MD850: e-Service OperationsMD850: e-Service Operations
Capacity Management
OverviewOverview
Motivation Background Capacity Management
Manufacturing Operations Person-to-Person Service Operations e-Service Operations
Capacity Management Strategies Software Demonstration
MercuryInteractive’s Astra Load Test
MotivationMotivation
MotivationMotivation
Many e-Service Problems Exist that Affect Service Quality and Hurt Revenues Downtime Slow times (service slowdown)
37% of site pages exhibit unacceptable performance as defined by their managers (Mercury Interactive 2001)
Unpredicted traffic volume spikes Transaction failure
1 in 20 transactions fail on the average corporate website (Mercury Interactive 2001)
MotivationMotivation
52% of e-Service applications failed to scale according to original design expectations (Newport Group 1999) Automated load testing tools were not used
60% used no load testing tool prior to deployment 34% used load testing late in development or post deployment
only Budget and time overruns above average
23.5 days, $67,083 Scalability expectations were not realistic
Thought they would handle 4300 concurrent users on average Actually could only handle 2200 concurrent users on average
BackgroundBackground
BackgroundBackgroundCapacity ConceptsCapacity Concepts
Capacity rate at which output can be produced by an operating unit (e.g., a
machine, a process, a facility, a company) capacity = [number of units of output]/[time period]
Design Capacity maximum rate at which the process can operate on a continual
basis under ideal conditions
Effective Capacity rate of production that can be achieved for extended periods under
normal conditions, taking into account … [various infrastructure and environmental factors]
BackgroundBackgroundCapacity ConceptsCapacity Concepts
Capacity Utilization= [actual output]/[design capacity]
Capacity Efficiency= [actual output]/[effective capacity]
0
DesignCapacity
EffectiveCapacity
Actual OutputPeriod #5
Actual OutputPeriod #8
Managerial Focus On:Loss in Production
Capability
Capacity Management in Capacity Management in Manufacturing OperationsManufacturing Operations
Capacity ManagementCapacity ManagementManufacturing OperationsManufacturing Operations
Supply Chain Processes Supply Chain Modularity Facilities Facility Location Facility Focus Process Design
Product Design Product Variety Product Quality Production Scheduling Materials Management Maintenance Job Design and Personnel
Management
Capacity ManagementCapacity ManagementManufacturing OperationsManufacturing Operations
Common Approaches Forecasting demand Translate demands (d1, d2, …, dn) for products (1, …, n)
into capacity requirements Break-Even Analysis Decision Analysis
Capacity ManagementCapacity ManagementManufacturing OperationsManufacturing Operations
Demand-Side Management of Capacity Counter-cyclic products
Toro - lawnmowers, snowblowers
Promotion Pricing
Capacity ManagementCapacity ManagementManufacturing OperationsManufacturing Operations
Medium-term to Short-term approaches
Aggregate planning chase demand strategy level demand/workforce
strategy
Inventory management Materials management Operations scheduling Personnel scheduling
Typical capacity analysis & management tools
Optimization
MRP … CRP Inventory review
Queueing Simulation Optimization
Capacity ManagementCapacity ManagementManufacturing OperationsManufacturing Operations
Capacity Strategies Facility
Product-Organized: facility makes one product type Process-Organized: multiple products or parts made through one process Market-Organized: close physical proximity to the customer Focused Factory/Plant-Within-A-Plant
Capacity Expansion Demand-Leading: maintain excess capacity Demand-Trailing: capacity lags behind demand Demand-Matching: match capacity to demand Steady Expansion: add capacity on regular basis, based on long-term
needs, but not on demand fluctuations
Capacity Management in Person-Capacity Management in Person-to-Person Service Operationsto-Person Service Operations
Capacity ManagementCapacity ManagementPerson-to-Person ServicesPerson-to-Person Services
Capacities to Manage Facilitating Goods
When produced internally, same issues as manufacturing Often procured from supplier
Services Persons who can deliver service at a certain rate
Information Printed information … either inventoried, or printed on demand at
some rate Communicated in person at some rate
Capacity ManagementCapacity ManagementPerson-to-Person ServicesPerson-to-Person Services
Demand Side Management of Customer Behaviors Appointment reminders Pricing
penalty for being late yield-management
Wave scheduling Capacity sharing
One printer, 20 professors Ask customer to stop consuming/conserve
Energy efficient light bulbs Stop filling demand/shut off service
California electric service
Capacity ManagementCapacity ManagementPerson-to-Person ServicesPerson-to-Person Services
Service Facility Design Queuing model of service system queues Simulation models to study activities within system and
to estimate effective capacity, service times, and so forth
Daily/Weekly Capacity Management Service Personnel/Staff Scheduling
Nurses, police, paramedics optimization - integer linear programming
Capacity Management in Capacity Management in e-Service Operationse-Service Operations
Capacity ManagementCapacity Managemente-Service Operationse-Service Operations
e-Service Capacities to Manage Digital network capacities Goods network capacities Person-to-Person service network capacities Inter-layer digital service capacities
Service Personnel
Service Networks, Intelligent Goods Networks
Digital Networks & Digital Content
Service-Product Service-Process
Capacity ManagementCapacity Managemente-Service Operationse-Service Operations
Digital Networks
Networks of Physical Objects
People
Client/ Server
DistributedComponentApplications
Service-Product Service-Process
Info:Digital Content
Capacity ManagementCapacity Managemente-Service Capacitye-Service Capacity
Determinants of e-Service Capacity Number of customers and internal users
demand
“Service-Product” site content
“Service-Process” server capacity and configuration of hardware and
software
Capacity ManagementCapacity ManagementDeterminers of e-Service CapacityDeterminers of e-Service Capacity
Desired Capacity = Average
Digital ContentDemand Per User
x Number of UsersPer Time Period
= Load on HardwarePer User
/ Number of Supported
Concurrent UsersHardwareCapacity
(design)
Goods InfoServices Info
Digital ServicesContent
Consumed Service-Product
Web browsersWireless apps
Other processes (e.g., ERP system)User Mix
ServerNetwork Infrastructure
Software ModulesService-Process
Capacity ManagementCapacity ManagementDeterminers of e-Service CapacityDeterminers of e-Service Capacity
Capacity ManagementCapacity ManagementDeterminers of e-Service CapacityDeterminers of e-Service Capacity
Common e-Service Problems and Causes Long response time from end users’ point of view Long response time as measured by the servers Memory leaks High CPU usage Too many open connections between the application and end users Lengthy queues for end user requests Too many table scans of the database Erroneous data returned HTTP errors Pages not available
Capacity ManagementCapacity Managemente-Service Operationse-Service Operations
Typical e-Service Infrastructure Managed to Achieve Desired Capacity Application Server Software Web Server Software Database Software Networking Software Load Balancing Software Application Server Hardware Web Server Hardware Database Hardware Networking Hardware
Capacity ManagementCapacity ManagementStrategies for e-Service CapacityStrategies for e-Service Capacity
Strategies for Managing e-Service Capacity Manage demand Manage “Service-Product” Manage “Service-Process”
Managing e-Service Demand Managing e-Service Demand PatternsPatterns
Capacity ManagementCapacity Managemente-Service Demandse-Service Demands
6am 12pm 6pm 12am 6am 12pm 6pm 12amDay 1 Day 2
6am 12pm 6pm 12am 6am 12pm 6pm 12amDay 1 Day 2
Demand Surge
CyclicalRandom & Infrequent
Managing the e-Service ProductManaging the e-Service Product
Capacity ManagementCapacity Managemente-Service Reference Model e-Service Reference Model (Menasce and Almeida, (Menasce and Almeida, Scaling for e-BusinessScaling for e-Business, 2000), 2000)
Characteristicsof the Business
NavigationalStructure &
Function
Patterns of CustomerBehavior
Site Architectureand
Service Demands
BusinessModel
FunctionalModel
CustomerModel
ResourceModelTechnological
View
BusinessView
InternalMetrics
ExternalMetrics
Capacity PlanningCapacity PlanningTranslating the Service-Product into Loads Translating the Service-Product into Loads
Characteristicsof the Business
NavigationalStructure &
Function
BusinessModel
FunctionalModel
Service-Product
GoodsServices
Information
WWW SiteContent
A Network of Paths BetweenPages/Objects
Heim andSinha (2000),Spiller and
Lohse (1998)
Menasce andAlmeida (2000)
Managing the e-Service ProcessManaging the e-Service Process
Capacity ManagementCapacity Managemente-Service Process Strategiese-Service Process Strategies
Implications of Classic Aggregate Planning Strategies chase demand strategy
add a server when you sense it is needed take away a server when it is not needed
level demand strategy build an inventory of e-Services (IMPOSSIBLE)
Capacity ManagementCapacity Managemente-Service Process Strategiese-Service Process Strategies
Implications of Classic Capacity Expansion Strategies Demand-Leading: maintain excess capacity
expensive solution service level stays reasonable customers satisfied
Demand-Trailing: capacity lags behind demand queue of requests builds service slows as servers average capacity across all requests server computers grind to a halt when demand severely surpasses
capacity … time to purchase new servers dissatisfied customers
Capacity ManagementCapacity Managemente-Service Process Strategiese-Service Process Strategies
Implications of Classic Capacity Expansion Strategies Demand-Matching: match capacity to demand
as demand grows (shrinks), in-house (and outsourced) servers are dynamically added (removed)
capacity for static files (GIF, JPEG, .EXEs, etc.) outsourced to a third-party
as demand grows (shrinks), third-party senses growth and re-allocates files across a larger (smaller) subset of their network
you manage basic content (e.g., HOME.HTML file) caching strategy (Akamai, Inktomi, others vendors)
Steady Expansion: add capacity on regular basis, based on long-term needs, but not on demand fluctuations intermittent periods of poor service responsiveness if demand grows too fast, system may fail, customers dissatisfied
e-Service Capacity Planning, e-Service Capacity Planning, Analysis, and ManagementAnalysis, and Management
e-Service Capacity Planning, e-Service Capacity Planning, Analysis, and ManagementAnalysis, and Management
Best Practices Set conservative goals with the intent to expand them out in a
systematic fashion Have a plan for short-term incremental achievements of long-term
goals Integrate load testing into the development process of web
applications early and often Always test the limits of any e-Service application prior to going live
Leverage pre-deployment test assets in the production environment to monitor live web application performance
Set a load capacity threshold for growth which is continuously monitored
At 70-80% of the existing system’s handling capacity, start executing plans to add more capacity
(Source: Newport Group 1999, 2000)
e-Service Capacity Planning, e-Service Capacity Planning, Analysis, and ManagementAnalysis, and Management
Best Practice Objectives Minimized Latency
Keep waiting time between making a request and beginning to see a result as low as possible
Maximized Throughput Number of items potentially processed per unit time should
be as high as possible Mid-to-high Utilization
Actual capacity utilization of components should be kept around 75% … latency suffers as you go over this level
Maximized Efficiency Keep overall performance high and cost low
e-Service Capacity Planning, e-Service Capacity Planning, Analysis, and ManagementAnalysis, and Management
Best Practice Metrics End User Response Time
Measures the performance of an application from the end-user perspective
The amount of time required for an end user to receive a response or to execute a business transaction
Application Availability Page availability Transaction availability User-perceived availability
Reliability Correct Content Delivery in Multi-Step Transactions
(Sources: Newport Group 1999, Mercury Interactive 2001)
e-Service Capacity Planning, e-Service Capacity Planning, Analysis, and ManagementAnalysis, and Management
Best Practice Guidelines1. Focus on critical business processes2. Use the right monitoring solution to meet your business needs3. Get a consistent performance baseline and watch for trends4. Avoid an alerting flood …send alerts conservatively5. Think “recurring” when acting on alerts6. Correlate end-user performance with back-end issues7. When a problem is identified, prioritize IT resources8. Optimize your existing infrastructure9. Define escalation procedures to follow to address performance
issues10. Use application performance monitoring to avoid having every
department scramble when performance goes bad
(Source: Mercury Interactive, 2001)
Capacity Management Techniques Capacity Management Techniques During e-Service Design and During e-Service Design and DevelopmentDevelopment
Capacity ManagementCapacity ManagementDesign & Development StageDesign & Development Stage
Basic Question: How to design communication between e-Service application layers? Capacity Issue
How to set up appropriate connections between tiers? How to avoid too few connections between tiers? How to avoid bad effects from failure of a server in one of the
tiers? Methods
Separation of presentation logic from business logic from database management
Load balancing Outsource network storage for caching of content
Capacity ManagementCapacity ManagementDesign & Development StageDesign & Development Stage
Commonly Observed Design Problems General
Insufficient memory Incompatible service packs and application extensions (e.g., .DLLs) Excessive queueing requests Too many secure HTTPS connections in use
Web Server Insufficient memory Poor web server design High CPU usage
Application Server Poor cache management and high CPU usage Lack of memory Poor session management Poor database tuning
Capacity ManagementCapacity ManagementDesign & Development StageDesign & Development Stage
Commonly Observed Design Problems Database
Inefficient indexing Fragmented databases Out-of-date statistics Faulty application design
Network Inadequate Internet pipe Hidden bottlenecks between the customer’s Web site and the
ISP Faulty hops (misdirected traffic and lost packets) Misconfigured software and incompatible hardware
(Source: Mercury Interactive 2001)
Capacity ManagementCapacity ManagementDesign & Development StageDesign & Development Stage
Load Balancing Objective is to be able to allocate demand – as it is requested – to
resources that will provide best service response Many sites use N-tier systems with many servers in each tier that
perform same functions Reliability Backup in case of failure and when server maintenance must be
performed Possible techniques:
RANDOM DISTRIBUTION – Round-robin (random) allocation of a customer request to an IP address for a web server. If a server fails, customer requests will still be allocated to it until its IP address is removed from service.
INTELLIGENT DISTRIBUTION – Customer requests are allocated to servers based upon current utilization at each server. The lowest utilization server will receive the next job.
Capacity ManagementCapacity ManagementDesign & Development StageDesign & Development Stage
Outsource network storage for cache capacityAkamai, Inktomi, etc. provide services for
storing and serving static content Many of these companies are also setting up
caching procedures for dynamic content
Capacity ManagementCapacity ManagementDesign & Development StageDesign & Development Stage
Basic Question: Theoretically, how many people can my homepage be served to simultaneously? Capacity Issue:
What is my website’s design capacity? Back of the Envelope Method:
Home page (average) size: 50,000 bits = 6250 bytes Rented T3 line = 45,000,000 bits/sec [45,000,000 bits/sec] / [50,000 bits/customer] =
900 happy customers/sec … the “design capacity” 900 customers/sec * 60 * 60 * 24 =
77,760,000 happy customers/day assuming 900 customers request the page simultaneously at the beginning of each second, and
each has a fast enough modem to receive the file by the end of the second
Capacity ManagementCapacity ManagementDesign & Development StageDesign & Development Stage
Load Analysis and Testing Tools Brute Force Calculations
Queueing theory
Brute Force Load Testing Thousands of employees at their computers
Simulation-Based Load Testing & Monitoring Software Load-Test: “Can our site handle 25 users?” Stress-Test: “Stable? Reliable? Over long period?” Capacity-Test: “Maximum number of concurrent customers?”
Capacity ManagementCapacity ManagementDesign & Development StageDesign & Development Stage
Simulation Based Load Testing Employ virtual (i.e. fake) users on client computers Have virtual users use the e-Service system based on
scripted behaviors recorded for them by the load testing program
Collect data about the performance that the virtual users experience
Modify service process depending upon the typical performance observed
Capacity ManagementCapacity ManagementDesign & Development StageDesign & Development Stage
Major Vendors & Applications Mercury Interactive
LoadRunner + WinRunner + TestDirector Astra LoadTest + Astra Quicktest + Astra SiteManager
Segue SilkPerformer
Empirix Compuware Radware
Capacity ManagementCapacity ManagementDesign & Development StageDesign & Development Stage
Development Deployment
DeploymentDeployment
Software Product
Service
M.I. LoadRunner, etc.
M.I. Astra LoadTest, etc.
Segue SilkPerformer
Capacity/Load Test Monitoring
Capacity Management Techniques Capacity Management Techniques After e-Service DeploymentAfter e-Service Deployment
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
General Patterns As richness of content increases, capacity decreases
(for a fixed service process) As customers converge to more actively involved
behavior types (are retained, and thus consume more content), capacity decreases (for a fixed service process)
As network of site paths increases in complexity, the complexity of managing capacity will increase
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
General Responses Decrease content richness (when appropriate) Make customers more efficient in their
behavior Manage site complexity
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Content TuningServe out content at a lower level of richness, or
remove some contentBy lowering the number of bytes for each file, less
capacity will be used by each individual userSaved capacity can then be used to serve
additional customers
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Content Tuning Possible techniques:
DESIGN TIME – Create separate sets of high resolution and low resolution images for your e-Service, and program your site so that you can change which images are dynamically inserted into your pages
DESIGN TIME – Create a program that loads an image directory on the web server with high or low resolution images, depending upon present demands
RUN TIME – Use a program procedure or filter to pre-process each image to a lower resolution, as the image is requested. Once the image has first been processed, the filter could just serve out the lower resolution image. (Note: this approach lowers image bandwidth but increases page processing time.)
RUN TIME – Change heavily downloaded page from dynamic content (3.5 seconds on average to generate and then download) to static page (1.5 seconds on average to download)
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Managing customers’ efficiency New customers – difficult to do
Better site design as you figure out how new customers behave
Longer-term customers/users – should experience a learning effect that will cause them to be more directed in their activities
They may consume more content per visit as site stickiness keeps them around
They may consume less content if they become more efficient at shopping and other tasks on the site
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Basic Question: Is resource X running? Capacity Issue:
Has server failed? Has network router failed?
Method: Systems performance monitoring tools for each infrastructure
component Tend to monitor and provide information in a stovepipe
fashion, but useful if you need to know about a specific resource
Note – system monitors can report that each individual component is running fine, yet the system overall can exhibit poor performance
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Basic Question: Is my site running? Capacity Issue:
Is capacity > 0 right now? How long has it been 0?
Method: Simple site monitor, similar to “ping” service on any computer
Example: ArsDigita.com’s free “Uptime” web site monitoring service ... Every
15 minutes, it tries to download a text file called “http://www.mysite.com/textfile.txt” from your web site
If it fails, it emails you that your site is down
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Basic Question: What portions of my “service-product” are popular? Capacity Issue:
Which files are being requested frequently? Which content configurations are requested frequently? Which processes deliver that content? Am I paying too much for my ISP service contract? Can I get by on a
lower-bandwidth contract? Method:
Site log file analysis Add up all http: transactions made to your web site during some time
period
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Log File AnalysisAnalyze http requests stored on the serverTranslate requests into aggregate historical
demands for certain time period bucketsVendors
Webalizer (www.mrunix.com/webalizer) NetTracker (www.sane.com/products/NetTracker) many more
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Customer Profiling (via data mining)Translate customer behaviors within system
into common customer profilesLink profiles to the resources that are required
to service the respective activities
Network ofSite Paths
Subset of Paths Having Some Relationship (Business Activities) Within an E-Service
WWW Site Log(http GET, page referrals between pages )
Subset #1of paths commonly
visited together
Subset #2of paths commonly
visited together
Subset #3of paths commonly
visted together
“DataMining”
is made up of ...
has some business meaning, and is
stored in ...
Three Consumer Types … 1, 2, and 3
additional analysis
Subset #1 … “browse only”Subset #2 … “directly buy”Subset #3 … “browse, then buy”
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Translating the service product
into demandloads
WWW SiteContent
Content Located Together Within an E-Service
WWW Site Log(http GET commands for pages visited)
Goods XServices XContent X
Goods YServices YContent Y
Goods ZServices ZContent Z
“DataMining”
is made up of ...
has some business meaning, and is
stored in ...
Three Consumer Types … X, Y, and Z
additional analysis
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Translating the service product
into demandloads
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Basic Question: How do popular areas of my site affect my capacity?Capacity Issue:
How to link common behaviors to specific capacity providing resources?
Methods Queueing models Load testing tools Site monitoring tools
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Enter
Home Page
Search Page
Add to Cart PayPage 1 Page 2
Page 3 Page 4
prob. = 0.2
prob. = 0.5
prob. = 0.3
1.0 0.3
0.30.6
0.1
0.4
0.8
0.4
0.1
Exit
0.0 1.0
Business Activity = “Browse”
Identify user navigation paths within e-Service … then use queueing theory
equations to determine long-run impact of various paths
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Enter Home Exit
Enter
Enter Home Browse Exit
Browse Add to Cart Pay Exit
Three Simple Consumer Behavior “Types”Observable in Navigation Model
Identify user navigation paths within e-Service … then use queueing theory
equations to determine long-run impact of various paths
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Web Application Performance MonitoringWeb application monitoring tools work to send
up red flags when the application under surveillance fails to meet its performance objectives
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Web Performance Monitoring Active (synthetic) monitoring
Identify several key transactions in the e-Service Create an emulated (or simulated) client for each key transaction Execute transactions against the simulated client on a regular
basis Measure transactions in detail and collect service availability data If performance violates some predetermined threshold, send an
alert to e-Service manager
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Web Performance Monitoring Passive (observational) monitoring
Collect data from actual end user activity and store it in a database
Analyze data for patterns Identify several key transactions in the e-Service Measure transactions in detail Generate performance metrics Link performance to activities experienced within the e-Service
system
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Based on actual end user data High degree of detail Capture client processing time Aids in root-cause analysis
Active Passive
Data collected opportunistically Reactive by nature … to real
customer problems Potential for collecting excessive
amount of data
24 x 7 monitoring Constant controlled data collection Data collection provides baseline Proactive in nature
Not based on true end-user data Limited insight into the back-end
infrastructure performance Difficult to execute “real”
transactions Creation/maintenance of scripts
Advantage
Drawback
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Site Monitoring Software/Services Perform Systems Analysis
Manager must understand the current system architecture Translate architecture into load testing objectives Define input data for testing Choose a testing/monitoring strategy
Create Virtual User Scripts Record scripts that virtual users will use to interact with the e-
Service system Define User Behaviors
Specify how the virtual users will behave (random think times, browser types to use, dial-up speeds, etc.)
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Site Monitoring Software/Services Create a User Scenario
A scenario is a set of user behaviors combined together to test a web site
Monitor Performance Run the user scenario Collect data (client, network components, hardware
components, server/software components) on performance for user requests
Analyze Performance Statistics Graphs
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Prominent Vendors & ApplicationsSoftware Products
Mercury Interactive Topaz Segue SilkPerformer
Services Mercury Interactive ActiveWatch (Topaz-based
service) Mercury Interactive ActiveTest (LoadRunner-based
service)
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
“Test on Demand” ServicesDeliverables
Consulting time Set of tailored test scripts for an e-Service Application test run Final report providing details about the tests Suggestions for areas of e-Service process/
infrastructure in need of improvement
Capacity ManagementCapacity ManagementAfter e-Service DeploymentAfter e-Service Deployment
Development Deployment
DeploymentDeployment
Software Product
Service
M.I. LoadRunner, etc.
M.I. Astra LoadTest, etc.
Segue SilkPerformer
M.I. Topaz
M.I. ActiveWatch
GomezNetworks
M.I. ActiveTest
Capacity/Load Test Monitoring
Software DemonstrationSoftware Demonstration
Software DemonstrationSoftware DemonstrationMercury Interactive Astra LoadTestMercury Interactive Astra LoadTest
Some basic capabilities Record customer activities; save as “script”
Activities that have meaning Content consumption … “service-product” subset Economic activity … “checkout system” Network paths … typical customer paths through system
Site and/or service parameters … automatically iterate through subset of or all possible values
Multiple-option click boxes Drop-down options
Combine multiple customer activities into a demand “load scenario”
Software DemonstrationSoftware DemonstrationMercury Interactive Astra LoadTestMercury Interactive Astra LoadTest
Some basic capabilities Run scenario using actual web site or n-tier service process
Prior to going “live” After going “live”
Data collection Content transaction Process technology “monitors”for typical
WWW servers & hardware off-the-shelf WWW component software
Data analysis Averages, stdev, graphs Drill-down Data export