz
Bandwidth (Mbps): 5.0
CPU Utilization: 50%
Performance: 2.3 sec
Maximum Users: 12
Requirements Analysis WEB TPH => WEB Users Network Unique 4 2
Types of Workflows
User Requirements 12,000 33 Bandwidth Display User Workflow Blink
Performance Summary Adjusted Platform and Network service times
Peak Workflow Network Mbps Traffic Think R_time Batch 0.01 Desktop Network Desktop Web Server Data
Workflow Data Source Users DPM/Client DPM Mbps DPH 12
1.4 Time sec DPM Latency Client Latency NWQ Xport WTQ WTS WAQ WA SSQ SS DBQ DBMS
Client
Local Clients 3 sec 1,000.0 Mbpd 100000 0.00001 0.010
1_ArcGIS Desktop Desktop SDE DC/DBMS 10
1.2 6.0 1000.0 10.01
NW Latency
2b_ArcGIS WTS/Citrix (w/image) WTS SDE DC/DBMS 10 6.0 1000.0 10.01 NWQ
3a_AGS9.3 AJAXlight Dynamic Server SDE DC/DBMS 6 1.0 10.0 1000.0 6.01 Network
WAN Clients 1.500 0.00002 0.000
Performance (sec)
WTSQ
1_ArcGIS Desktop Desktop SDE DC/DBMS 10 0.8 6.00 1000.0 10.01
WTS
3b_AGS9.3 AJAXMedium Dynamic Server SDE DC/DBMS 6 10.0 1000.0 6.01
3d_AGS9.3 AJAXlight Fully Cache Server SDE DC/DBMS 6 0.6 10.0 1000.0 6.01 Web Svr Q
Internet Clients Internet Clients = 12 Internet =2.4 Mbps 5.000 0.48 0.00003 0.000 Web Server
3b_AGS9.3 AJAXMedium Dynamic Server SDE DC/DBMS 6 0.4 10.0 1000.0 6.01
App Svr Q
3c_AGS9.3 (Web Service) Server SDE DC/DBMS 6 10.0 1000.0 6.01
3e_AGS9.3 (REST API) Server SDE DC/DBMS 12 6 72 2.400 4,320 0.2 8.8 1.20 49.9 6.01 0.400 App Svr 0.709 0.093
7a_ArcGIS Image Server Medium (preliminary) Server SDE DC/DBMS 6 10.0 1000.0 6.01 DBQ
8_AGS9.3 Globe Server (preliminary) Server SDE DC/DBMS 6 0.0 10.0 1000.0 6.01
Database
9_AGS9.3 Mobile ADF AGS Services Server SDE DC/DBMS 6 10.0 1000.0 6.01
3.0.11
Internet
1.0.1
1.0.2
1.0.3
2.0.4
2.0.5
2.0.6
3.0.7
3.0.8
3.0.9
3.0.10
3.0.12
3.0.13
LAN --
WAN --
2_Batch Reconcile and Post Process Server SDE DC/DBMS 6 10.0 1000.0 6.01
1.500 1.11E-05 0.000
Total Workload 12 2.400
2 Mbps Cores = 4 Minimal
Container Machines Intel Platform 8 GB RAM / Node SRint2006 WEBtier+SOC 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
WEBtier+SOC Arc08 Xeon E5335 4 core (1 chip) 2000(8) MHz 36.0 SOC Container Machines
Average DPM/client .................................................. 6.0 DPM 0.365 Cores = 4 Chips = 1 4 9.0/Core
Total Required .......................................................... 72 DPM Service Times Capacity Multiple Platforms Fix
CPU Utilization ...................................................... 21% CPU 12 Users Seconds Display/Min Nodes Capacity Nodes
Peak Users .............................................................. 56.4 56.4 / Node 0.709 338 1 338 1.0
Recommend 64bit Operating System 6 Mbps 6 Mbps 20,301 TPH 20,301 TPH 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Database Server Intel Platform 12 GB RAM / Node SRint2006
Arc08 Xeon E5335 4 core (1 chip) 2000(8) MHz 36.0 Database Server
Average DPM/client .................................................. 6.0 DPM 0.048 Cores = 4 Chips = 1 4 9.0/Core
Total Required .......................................................... 72 DPM Service Times Capacity Multiple Platforms Fix
CPU Utilization ........................................................ 3% CPU 12 Users Seconds Display/Min Nodes Capacity Nodes
Peak Users ............................................................ 428.6 428.6 / Node 0.093 2,571 1 2,571 1 1.0
Recommend 64bit Operating System 154,286 TPH 154,286 TPH 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
REST w 4 core, 1chip, 2 Ghz
Bandwidth (Mbps): 5.0
CPU Utilization: 21%
5/28/2009 Performance: 1.2 sec pg 2
Maximum Users: 12