Benchmarking Shows Combined Power of Azure and the Accenture Life Insurance & Annuity PlatformAccenture test engineers demonstrate Tier 1-class performance and scalability when deploying the Accenture Life Insurance & Annuity Platform on Microsoft Azure, including Microsoft SQL Server 2014 data management software and the Windows Server 2012 R2 operating system.

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 1

•Meeting the Need for Cloud-Based Solutions

•Deploying ALIP on Microsoft Azure

•ALIP Architecture

Benchmark Test Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 3

•Test Software

•Test Database

Online Load & Scalability Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 5

•Methodology for Online Load & Scalability Test

- Term Life Insurance Application Test

- Whole Life Insurance Application Test

- Immediate Annuity Application Test

- Policy Maintenance (Modify Customer Address) Test

- Financial Maintenance (Create and Apply Suspense) Test

Configurations for Online Load & Scalability Test . . . . . . . . . . . . . . . Page 7

Test Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 7

Batch Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 8

•Findings

•Test Team Observations

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 10

Table of Contents

Abstract Accenture tested its Accenture Life Insurance & Annuity Platform (ALIP) deployed on Microsoft Azure, including Microsoft SQL Server 2014 data management software and the Windows Server 2012 R2 operating system, to determine whether a cloud-based deployment of ALIP could meet the needs of its largest Tier 1 insurance carrier customers. Testing found that ALIP deployed on Azure could meet and exceed the needs of Tier 1 insurance carriers, providing processing power to handle billing contracts at a rate of 50 per second, and generation of complex cash value quotes at the rate of more than 68 per second. Scalability testing showed that when increasing workloads from a single user to 3,000 users there was no change in the average sub-second page time delivery in 97% of pages, and that average page time overall rose from 0.21 seconds for a single user to just 0.25 seconds when scaled to 3,000 users. This report provides results of the tests, in addition to details about the test environment and the methodology used.

This white paper provides results from Accenture’s successful benchmark testing of Accenture Life Insurance & Annuity Platform (ALIP) for Tier 1 life and annuity carrier workloads on Microsoft Azure. Accenture, a global management consulting, technology services and outsourcing company, with more than 358,000 people serving clients in more than 120 countries, is dedicated to helping organizations maximize their performance and achieve their vision. The company operates as five businesses: Accenture Strategy, Accenture Consulting, Accenture Digital, Accenture Technology, and Accenture Operations, which together generated net revenues of US$31 billion for the fiscal year ended Aug. 31, 2015. Accenture Life and Annuity Software is making an impact within the Insurance industry with ALIP—an integrated suite of modern components that manage the full policy lifecycle from new business and policy administration to claims and payout. Leveraging an advanced architecture and Java-based technology, ALIP offers carriers the ability to adopt today’s modern technology capabilities such as mobile-enabled solutions, portals, and enriched business processes and workflows. Based on a centralized product and business rules engine, the system offers industry-leading configurability allowing business users to build, modify, test and launch products, all within days or weeks and without IT involvement. ALIP’s simplified platform and integration approach enables life and annuity carriers to lower total cost of ownership for IT operations, enhance customer and agent service, and increase administration automation.

Overview

1

Meeting the Need for Cloud-Based SolutionsThe benchmark test was conducted because life and annuity carriers, especially those looking at refreshing legacy systems or provisioning for new services, are attracted to the software-as-a-service and platform-as-a-service opportunities that the cloud provides. However, they have proven reluctant about entrusting their mission critical customer data and operations to the kind of many-to-one, multi-tenant public cloud solutions typically offered. Accenture Life and Annuity Software recognized the significant business agility and IT cost benefits that carriers could achieve through a modern policy administration system delivered in the cloud. The team responded by launching “ALIP in the Cloud,” its leading life and annuity platform running on Microsoft Azure. ALIP in the Cloud takes advantage of the Microsoft Cloud for the technology infrastructure behind ALIP while adding a private network model to support hybrid and private cloud deployment options. Accenture also provides a managed services layer that produces, orchestrates, manages, and governs enterprise cloud resources. The combination of the private network model and managed services layer gives carriers the control and agility to manage their

Over twenty US Life and Annuity carriers leverage ALIP today on-premises (including 70% of the top 10 A.M. Best Company rated carriers).

services simply, securely, and effectively in public, hybrid, and private cloud environments. ALIP in the Cloud is attractive to carriers of all sizes because of a value proposition that includes:

• Improved Efficiency. Carriers can leverage the solution as an alternative or augmentation to on-premises implementation. They can benefit from lower TCO by running full administration capabilities in the cloud; or can augment their current on-premises environment by leveraging the cloud to gain processing efficiencies for initiatives such as product development, testing, conversions, pilots, UAT and/or overnight processing.

•Robust Functionality at Lower Cost. The solution is ideal for carriers who want the functionality of a sophisticated system but can’t afford the architecture and implementation costs typically associated with on-premises solutions.

1

•Scalability on Demand. By leveraging cloud-based IO services, carriers are able to spin up—and down—the full end-to-end ALIP environment with on-demand infrastructure. Carriers only pay for the infrastructure they actually use, saving time and money.

•Staying Current. Cloud deployment supports regular software upgrades to keep the carrier’s investment current with the latest functionality.

Deploying ALIP on Microsoft Azure

Accenture partnered with Microsoft to benchmark ALIP performance when hosted on Microsoft Azure. Azure, the open and flexible cloud computing platform, has earned a reputation for providing enterprise-class cloud services. Accenture was also attracted to Azure because it supports the broadest selection of operating systems, programming languages, frameworks, tools, databases and devices—which was especially important as ALIP was created with Java-based development tools. Azure should also prove popular with insurers, as Microsoft was the first cloud provider recognized by the European Union’s data protection authorities for its commitment to rigorous EU privacy laws. Microsoft was also the first major cloud provider to adopt the new international cloud privacy standard, ISO/IEC 27018, and has launched Azure Government, a stand-alone version of Azure designed to meet the rigorous compliance requirements of U.S. public agencies. Accenture engineers created demanding benchmark tests—exceeding the demands seen in even Tier 1 carrier environments—and were impressed with the results. The tests were run against 15 terabytes of data and 10 million policies, simulating 3,000 concurrent users. Test findings concluded: We believe these results provide a unique value proposition for smaller carriers seeking to leverage a highly functional system for the first time,

and for large carriers looking to simplify their environment and reduce costs and/or supplement their current environment to support fluctuating IT demands and improve process efficiencies.

ALIP Architecture

A brief look at ALIP’s architecture provides some background for better understanding the significance of the benchmarking tests and what they found. ALIP is platform neutral, based on Java and JEE, supporting multiple relational databases. Advanced Web 2.0 techniques drive a more productive user interface. ALIP‘s workflow-driven processes consume underlying services providing a flexible framework on which to define and customize business solutions. Configurable business rules and product engines leverage advanced dynamic and domain specific languages to accelerate development without sacrificing performance. A message-oriented execution architecture provides dynamic scalability at reduced cost and exposes all ALIP services to real-time requests. Messaging—through a message broker (Apache ActiveMQ was used for the testing)—is also used to

simplify and extend integration capabilities through the use of pre-built and configurable mediation and transformation logic for common integration patterns based on Spring Integration. ALIP is architected to achieve high availability at all layers. Interactive web-based ALIP components are hosted in a JEE application server container. ALIP supports the use of clustering and failover facilities provided by the container. ALIP services are stateless which simplifies configuration. ALIP includes a message-based batch execution and integration framework as well. ALIP processes persistent messages atomically so that failures can be safely retried. All components including the message broker are configured for failover. Even in the event of a full shutdown, ALIP is designed to pick up gracefully where it left off after a restart. ALIP is also architected for scalability through its use of low-cost commodity nodes. This makes it ideal for Azure deployment, as life and annuity carriers can simply add or subtract nodes as needed—including on the fly—to match current demands.

2

Benchmark Test EnvironmentAccenture’s engineers performed the testing, using their own data and the Azure platform. This means that the testing wasn’t performed in a lab but rather in the actual cloud environment a carrier could use in deploying a private-cloud ALIP solution.

The ALIP environment, as shown in Figure 1, was created using four Azure virtual machine (VM) nodes. A fifth Azure VM was used for executing test scripts. The four ALIP virtual machines were selected from Azure template offerings, as shown in Table 1.

Tests simulating concurrent user activity involved interactions between the application server, process servers, and database. Batch testing doesn’t involve the application server, as batch processing is handled by the process servers and the database.

Test SoftwareSoftware that was used to facilitate testing included:• Load Generation. The load testing

feature of Microsoft Visual Studio Ultimate was used for load generation, maintaining simulated users, the test runner, and for collecting results, working in conjunction with a test controller application.

• Batch Performance Test Tool. The ALIP Performance Team created a batch performance test tool. The tool, written in the Groovy scripting language, was designed to run and time a series of batch jobs. The tool also collected a set of output data providing further details about the run of the job.

6x1TB Premium Storage Logs

...

Application ServerDS14 - 16CPU/112GB RAMCentOS 6.6Jboss EAP 6.4

ALIP Process ServerDS14 - 16CPUJ/112GB RAMCentOS 6.6Java 1.7

ALIP Process ServerDS14 - 16CPU/112GB RAMCentOS 6.6Java 1.7

Database ServerGS5 - 32CPU/448GB RAMWindows Server 2012 R2SQL Server 2014

20x1TB Premium Storage Data

...

Virtual Machines

Virtual Machines Details

Database Server Azure GS5 template for 1 VM with:• 32CPUs• 448GBmemory• 201-terabytedrivesforstorage• 61-terabytedrivesforlogstorage• 51-terabytedrivesforbackups• WindowsServer2012R2operatingsystem• MicrosoftSQLServer2014Database• 15terabytestotaldata• 1,500tables

• 5.6billionrowsinlargesttable(PremiumHistory)

Application Server Azure DS14 template for 1 VM with:• 16CPUs• 112GBmemory• LinuxCentOS6.6operatingsystem

• JBossEnterpriseApplicationPlatform6.4

Note: One installation of JBoss was used with two JVMs to split the 3,000 users for load-balancing.

ALIP Process Servers

Azure DS14 template for 2 VMs, each with:

• 16 CPUs• 112GBmemory• LinuxCentOS6.6operatingsystem

• Java1.7

Note: Each VM ran six process servers, with each set to process up to 10 threads of work, for a total of 12 process servers with the ability to handle 120 simultaneous threads of work.

Table 1: Virtual Machine Descriptions

Figure 1: The ALIP Environment

3

Test DatabaseThe 15-terabyte database used in the testing included contracts from the following product types:• Term• WholeLife• VariableUniversalLife• DeferredAnnuity• ImmediateAnnuity

The contract data was entered so that the data was spread across several tables, including application, policy, customer, general ledger, and requirements. Table 2 provides a partial list of approximate table row counts from the test runs.

Test Database Tables (Partial Listing)

Table Type Main Table Rows Notes

Application 41 Million There are multiple rows in this table for each actual application.

Clients 19 Million In this context, client refers to any person or organization involved with ALIP or a policy in any way. All ALIP users have a row, all policy owners, beneficiaries, agents, etc.

Policy Roles 35 Million All people associated with a policy, including owner, joint owner, insured (even if the same as owner, this table must have a unique row by role), agent, etc.

Transactions 247 Million Transactions at a policy level.

Billing Records 61 Million Number of bills.

Requirements 53 Million Application, client, policy or transaction level require-ments which must be met in order to proceed.

General Ledger 85 Million General ledger entries.

Tasks 134 Million Tasks requiring the attention of ALIP users.

Cash Value Quote

309 Million Current and historical records of the financial values of the policies.

Generic Feature Values

1.7 Billion Stores policy’s running values needed for product calculation for every transaction.

Premium History Values

5.6 Billion This is the largest table. It stores history about all the premiums paid in to policy.

Table 2: Partial Listing of Database Tables

4

Online Load & Scalability TestFive separate tests, referred to as web tests, were created, and then loaded into Visual Studio to be executed simultaneously as a load test. Scalability was measured by performing all five web tests using the loads generated by a single user, and then repeating the tests with loads generated by 3,000 users.

Methodology for Online Load & Scalability TestThroughout testing, the goal was to replicate real-life workloads as much as possible—even more so. In fact, the actual workloads tested exceeded what most Tier 1 carriers would likely see. To provide a cross section of real-life workloads, three types of application workflows were used, as well as two common forms of maintenance. The scenarios used for the five web tests were:•TermLifeInsuranceApplicationTest•WholeLifeInsuranceApplicationTest•ImmediateAnnuityApplicationTest•PolicyMaintenanceTest•FinancialMaintenanceTest

A closer look at what was involved in each test is described next.

Term Life and Whole Life Insurance Application TestsWorkflow for the Term Life Insurance Application test included opening web pages, accepting inputs, replicating the order in which an agent would enter data. The same workflow was used for the Whole Life Insurance Application Test. Workflow for both is shown in Table 3.

Immediate Annuity Application TestWorkflow for the Immediate Annuity Application test included opening web pages, accepting inputs, replicating the order in which an agent would enter data. The workflow is described in Table 4.

Life Insurance Application Test

Test Description

Term Insurance and Whole Life used the same procedure

Open Term Life (or Whole Life) Insurance Application – Visual Pages

• Login

• WelcomeScreen

• Inthenewapplicationworkflow:

- Product Type Selection

- Product Selection

- Insured and Owner Information

- Personal and Medical History

- Policy and Premium Information

- Replacement Information

- Application Summary

- Forms Summary

• ApplicationList

• Logout

Table 3: Workflow for the Term Life and Whole Life Insurance Application Tests

Immediate Annuity Application Test

Test Description

Immediate Annuity Application

Open Immediate Annuity Application – Visual Pages

• Login

• WelcomeScreen

• Inthenewapplicationworkflow:

- Product Type Selection

- Application Details

- Product Selection

- Annuitant Information

- Joint Owner/Annuitant Information

- Joint Owner Confirmation

- Rule Group Package

- Premium Information

- Additional Contract Information

- Financial stream summary information page

- Add a Stream

- Stream Information

- Fund Information

- Financial stream summary information page (Return here after adding details)

- Replacement Information

- Application Summary

- Forms Summary

• ApplicationList

• Logout

Table 4: Workflow for the Immediate Annuity Application Test

5

For all three application entry tests randomized data was used for key fields in the questionnaire such as names and Social Security numbers. The application entry tests were done primarily within the application server VM and the database until the very end of the application entry when a request is sent to the process server, via the message queue, for underwriting processing.

Policy Maintenance (Modify Customer Address) TestPolicy maintenance testing was based upon the common task of modifying a customer address. The policy maintenance workflow is shown in Table 5.

The policy maintenance test is done entirely within the application server VM and database.

Financial Maintenance (Create and Apply Suspense) TestFinancial maintenance testing was based upon the common task of create and apply suspense—the process of receiving a payment and manually matching the payment to the contract. In this test the payment was applied immediately, creating a billing premium payment transaction. The financial maintenance workflow for the test is shown in Table 6.

The financial maintenance test is done entirely within the application server VM and database.

Policy Maintenance Test

Test Description

Modifying a Customer Address

Open Modify Customer Address – Visual Pages

• Login

• WelcomeScreen

• ContractSearch

• ContractSearchResults

• ContractSummaryPage

• ContractRelationshipsPage

• CustomerProfilePage

• CustomerAddressManagementPage

• Intheaddresschangeworkflow:

- Address Edit screen

- Address summary

• CustomerAddressManagementPage(Returnhereafterworkflow)

• Logoff

Table 5: Workflow for the Policy Maintenance Test

Financial Maintenance Test

Test Description

Create and Apply Suspense

Open Create and Apply Suspense – Visual Pages

• Login

• WelcomeScreen

• SuspenseList

• IntheSuspenseworkflow:

- Suspense Information Entry

- Suspense Summary

• SuspenseList(Returnhereafterworkflow)

• Logoff

Table 6: Workflow of the Financial Maintenance Test

6

Configurations for Online Load & Scalability TestThe five web test scenarios described above were combined into a Visual Studio load test. The load test was configured for the number of users assigned to each web test, the number of times each user should perform the test per hour and the time of the test itself.

Two versions of load tests were created:

•Single User Baseline. In the single user baseline, each load test was run one time sequentially by one user. The results were calculated in the same way that they would be if a large load had been placed on the system. This allowed for a comparison between how ALIP responds when only a single user is performing a task versus when a large load is placed on the system.

•Multiple User Testing. The multiple user testing simulated 3,000 simultaneous users performing the individual test scenarios for 1 hour.

To simulate real-life workplace usage, each of the 3,000 users generated one action, drawn from the five web tests, during the hour of testing. One simulated person entered data for a new Term Life insurance policy, while another modified a customer address. The workloads from all 3,000 users were executed within the hour. Configuration for the 3,000 user assignments, as shown in Table 7, were for 200 users to enter Whole Life insurance applications, while 400 entered data for Term Life applications and 400 for an immediate Annuity application, while 1,000 performed a create and apply suspense, and 1,000 modified a customer address. Note there is slight variation in the requested pace per hour and the actual total tests in the hour, as a function of how the test engine randomized the total flow of assignments. For example, 200 Whole Life applications were scheduled to be entered, and 205 were actually entered, as shown in Table 7. So the actual tests performed are actually a bit higher.

Test Findings The online load testing showed impressive scalability, as there was very little difference between page loading times when comparing the single-user results to those of the 3,000-user test. As shown in Table 8, the average page serving time for a single user was 0.21 seconds difference, compared to the page load time with 3,000 users of 0.25 seconds—a mere 0.04 seconds difference. A total of 228,615 pages were served up during the 1 hour test, processing 1,764,999 individual requests.

Testing Distribution for Online Load & Scalability Testing

Test Users Requested Pace Total Tests in 1 Hour

Term Life Application Entry 400 1 per hour 424

Whole Life Application Entry 200 1 per hour 205

Immediate Annuity Application Entry 400 1 per hour 352

Suspense Create and Apply 1000 1 per hour 1,056

Modify Customer Address 1000 1 per hour 1,050

Table 7: Total Distribution

Test Results for Online Load & Scalability Testing Showing Page Loading Times

Criteria Single User 3,000 Users

Number of unique pages hit 29 29

Average page time overall (in seconds) 0.21 0.25

Pages served in less than 1 second (number & percentage)

27/93% 27/93%

Pages served in less than 1 to 2 seconds (number & percentage)

2/7% 1/3.5%

Pages served in more than 2 seconds 0/0% 1/3.5%

Table 8: Page Serving Time Test Results

Testing also found that the percent of pages served in less than 1 second remained at 93% when scaling from one user to 3,000, as shown in Table 8. The single page that exceeded 2 seconds after scaling to 3,000 users only exceeded the threshold by 0.05 seconds, loading in at 2.05. When looking at CPU usage, we also find enterprise-grade scalability. While CPU usage was negligible for a single user, increasing the load by a factor of 3,000, only raised CPU utilization to an average of about 45%. The ALIP architecture and Azure deployment means that at any point you could reduce CPU use further by simply adding another application performance node.

Note: The 1 page that exceeded 2 seconds had a response time of 2.05 seconds

7

Batch TestAccenture also tested performance of batch jobs. ALIP batch jobs require specific data to exist at the contract level. As such, the number of contracts for each test varies slightly, ranging in these tests from approximately 75,000 to 100,000 contracts.

The jobs used for the batch testing loads are shown in Table 9.

Batch Test Workloads

Batch Job Name Description

Billing Determines which contracts need to be billed for upcoming premium and generates a billing record for further processing. Testing used an electronic fund transfer (EFT) mode of payment which also generates a pending Billing Premium Payment transaction.

Premium Due Date (PDD)

Analyzes a set of contracts to determine when a premium payment is next due on the contract, as well as payment details, including amount due. The PDD batch then creates a pending transaction for the premium due date.

Cycle The cycle job can include a wide variety of operations on contracts. For testing, 3 common cycle activities were used:

• Billing.Similartothebillingbatchjobdescribedabove.Fortestingonly EFT transactions were used.

• CashValueQuote.Determinesthecurrentcashvalueoftheinsurance or annuity contract.

• TransactionProcessing.Processanypendingtransactionsonthecontracts, such as premium due date and billing premium payment transactions. The Billing and PDD jobs perform the necessary calculations and stores the data, but the actual changes to the contract are applied during cycle.

Table 9: Description of Batch Test Workloads

Table 10: Results of Batch Testing

Batch Test Results

Job NameContracts Processed

Contracts per Second

Time in Seconds

Time in Minutes

Billing 100,000 50.19 1992.54 33.21

PDD 99,970 19.37 5161.30 86.02

Cycle 74,995 18.16 4129.88 68.83

8

Test Findings

The batch testing, with its online transaction processing, demonstrated that ALIP deployed on the Azure platform provides Tier 1 levels of processing power. As shown in Table 10, the billing batch job processed 100,000 contracts at a rate of 50 contracts per second, completing the batch in just 33 minutes. The complex cycle batches were handled at a rate of 18 contracts per second, completing some 75,000 contracts in just under 69 minutes.

CPU usage for the cycle test, as shown in Chart 1, was about 50% for the database server and about half that for the two ALIP process servers. The initial spike for the process servers reflects loading the contract data, along with efficiency gains after the spike from caching.

Chart 1: Cycle test CPU utilization for database (red) and policy servers (blue and green)

Chart 2: Cash value quote CPU utilization for database (red) and process servers (blue and green)

9

Accenture testers performed a final batch test, using a single component of the cycle batch—the calculation-heavy job of generating cash value quotes—for half a million contracts. The cash value quote was run against 499,683 contracts at a rate of 68.24 per second, to accomplish the task in just over two hours (122.05 minutes). CPU usage for this test is shown in Chart 2, with the database showing about 50% utilization and the two ALIP process servers running at about 20%.

Test Team ObservationsThe Accenture testing team was impressed with what the testing revealed about deploying ALIP on the Azure platform. “Going into the tests, our assumption was that the cloud-based version of ALIP would be slower than what our tests had previously found using our own physical servers,” one engineer said. “What we found instead was that ALIP on Azure was actually faster—with heavier workloads.”

Another engineer underscored that the testing was performed with very heavy loads. “We purposely chose the most demanding jobs to create very heavy workloads,” the engineer said. “We didn’t cherry pick. In fact, we worked with volumes that exceed what many Tier 1 insurers would likely face in a single night.”

Summary Benchmark testing of ALIP on Azure, supported by Microsoft SQL Server 2014 data management software and running on the Windows Server 2012 R2 operating system, found that ALIP provided the performance and scalability to meet the needs of Tier 1 insurance carriers. Scalability was demonstrated by the ease with which workloads were scaled, with average page serving time of 0.21 seconds for a single user rising by only 0.04 to 0.25 seconds when scaled to 3,000 users.

Testing also showed no change from the 93% of pages delivered sub-second when scaled from 1 to 3,000 users. High availability can be achieved through ALIP’s robust architecture and the ease with which work can be transferred from one Azure node to another, on the fly.

About Microsoft Founded in 1975, Microsoft (Nasdaq “MSFT”) is the worldwide leader in software, services, and solutions that help people and businesses realize their full potential. For more information about Microsoft products and services, visit www.microsoft.com.

Copyright © 2016 Accenture All rights reserved.

Accenture, its logo, and High Performance Delivered are trademarks of Accenture. This document is produced by consultants at Accenture as general guidance. It is not intended to provide specific advice on your circumstances.

About Accenture Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations. Combining unmatched experience and specialized skills across more than 40 industries and all business functions—underpinned by the world’s largest delivery network—Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders. With more than 358,000 people serving clients in more than 120 countries, Accenture drives innovation to improve the way the world works and lives. Visit us at www.accenture.com

Copyright © 2016 Microsoft. All rights reserved. This document is provided “as-is.” Information and views expressed in this document, including URL and other Internet Web site references, may change without notice. This document does not provide you with any legal rights to any intellectual property in any Microsoft product. You may copy and use this document for your internal, reference purposes.