This document details how to monitor key SOA performance metrics using Oracle Enterprise Manager. It describes metrics like response time, uptime, scalability, and infrastructure usage that are critical for monitoring SOA health. The metrics are collected from sources like Website Pulse and Oracle Grid Control. Response time is calculated from Website Pulse transactions, while uptime accounts for proportional SOA downtime. Scalability involves requests per second and connections across nodes. Infrastructure monitoring includes connection counts, CPU usage, and heap usage averaged across nodes.
This document discusses key metrics and measurements for service-oriented architecture (SOA). It covers management metrics related to business value and ROI, project metrics for monitoring development efforts, development metrics for service quality, and corporate metrics for overall SOA performance and benefits realization.
Solving big data challenges for enterprise applicationTrieu Dao Minh
This document discusses the challenges of application performance monitoring (APM) systems that deal with "big data". APM systems instrument enterprise applications to monitor metrics like response times and failures across distributed systems. This generates enormous amounts of monitoring data. The document evaluates six open-source data stores (Cassandra, HBase, Voldemort, Redis, VoltDB, MySQL Cluster) for their ability to handle the throughput of APM workloads in memory-bound and disk-bound cluster setups. It aims to provide performance results, lessons learned on setup complexity, and insights for using these data stores in an industrial APM system context.
This document describes the design and implementation of an online banking system for May Fresh Savings and Loans Bank at Caritas University, Enugu. It includes an introduction describing the increasing role of information technology in banking. The document then covers the existing manual banking system, proposed online system design including database, interface and security considerations. It describes testing of the online system and concludes with limitations, recommendations and conclusions from the project. Key aspects covered include database design, interface screens for administrators, tellers and customers, and integration and security testing of the online banking system.
IRJET- Web Scraping Techniques to Collect Bank Offer Data from Bank WebsiteIRJET Journal
The document discusses using web scraping techniques to collect bank offer data from websites. It describes how web scraping works by analyzing website content, extracting relevant data, and formatting it into a structured database or spreadsheet. The paper then presents the process used to scrape bank offer data from Indian websites, including developing a Python script to automate scraping, scheduling regular scraping, cleaning the extracted data, and transforming it into a standardized format for analysis. The results section demonstrates the web scraping process and shows how the extracted data is further transformed using an ETL process into a clean dataset for analytics purposes.
IRJET- Web Scraping Techniques to Collect Bank Offer Data from Bank WebsiteIRJET Journal
This document discusses using web scraping techniques to collect bank offer data from websites. It describes how an offer scavenger software can automate the extraction of relevant data from websites and organize it in a predefined format like a database. The document then provides details on how the researchers collected bank offer data from websites like centralbank.net.in using web scraping and Python libraries. It explains the data extraction, transformation and loading process to clean the scraped data and load it into a database. Some preliminary statistics are also generated from the collected data. Finally, it discusses some legal aspects of using web scraping techniques.
The document provides a software design description for a weather station database management system. It describes the system decomposition into five subsystems and the database subsystem's six components: API, Query Manager, Database Connection Manager, Unit Conversion, Notification Manager. It provides detailed descriptions of each database component, their classes, responsibilities, and contracts to manage interactions with the database.
Hospitals currently use a manual system for visiting Doctor Slip as a token. The current system
requires numerous paper forms, with data stores spread throughout the hospital management infrastructure.
Often information (on forms) is incomplete, or does not follow management standards. Forms are often lost
in transit between departments requiring a comprehensive auditing process to ensure that no vital
information is lost. Multiple copies of the same information exist in the hospital and may lead to
inconsistencies in data in various data stores.
A significant part of the operation of any hospital involves the acquisition, management and timely
retrieval of great volumes of information. This information typically involves; Doctor, Room, Department
and Patient personal Information. All of this information must be managed in an efficient and cost wise
fashion so that an institution's resources may be effectively utilized Hospital E-Token management will
automate the management of the hospital making it more efficient and error free for outdoor patient. It aims
at standardizing data, consolidating data ensuring data integrity and reducing inconsistencies.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document discusses key metrics and measurements for service-oriented architecture (SOA). It covers management metrics related to business value and ROI, project metrics for monitoring development efforts, development metrics for service quality, and corporate metrics for overall SOA performance and benefits realization.
Solving big data challenges for enterprise applicationTrieu Dao Minh
This document discusses the challenges of application performance monitoring (APM) systems that deal with "big data". APM systems instrument enterprise applications to monitor metrics like response times and failures across distributed systems. This generates enormous amounts of monitoring data. The document evaluates six open-source data stores (Cassandra, HBase, Voldemort, Redis, VoltDB, MySQL Cluster) for their ability to handle the throughput of APM workloads in memory-bound and disk-bound cluster setups. It aims to provide performance results, lessons learned on setup complexity, and insights for using these data stores in an industrial APM system context.
This document describes the design and implementation of an online banking system for May Fresh Savings and Loans Bank at Caritas University, Enugu. It includes an introduction describing the increasing role of information technology in banking. The document then covers the existing manual banking system, proposed online system design including database, interface and security considerations. It describes testing of the online system and concludes with limitations, recommendations and conclusions from the project. Key aspects covered include database design, interface screens for administrators, tellers and customers, and integration and security testing of the online banking system.
IRJET- Web Scraping Techniques to Collect Bank Offer Data from Bank WebsiteIRJET Journal
The document discusses using web scraping techniques to collect bank offer data from websites. It describes how web scraping works by analyzing website content, extracting relevant data, and formatting it into a structured database or spreadsheet. The paper then presents the process used to scrape bank offer data from Indian websites, including developing a Python script to automate scraping, scheduling regular scraping, cleaning the extracted data, and transforming it into a standardized format for analysis. The results section demonstrates the web scraping process and shows how the extracted data is further transformed using an ETL process into a clean dataset for analytics purposes.
IRJET- Web Scraping Techniques to Collect Bank Offer Data from Bank WebsiteIRJET Journal
This document discusses using web scraping techniques to collect bank offer data from websites. It describes how an offer scavenger software can automate the extraction of relevant data from websites and organize it in a predefined format like a database. The document then provides details on how the researchers collected bank offer data from websites like centralbank.net.in using web scraping and Python libraries. It explains the data extraction, transformation and loading process to clean the scraped data and load it into a database. Some preliminary statistics are also generated from the collected data. Finally, it discusses some legal aspects of using web scraping techniques.
The document provides a software design description for a weather station database management system. It describes the system decomposition into five subsystems and the database subsystem's six components: API, Query Manager, Database Connection Manager, Unit Conversion, Notification Manager. It provides detailed descriptions of each database component, their classes, responsibilities, and contracts to manage interactions with the database.
Hospitals currently use a manual system for visiting Doctor Slip as a token. The current system
requires numerous paper forms, with data stores spread throughout the hospital management infrastructure.
Often information (on forms) is incomplete, or does not follow management standards. Forms are often lost
in transit between departments requiring a comprehensive auditing process to ensure that no vital
information is lost. Multiple copies of the same information exist in the hospital and may lead to
inconsistencies in data in various data stores.
A significant part of the operation of any hospital involves the acquisition, management and timely
retrieval of great volumes of information. This information typically involves; Doctor, Room, Department
and Patient personal Information. All of this information must be managed in an efficient and cost wise
fashion so that an institution's resources may be effectively utilized Hospital E-Token management will
automate the management of the hospital making it more efficient and error free for outdoor patient. It aims
at standardizing data, consolidating data ensuring data integrity and reducing inconsistencies.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document discusses common performance testing mistakes and provides recommendations to avoid them. The five main "wrecking balls" that can ruin a performance testing project are: 1) lacking knowledge of the application under test, 2) not seeing the big picture and getting lost in details, 3) disregarding monitoring, 4) ignoring workload specification, and 5) overlooking software bottlenecks. The document emphasizes the importance of understanding the application, building a mental model to identify potential bottlenecks, and using monitoring to measure queues and resource utilization rather than just time-based metrics.
Article by Marlabs Bangalore employee receives international recognition!Marlabs
“Testing Experience”, a Germany-based online magazine for software testers and test managers, has published an article by Ramesh Viswanathan, Senior Test Architect, Marlabs Bangalore in their March 2013 edition. Ramesh has presented his observations on the topic ‘Need for Performance Requirements to Ensure Reliable Business Applications’ through this article. It has incorporated topics associated with application development for various industry domains, techniques and tools for non-functional requirements gathering, optimal performance target, and best practices for developing resource-specific outputs.
Testing Experience serves as a platform for knowledge transfer in software testing projects, with more than 250000 downloads per issue, in over fifty countries. Marlabs congratulates Ramesh Viswanathan for this accomplishment. We hope and wish that such valuable tech-philosophies lead him to better avenues of success in career!
For viewing the detailed version of Ramesh’s article, refer the following link - http://www.testingexperience.com/testingexperience21_03_13.pdf
This document is a curriculum vitae for Bikram Samaddar that includes his contact information, objective, technical experience summary, professional experience summary, relevant experience, and key projects. It summarizes his experience as a .NET programmer working on various web application projects over 9 years for companies like Cognizant, CMC Limited, Praxis Softek Solutions, and IPEG Solutions. The projects involved technologies like ASP.NET, C#, SQL Server, Oracle, and MVC frameworks.
An Analytical Framework Of A Deployment Strategy For Cloud Computing Services...Rachel Doty
This document proposes an analytical framework to analyze the costs and risks of deploying academic websites on either local servers or Google App Engine (GAE), a platform as a service (PaaS) cloud. The framework uses queueing theory and the analytical hierarchy process to evaluate costs and service level agreements. Network data from two university websites are analyzed to compare the costs and risks of local versus GAE deployment. Results found that deploying the websites on GAE could save over 83% of costs with low risk, suggesting GAE is a suitable deployment option.
Other requirements, requirement specification and mapcsk selva
The document discusses requirements for network design and engineering. It outlines three key characteristics that reflect a customer's needs: operational suitability, supportability, and confidence. Operational suitability measures how well a network can be configured and monitored by customers. Supportability measures how well a network can be maintained over its lifetime. Confidence measures a network's ability to deliver data without errors or losses. The document also discusses financial requirements, enterprise requirements, and the process of gathering and documenting network requirements from various sources.
The document discusses operation modes in an ERP application, including creating and setting up different modes to optimize system resources for varying user demand throughout the day. Key transactions are listed for creating, maintaining, and forcing different operation modes, as well as displaying work processes. References for additional information on operation modes are also provided.
Web Application Penetration Tests - Information Gathering StageNetsparker
This document discusses the information gathering phase of a web application penetration test using Netsparker. It describes how Netsparker crawls a target site to map its structure and identify vulnerabilities. Key steps include configuring scan settings such as authentication, URL rewriting rules, and crawling parameters. The results of an initial "crawl and wait" scan are presented, showing how Netsparker reveals technical details, comments, inputs, and existing vulnerabilities to provide visibility into the target application before further testing.
This document discusses performance tuning and optimization in client/server computing. It covers tuning performance at the server, client, database, and network levels. Some key points include:
- Upgrading server hardware, using multiple network cards, and high-performance file systems can improve server performance. Offloading processing to specialized servers also helps.
- Client performance is impacted by hardware, operating systems that support multitasking, and efficient applications.
- Database performance relies on normalized database design, efficient indexing, and optimized query design using WHERE clauses to limit returned records.
- Performance tuning follows steps like identifying bottlenecks, modifying systems, and remeasuring impact while performance optimization employs techniques to improve network usage and data retrieval speeds.
Exploring Neo4j Graph Database as a Fast Data Access LayerSambit Banerjee
This article describes the findings of an extensive investigative work conducted to explore the feasibility of using a Neo4j Graph Database to build a Fast Data Access Layer with near-real time data ingestion from the underlying source systems.
PS provides metrics on user experience for pages on mobile and desktop. It offers both lab and field data, with lab data from a controlled environment useful for debugging, and field data capturing real-world user experience. Field data comes from Chrome User Experience Report and includes metrics like First Contentful Paint. Lab data uses Lighthouse to analyze pages for performance and other categories like accessibility. Field and lab data can differ due to variability in networks, devices, and other conditions between real users and simulations.
This document discusses techniques for optimizing the performance of PeopleSoft applications. It covers tuning several aspects within a PeopleSoft environment, including server performance, web server performance, Tuxedo performance management, application performance, and database performance. Some key recommendations include implementing a methodology to monitor resource consumption without utilizing critical resources, ensuring load balancing strategies are sound, measuring historical patterns of server resource utilization, capturing key performance metrics for Tuxedo, and focusing on tuning high-resource consuming SQL statements and indexes.
This document summarizes the results of simulating a manufacturing system model with three parts: arrival of components, processing of components, and the main production process. The simulation found a bottleneck at machine 2, with the longest queue times. Several proposals to improve system performance were explored using a process analyzer tool, with scenario 2 found to spread queue times more evenly and reduce bottlenecks at a reasonable cost, and scenario 5 found to give the best results by further reducing wait times and balancing utilization across resources.
age 1Question 1.1. (TCO 1) An important aspect of a network mana.docxgalerussel59292
age 1
Question 1.1. (TCO 1) An important aspect of a network management system involves (Points : 5)
managing connectivity between all PDAs and mainframe systems.
collecting and analyzing data to support organizational business needs.
keeping track of some client/server applications.
keeping track of most multimedia applications.
both managing connectivity between all PDAs and mainframe systems and keeping track of some client/server applications.
Question 2.2. (TCO 1) One of the main goals of network management is to make operations more _____ and operators more productive. (Points : 5)
secure
stable
productive
efficient
Both productive and efficient
Question 3.3. (TCO 2) Which is not one of the four steps of the network management life cycle? (Points : 5)
Monitor
Operate
Decommission
Plan
Both monitor and plan
Question 4.4. (TCO 2) In the network management system, the interface between the manager and the network device is known as the _____. (Points : 5)
manager
agent
console
application programming interface (API)
NMS
Question 5.5. (TCO 2) Which is not a part of all management agents? (Points : 5)
The MIB
Core agent logic
Management interface
XML
Both core agent logic and XML
Question 6.6. (TCO 3) One of the key words found in the definition of network management is operation. Select the best definition of operation from the following choices. (Points : 5)
Performing repairs and upgrades and taking corrective and preventive proactive measures to make the network run better
Keeping the network up and running smoothly
Configuring resources in the network to support a given service
Keeping track of resources in the network and how they are assigned
None of the above
Question 7.7. (TCO 3) Select the name of the language used for the definition of management information used with SNMP. (Points : 5)
Guidelines for the definition of managed objects (GDMO)
Abstract syntax notation version 1 (ASN.1)
Managed object format (MOF)
Structure of management information (SMI)
Object protocol language for management (OPLMv2)
Question 8.8. (TCO 3) Which item does not fall under the configuration management functional responsibility? (Points : 5)
Configuring alarm-forwarding information
Reconciling the as-built network to the as-planned network
Managing software images running on the network
Synchronizing cached network configuration information with the network's actual configuration
Performing backup and restore operations
Question 9.9. (TCO 4) Which is not one of the reasons why polling a managed device for operational data and state information is generally done? (Points : 5)
Troubleshooting
Device viewing
Diagnostics
Device update
.
Troubleshooting Tips and Tricks for Database 19c - EMEA Tour Oct 2019Sandesh Rao
This session will focus on 19 troubleshooting tips and tricks for DBA's covering tools from the Oracle Autonomous Health Framework (AHF) like Trace file Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feel like a rockstar DBA
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...AM Publications
Cloud computing means storing and accessing data and programs over the Internet instead of your computer's hard drive. The cloud is just a metaphor for the Internet. The elements involved in cloud computing are clients, data center and distributed server. One of the main problems in cloud computing is load balancing. Balancing the load means to distribute the workload among several nodes evenly so that no single node will be overloaded. Load can be of any type that is it can be CPU load, memory capacity or network load. In this paper we presented an architecture of load balancing and algorithm which will further improve the load balancing problem by minimizing the response time. In this paper, we have proposed the enhanced version of existing regulated load balancing approach for cloud computing by comping the Randomization and greedy load balancing algorithm. To check the performance of proposed approach, we have used the cloud analyst simulator (Cloud Analyst). Through simulation analysis, it has been found that proposed improved version of regulated load balancing approach has shown better performance in terms of cost, response time and data processing time.
This document discusses common performance testing mistakes and provides recommendations to avoid them. The five main "wrecking balls" that can ruin a performance testing project are: 1) lacking knowledge of the application under test, 2) not seeing the big picture and getting lost in details, 3) disregarding monitoring, 4) ignoring workload specification, and 5) overlooking software bottlenecks. The document emphasizes the importance of understanding the application, building a mental model to identify potential bottlenecks, and using monitoring to measure queues and resource utilization rather than just time-based metrics.
Article by Marlabs Bangalore employee receives international recognition!Marlabs
“Testing Experience”, a Germany-based online magazine for software testers and test managers, has published an article by Ramesh Viswanathan, Senior Test Architect, Marlabs Bangalore in their March 2013 edition. Ramesh has presented his observations on the topic ‘Need for Performance Requirements to Ensure Reliable Business Applications’ through this article. It has incorporated topics associated with application development for various industry domains, techniques and tools for non-functional requirements gathering, optimal performance target, and best practices for developing resource-specific outputs.
Testing Experience serves as a platform for knowledge transfer in software testing projects, with more than 250000 downloads per issue, in over fifty countries. Marlabs congratulates Ramesh Viswanathan for this accomplishment. We hope and wish that such valuable tech-philosophies lead him to better avenues of success in career!
For viewing the detailed version of Ramesh’s article, refer the following link - http://www.testingexperience.com/testingexperience21_03_13.pdf
This document is a curriculum vitae for Bikram Samaddar that includes his contact information, objective, technical experience summary, professional experience summary, relevant experience, and key projects. It summarizes his experience as a .NET programmer working on various web application projects over 9 years for companies like Cognizant, CMC Limited, Praxis Softek Solutions, and IPEG Solutions. The projects involved technologies like ASP.NET, C#, SQL Server, Oracle, and MVC frameworks.
An Analytical Framework Of A Deployment Strategy For Cloud Computing Services...Rachel Doty
This document proposes an analytical framework to analyze the costs and risks of deploying academic websites on either local servers or Google App Engine (GAE), a platform as a service (PaaS) cloud. The framework uses queueing theory and the analytical hierarchy process to evaluate costs and service level agreements. Network data from two university websites are analyzed to compare the costs and risks of local versus GAE deployment. Results found that deploying the websites on GAE could save over 83% of costs with low risk, suggesting GAE is a suitable deployment option.
Other requirements, requirement specification and mapcsk selva
The document discusses requirements for network design and engineering. It outlines three key characteristics that reflect a customer's needs: operational suitability, supportability, and confidence. Operational suitability measures how well a network can be configured and monitored by customers. Supportability measures how well a network can be maintained over its lifetime. Confidence measures a network's ability to deliver data without errors or losses. The document also discusses financial requirements, enterprise requirements, and the process of gathering and documenting network requirements from various sources.
The document discusses operation modes in an ERP application, including creating and setting up different modes to optimize system resources for varying user demand throughout the day. Key transactions are listed for creating, maintaining, and forcing different operation modes, as well as displaying work processes. References for additional information on operation modes are also provided.
Web Application Penetration Tests - Information Gathering StageNetsparker
This document discusses the information gathering phase of a web application penetration test using Netsparker. It describes how Netsparker crawls a target site to map its structure and identify vulnerabilities. Key steps include configuring scan settings such as authentication, URL rewriting rules, and crawling parameters. The results of an initial "crawl and wait" scan are presented, showing how Netsparker reveals technical details, comments, inputs, and existing vulnerabilities to provide visibility into the target application before further testing.
This document discusses performance tuning and optimization in client/server computing. It covers tuning performance at the server, client, database, and network levels. Some key points include:
- Upgrading server hardware, using multiple network cards, and high-performance file systems can improve server performance. Offloading processing to specialized servers also helps.
- Client performance is impacted by hardware, operating systems that support multitasking, and efficient applications.
- Database performance relies on normalized database design, efficient indexing, and optimized query design using WHERE clauses to limit returned records.
- Performance tuning follows steps like identifying bottlenecks, modifying systems, and remeasuring impact while performance optimization employs techniques to improve network usage and data retrieval speeds.
Exploring Neo4j Graph Database as a Fast Data Access LayerSambit Banerjee
This article describes the findings of an extensive investigative work conducted to explore the feasibility of using a Neo4j Graph Database to build a Fast Data Access Layer with near-real time data ingestion from the underlying source systems.
PS provides metrics on user experience for pages on mobile and desktop. It offers both lab and field data, with lab data from a controlled environment useful for debugging, and field data capturing real-world user experience. Field data comes from Chrome User Experience Report and includes metrics like First Contentful Paint. Lab data uses Lighthouse to analyze pages for performance and other categories like accessibility. Field and lab data can differ due to variability in networks, devices, and other conditions between real users and simulations.
This document discusses techniques for optimizing the performance of PeopleSoft applications. It covers tuning several aspects within a PeopleSoft environment, including server performance, web server performance, Tuxedo performance management, application performance, and database performance. Some key recommendations include implementing a methodology to monitor resource consumption without utilizing critical resources, ensuring load balancing strategies are sound, measuring historical patterns of server resource utilization, capturing key performance metrics for Tuxedo, and focusing on tuning high-resource consuming SQL statements and indexes.
This document summarizes the results of simulating a manufacturing system model with three parts: arrival of components, processing of components, and the main production process. The simulation found a bottleneck at machine 2, with the longest queue times. Several proposals to improve system performance were explored using a process analyzer tool, with scenario 2 found to spread queue times more evenly and reduce bottlenecks at a reasonable cost, and scenario 5 found to give the best results by further reducing wait times and balancing utilization across resources.
age 1Question 1.1. (TCO 1) An important aspect of a network mana.docxgalerussel59292
age 1
Question 1.1. (TCO 1) An important aspect of a network management system involves (Points : 5)
managing connectivity between all PDAs and mainframe systems.
collecting and analyzing data to support organizational business needs.
keeping track of some client/server applications.
keeping track of most multimedia applications.
both managing connectivity between all PDAs and mainframe systems and keeping track of some client/server applications.
Question 2.2. (TCO 1) One of the main goals of network management is to make operations more _____ and operators more productive. (Points : 5)
secure
stable
productive
efficient
Both productive and efficient
Question 3.3. (TCO 2) Which is not one of the four steps of the network management life cycle? (Points : 5)
Monitor
Operate
Decommission
Plan
Both monitor and plan
Question 4.4. (TCO 2) In the network management system, the interface between the manager and the network device is known as the _____. (Points : 5)
manager
agent
console
application programming interface (API)
NMS
Question 5.5. (TCO 2) Which is not a part of all management agents? (Points : 5)
The MIB
Core agent logic
Management interface
XML
Both core agent logic and XML
Question 6.6. (TCO 3) One of the key words found in the definition of network management is operation. Select the best definition of operation from the following choices. (Points : 5)
Performing repairs and upgrades and taking corrective and preventive proactive measures to make the network run better
Keeping the network up and running smoothly
Configuring resources in the network to support a given service
Keeping track of resources in the network and how they are assigned
None of the above
Question 7.7. (TCO 3) Select the name of the language used for the definition of management information used with SNMP. (Points : 5)
Guidelines for the definition of managed objects (GDMO)
Abstract syntax notation version 1 (ASN.1)
Managed object format (MOF)
Structure of management information (SMI)
Object protocol language for management (OPLMv2)
Question 8.8. (TCO 3) Which item does not fall under the configuration management functional responsibility? (Points : 5)
Configuring alarm-forwarding information
Reconciling the as-built network to the as-planned network
Managing software images running on the network
Synchronizing cached network configuration information with the network's actual configuration
Performing backup and restore operations
Question 9.9. (TCO 4) Which is not one of the reasons why polling a managed device for operational data and state information is generally done? (Points : 5)
Troubleshooting
Device viewing
Diagnostics
Device update
.
Troubleshooting Tips and Tricks for Database 19c - EMEA Tour Oct 2019Sandesh Rao
This session will focus on 19 troubleshooting tips and tricks for DBA's covering tools from the Oracle Autonomous Health Framework (AHF) like Trace file Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feel like a rockstar DBA
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...AM Publications
Cloud computing means storing and accessing data and programs over the Internet instead of your computer's hard drive. The cloud is just a metaphor for the Internet. The elements involved in cloud computing are clients, data center and distributed server. One of the main problems in cloud computing is load balancing. Balancing the load means to distribute the workload among several nodes evenly so that no single node will be overloaded. Load can be of any type that is it can be CPU load, memory capacity or network load. In this paper we presented an architecture of load balancing and algorithm which will further improve the load balancing problem by minimizing the response time. In this paper, we have proposed the enhanced version of existing regulated load balancing approach for cloud computing by comping the Randomization and greedy load balancing algorithm. To check the performance of proposed approach, we have used the cloud analyst simulator (Cloud Analyst). Through simulation analysis, it has been found that proposed improved version of regulated load balancing approach has shown better performance in terms of cost, response time and data processing time.
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...
SOA PERFORMANCE METRICS
1. f8b742db-54c6-45f7-b3ff-28eea5213e5c-160121232551.doc
SOA PERFORMANCE METRICS:
HOW-TO MONITOR ORACLE ENTERPRISE MANAGER
By Krishnaprabha Chari, SOA/Middleware Architect
This document details the different performance metrics that are critical to monitoring the overall
health of SOA.
The first section describes the different parameters that are examined as well as the different
sources from where these metrics are extracted.
The later sections describe the data collection methodology for the metrics.
I. SOA Performance Report
Currently, the SOA servers are monitored on a daily basis and a report is generated everyday
showing the uptime, response time, CPU usage and other critical metrics.
A sample of the report is shown below.
Metrics Metric Measures Data Source
Baseline
(05/29/2009)
Response Time
Average Response
Time Website Pulse 2.0 seconds
Up Time
FMW uptime (for
this month, until
today) Website Pulse 96%
Scalability
# of Request per
second Grid Control 5.22/second
# of Request -
OHS Grid Control 4.8/second
SOA Infrastructure
Average
Connection counts Grid Control 128
CPU usage Grid Control 8.3%
Heap Usage(MB) Grid Control 1204
II. Data Collection Methodology for the SOA Report
Krishnaprabha Chari Page 1 of 16 01/20/2014
2. f8b742db-54c6-45f7-b3ff-28eea5213e5c-160121232551.doc
1. Computation of Response Time
Source: Website Pulse
URL: http://www.websitepulse.com/, login as portals/portals
Under Reports, go to All Reports/Transactions
Select target: FMW Check
Select report type: Monthly Log
..
Select starting date: Start of Month/Date of choice
Generate Report generates the data a shown below.
The Average Response Time column shows the average response time on a daily basis.
This time is the sum total of the average time taken by all components in FMW Check:
Portal, IDM, and SOA.
Krishnaprabha Chari Page 2 of 16 01/20/2014
3. f8b742db-54c6-45f7-b3ff-28eea5213e5c-160121232551.doc
The best source for getting the Average Response time for BPEL/SOA Processes is the
BPEL dehydration database. Run the appropriate queries against the schema (sample query
shown in Appendix) to determine this.
2. Computation of Up Time
Source: Website Pulse URL:
http://www.websitepulse.com/, login as portals/portals
Under targets, select FMWCheck
Krishnaprabha Chari Page 3 of 16 01/20/2014
Average Response Time
5. f8b742db-54c6-45f7-b3ff-28eea5213e5c-160121232551.doc
a. Formula for Up Time
From the main page of FMW Check, Uptime% shows the % of time the different
components of SOA, Portal and EBS were up. As only 1/3 of the downtime is attributable
to SOA, we recast the Uptime, only taking 1/3 of the total downtime.
SOA Uptime is calculated as 100- (100 – Uptime% *1/3).
This Month Uptime
Location Total checks Fails Average response time Uptime % Downtime
Seattle 2323 4 1.8395 99.8278 % 0h 18m
New York 2166 3 2.1710 99.8615 % 0h 17m
Total 4489 7 1.9995 99.8441 % 0h 18m
In this example, SOA Uptime would be 100 – (100 – 99.8441 *1/3)
Krishnaprabha Chari Page 5 of 16 01/20/2014
6. f8b742db-54c6-45f7-b3ff-28eea5213e5c-160121232551.doc
3. Computation of Scalability
Source: EM Grid
URL: login with appropriate user credentials.
Scalability is made of two components, # of Requests/sec and # of Requests-OHS.
a. # Of Request per second
Under EM Grid, navigate to Targets/Application Servers Tab
Click on the SOA Production Node
SOA.ora-prod-fmsoa-a1.com
(You can also select other nodes from A2 through A4)
Choose Component OC4J_SOA and the Performance Tab for this component.
Screen shot below shows the page that comes up. Click on the Requests per second link
highlighted below,
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On the next page, in the View Data tab, select the time period (as shown 4 pm – 4pm) and then
select Compare Targets to select the other nodes (A2-A4). The Compare Targets is a visual check
to ensure that the requests are equally distributed across the different nodes.
The screen show below shows the resulting page.
i. Formula for Requests per second
Requests/second = Average Value * Number of Nodes (in our current scenario, 4)
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Double-click this option
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b. # of Requests - OHS
Under EM Grid, navigate to Targets/Application Servers Tab
Click on the SOA Production Node
OHS.ora-prod-fmsoa-w1.com
(You can also select other nodes through OHS2)
Choose Component HTTP Server and the Virtual Host Performance Tab for this
component.
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Screen shot below shows the page that comes up. Click on the Request Throughput
(requests per second) link highlighted below,
On the next page, select prodsoa.com:IP0.0.0.0,Port7777.
In the View Data tab, select the time period (as shown 4 pm – 4pm) and then select
Compare Targets to select the other nodes (OHS2). The Compare Targets is a visual
check to ensure that the requests are equally distributed across the different nodes.
The screen show below shows the resulting page.
i. Formula for Requests per second - OHS
Requests/second –OHS = Average Value * Number of Nodes (in our current scenario, 2)
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Select this
option
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4. Computation of SOA Infrastructure
Source: EM Grid
URL: http://emprod.com:4889/em/ login with appropriate user credentials.
SOA Infrastructure is made of three components, Average Connection Counts, CPU Usage
and # of Requests-OHS.
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a. Average Connection Counts
Under EM Grid, navigate to Targets/Application Servers Tab
Click on the SOA Production Node
SOA.ora-prod-fmsoa-a1.com
(You can also select other nodes from A2 through A4)
Choose Component OC4J_SOA and the Performance Tab for this component.
Screen shot below shows the page that comes up. Click on the Open JDBC Connections
link highlighted below,
On the next page, in the View Data tab, select the time period (as shown 4 pm – 4pm) and then
select Compare Targets to select the other nodes (A2-A4). The Compare Targets is a visual check
to ensure that the requests are equally distributed across the different nodes.
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Select this option
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The screen show below shows the resulting page.
i. Formula for Average Connection Counts
Average Connection Counts = Average Value as shown under Statistics
b. CPU Usage/node
Under EM Grid, navigate to Targets/Application Servers Tab
Click on the SOA Production Node
SOA.ora-prod-fmsoa-a1.com
(You can also select other nodes from A2 through A4)
Choose Component OC4J_SOA and the Performance Tab for this component.
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Screen shot below shows the page that comes up. Click on the CPU Usage link highlighted
below,
On the next page, in the View Data tab, select the time period (as shown 4 pm – 4pm) and then
select Compare Targets to select the other nodes (A2-A4). The Compare Targets is a visual check
of the CPU Usage across the different nodes.
The screen show below shows the resulting page.
i. Formula for CPU Usage
CPU Usage/node = Average Value as shown under Statistics
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Select this
option
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c. Heap Usage/node
Under EM Grid, navigate to Targets/Application Servers Tab
Click on the SOA Production Node
SOA.ora-prod-fmsoa-a1.com
(You can also select other nodes from A2 through A4)
Choose Component OC4J_SOA and the Performance Tab for this component.
Screen shot below shows the page that comes up. Click on the Heap Usage link
highlighted below,
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On the next page, in the View Data tab, select the time period (as shown 4 pm – 4pm) and then
select Compare Targets to select the other nodes (A2-A4). The Compare Targets is a visual check
of the CPU Usage across the different nodes.
The screen show below shows the resulting page.
i. Formula for Heap Usage
Heap Usage/node = Average Value as shown under Statistics
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Select this
option