2 CONTENTS 1. Summary 2. Objectives 3. Methodology and Approach 4. Results 5. Next Steps 6. Glossary 7. Appendix EXECUTIVE SUMMARY Tenzing Managed IT services has recently partnered with Amazon Web Services (AWS) enabling Magento Merchants to combine the flexibility and scalability of AWS with the expertise and managed services that Tenzing provides. Prior to the official launch, Tenzing conducted beta testing of its reference architecture using real-world customer data. Tenzing Managed IT Services partnered with several Magento Solution Partners for assistance in this phase of the Magento on AWS project. This report details beta phase activities as well as summarizes findings. 1.1 Objective The objective of the beta phase was to build a functional Magento store based on real-world sources (versus vendor sample data) on the Amazon Web Services (AWS) platform using Tenzing s optimized Magento reference architectures for AWS as well as common AWS components and services. Once built, Tenzing tested the performance of the store to ensure the AWS solution was comparable to other offerings. The beta phase provided the participating systems integrator with details on how customer sites would be built on the platform, as well as access to review the demo sites. This allowed the Sis the opportunity to recommend updates and refinements to the offering, based on customer needs. 1.2 Scope This case study includes an outline of the build and testing that was done as part of the beta phase as well as presentation and discussion of results. 1.3 Methodology and Approach The architecture was devised to leverage and fully exploit AWS services. Systems were built and tested using site and data provided by the SIs on the Magento Enterprise application software. Tenzing Magento on AWS Performance Case Study 2 P a g e
3 Both Tenzing and the SI conducted basic functional testing and review. Limited non-functional testing was conducted by Tenzing using Apache Jmeter, Blazemeter.com, and webpagetest.org. Informal requirements 1 as they relate to traffic were provided by the systems integrator and used as a basis for analysis of non-functional test results. The following configurations were tested: A single application and a single database server Two load-balanced application servers and a single active database with failover (active/passive). 1.4 Results Section 3 of this document details the method used to estimate traffic and load requirements at average and at peak. Section 7 documents the origins of these requirements. Table 1 details the results of performance testing. Results were the same in both configurations. Test Detail Ability to satisfy average daily load of ~2500 unique site visitors. Ability to satisfy historical peak monthly load of ~ unique site visitors. Test Result Successful Successful 1.5 Conclusions Beta results show that a build of the Magento Store on AWS satisfied all outlined objectives and requirements and demonstrated improved performance to the current production environment. Table 1: Results 1 In a questionnaire distributed to the SI, it was indicated that while there are no formal requirements with the end-customer regarding load, traffic, etc., there is an expectation that the site should has been able to satisfy certain traffic. These details are documented in Section 7. Tenzing Magento on AWS Performance Case Study 3 P a g e
4 OBJECTIVES Part of the beta testing phase for Tenzing s Magento on AWS service offering required test of the solution and configuration with real-world data; partnering with Magento Solution Partners allowed Tenzing to accomplish this objective. In addition, this partnership seeks to illustrate the benefits of a public cloud platform as a potential alternative for a managed Magento Optimized hosted environment. Table 2 summarizes the objectives for the beta test activities. Objective Description 1 Environment setup and site load. 2 Functional testing 3 Performance testing & tuning Table 2: Beta Activities and Objectives 2.1 General Functional Requirements The following table lists the required elements of the AWS hosted test site: Requirement 1151-FR FR FR3 Detail All content is available for navigation with no errors encountered. Applicable Magento features and functional are available (admin page, Full Page Cache, etc.). End-to-end transactions are possible (adding a product to the cart, view cart, checkout) Table 3: Site Requirements Summary 2.2 General Non-Functional Requirements In addition to functional requirements, several non-functional items were deemed required elements of an AWS hosted test site: Requirement 1151-NFR NFR NFR3 Detail Flexibility: the ability quickly to meet increased capacity and demand - quickly scaling up to meet increased customer traffic and then scaling down when traffic decreases in an automated fashion. Agility: the ability to respond to unforeseen event, such as recovering from physical disasters, etc. Performance: the ability to accomplish work required compared to the time and resources consumed. In this case, response times of the test site should be comparable to the production site. Table 4: Site Requirements Summary 2.3 Specific Customer Requirements A few informal requirements were gathered and generated based on discussions and feedback from the systems integrator. These items are detailed below 2 : Assumption 1151-CR CR2 Detail The site must be able to handle at least 2500 unique visitors per day. The site must be able to handle approximately visits per month at peak. Table 5: Assumptions Summary 2 See Section 7 for additional details Tenzing Magento on AWS Performance Case Study 4 P a g e
5 METHODOLOGY AND APPROACH The site was configured to use small and large infrastructure definitions that allow alternatives for particular use-cases, which will be outlined in the respective sections. All test sites utilized the following components: Name Function Version PHP-APC PHP Opcode cache Magento EE Commerce Platform Nginx Web Server PHP Middleware PHP-FPM FastCGI Process Manager MySQL Database Server MySQL (RDS 3 ) Table 6: Software Components Additional tuning was performed on several components following Magento best practices and available data. The components above were tested with the following tools for purposes outlined below: Tool Jmeter Blazemeter.com Webpagetest.org Function Jmeter was used to test concurrency and response time of the test site under load. Testing with Jmeter utilized a single test node, local to the test Magento site components (within the same AWS Availability Zone, VPC, and subnet). Blazemeter.com uses Jmeter scripts to simulate multiple users from multiple sources. The same parameters used to test locally were used to test concurrency and load remotely. 4 As comparisons regarding concurrency and load were not available, Webpagetest.org provided a sample for comparison related to page load times, user experience, etc. Table 7: Test Tools 3.1 Small Test Configuration The small configuration consists of a minimal number of components that would satisfy smaller, lower traffic sites that may not need high availability. Additionally, testing a minimal number of components provided baseline for core system capabilities prior to adding ancillary components (Elasticache, etc.). A single, more powerful server, or instance, was used in this configuration Components The following components and arrangement comprised the small test site. Service Name Service Function Sizing CloudWatch Monitoring 1 minute EBS Persistent block storage 25 GB (x1) EC2 Compute c34.xlarge (x1) 5 RDS Database services db.m3.large (x1) provisioned IOPs GB storage 8 3 See Section 6 for further details on RDS. 4 Remote testing adds several variables and intermediaries that are not present during local tests. Thus, these tests are meant to be indicators versus definitive or extensive performance numbers, which would require additional resources to attain. 5 AWS EC2 Instance Types 6 AWS RDS Instance Types is the minimum number of provisioned IOPs available GB is the minimum required amount of storage to utilize provisioned IOPs. Tenzing Magento on AWS Performance Case Study 5 P a g e
6 Route53 DNS services N/A Table 8: Small Infrastructure Components 3.2 Large Test Configuration The large configuration consists of an expanded number of components that would satisfy larger, higher traffic sites that also may need high availability options. As load will be distributed, 2 x c32.xlarge9 instances were used and provided comparable performance to a single c34.xlarge at the same price point and provide availability across 2 physical locations. Fail-over scenarios are tested Components The following components and arrangement comprised the large test site. Service Name Service Function Sizing (count) Auto Scaling Facilitates automated horizontal scaling N/A CloudFront Content Delivery Network N/A CloudWatch Monitoring 1 minute EBS Persistent block storage 25GB (x1 per EC2 instance) EC2 Compute c32.xlarge (x2) 10 ElastiCache Caching services (memcached) m1.medium (x1) Elastic Load Balancer Load balancing services (x1) RDS Database services db.m3.large (x1) provisioned IOPs GB storage 13 Route53 DNS services N/A Table 9: Large Infrastructure Components 9 Instance number and type can be changed with minimum effort. 10 AWS EC2 Instance Types 11 AWS RDS Instance Types is the minimum number of provisioned IOPs available GB is the minimum required amount of storage to utilize provisioned IOPs. Tenzing Magento on AWS Performance Case Study 6 P a g e
7 Figure 1: Small Infrastructure Diagram Tenzing Magento on AWS Performance Case Study 7 P a g e
8 Figure 2: Large Infrastructure Diagram Tenzing Magento on AWS Performance Case Study 8 P a g e
9 3.3 Requirements Testing Requirement 1151-FR FR FR3 Test Detail Manual site navigation: static content, catalog, and product pages. Ensure that admin page is accessible and all applicable EE features are enabled. Clear cache directories, browse site, verify that cache directories get populated. Add product to cart Checkout as Guest Checkout as logged in user Login to admin and invoice/ship orders Requirement 1151-NFR NFR NFR3 Test Detail Create appropriate AutoScaling Group, and Elastic Load Balancer and run test simulation Vertically scale template that includes fully configured customer data Create applicable infrastructure in multiple zones; terminate active nodes to simulate failure and test failover to standby node in separate physical location. Test and collect data on concurrency and response time, comparing results to the production site where possible. Assumption 1151-CR CR2 Test Detail Create and execute tests using Jmeter, Blazemeter.com, or webpagetest.org or a combination thereof based on details in Section 3.4 and Section 7. Create and execute tests using Jmeter, Blazemeter.com, or webpagetest.org or a combination thereof based on details in Section 3.4 and Section 7. Table 10: Site Requirements Summary Tenzing Magento on AWS Performance Case Study 9 P a g e
10 3.4 Site Visitor Calculation The test site is required to handle on average 2,500 visitors per day and 120,000 at peak per month 14. Given that traffic would not be equal in a 24 period, a more realistic 4 hour distribution period was assumed. Average Users Peak Users Users Time Users Time sec sec min /hour min /hour hour /day hour /day day /month day /month month /year Table 11: Average and Peak Users From the conversion outlined above, as peak users per second are less than 1, for test purposes 1 user per second was used; assuming 1 user per second distributed over a 4-hour period, the following table illustrates the number of users over various lengths of time. Test Users Users Time 1 60 sec min /hour hour /day day /month Table 12: Average and Peak Test Users As a result of limited availability of site user behavior data, these simulations simply added 1 user every second for 60 seconds, held the 60 user traffic constant for a seconds, and then reduced the number of simulated users; this cycle was then repeated one additional time. Average results for response time and concurrency were recorded. Several pages were included as part of testing 15. Of these the first grouping simulated users visiting home and static pages with the next group in the test cycle visiting catalog and product pages. NOTE: While incorporating checkouts and transactions were not part of the load test scripts, some manual checkouts were performed with no excessive degradation in response time. 14 See Section 7.4 for details. 15 See Section 7.1 for details. Tenzing Magento on AWS Performance Case Study 10 P a g e
11 RESULTS The following section summarizes the results of the tests conducted and whether or not requirements detailed in Section 2 were successfully met and discusses additional implications. Requirement Detail Results 1151-FR1 All content is available for navigation with no Success errors encountered FR2 Applicable Magento features and functional are Success available (admin page, Full Page Cache, etc.) FR3 End-to-end transactions are possible (adding a product to the cart, view cart, checkout) Success Requirement Detail Results 1151-NFR1 Flexibility: the ability quickly to meet increased capacity and demand - quickly scaling up to meet increased customer traffic and then scaling down when traffic decreases in an automated fashion. Success: AWS provides mechanisms such as Auto Scaling to accomplish this requirement. The site was able to scale beyond its original configuration NFR NFR3 Agility: the ability to respond to unforeseen event, such as recovering from physical disasters, etc. Performance: the ability to accomplish work required compared to the time and resources consumed. In this case, response times of the test site should be comparable to the production site. Success: Testing of Multiple Availability Zone fail-over was successful with minimal service interruption (within minutes) for the Large configuration. Success: Average of total response time with local and remote tests was minimal 16 and testing conducted via websitetest.org was comparable. Assumption Detail Results 1151-CR1 The site must be able to handle at least 2500 unique visitors per day. Success: Based on current test methodology, both configurations were successful CR2 The site must be able to handle approximately visits per month at peak. Success: Based on current test methodology, both configurations were successful. Table 13: Assumptions Summary 16 See Section 7.2 for full details Tenzing Magento on AWS Performance Case Study 11 P a g e
12 Figure 3: BlazeMeter.com Average Response Times and User Distribution Given that load testing and performance data were not available for the production site, online test tools were used for comparison. Below is an example of comparable page load times of the same page from both the production and test sites. The first two examples are results from the test site. The third example shows results of the current production environment. In both instances, the test site outperformed the current production site. Figure 4: webpagetest.org Results: Test Site Tenzing Magento on AWS Performance Case Study 12 P a g e
13 Figure 5: webpagetest.org Results: Test Site 17 Figure 6: webpagetest.org Results: Production Site 17 The AWS CloudFront CDN was used in this sample Tenzing Magento on AWS Performance Case Study 13 P a g e
14 4.1 Implications Section 7.3 illustrates the approximate AWS cost for both small and large configurations used for testing. While both were able to satisfy the requirements as defined in previous sections, a public cloud platform further allows for dynamic and rapid infrastructure changes to accommodate evolving requirements while also introducing unique opportunities to reduce cost. For instance, Section 7.2 illustrates the acceptable results of the identical load used in all tests on a single c32.xlarge (differing from the single instance testing in Section 4 for the small configuration consisted of a single more powerful c34.xlage instance, while the large configuration used 2 x c32.xlarge instances and distributed load between these generally halving the results shown in Section7.2: Table 14 per instance). In this case a smaller server, or instance type, can be substituted to meet these specific requirements at reduced pricing compared to the alternative. Nevertheless, should requirements change and demand more resources, a larger server, or instance type 18, can be rapidly put into service. Horizontal scaling is also an option where feasible 19. Further, in Section 7.2: Table 17 illustrates that database resources for the instance type chosen 20 were far from exhausted during testing, even in testing without the use of caching mechanisms such as memcached. Thus, multiple opportunities exist to reduce cost through the use of resources that can be right sized but still flexible and dynamic. This eliminates the need to lock-in to resources that are sized and priced at estimated peak usage, as is the case with traditional physical infrastructure. 18 Section 7.2: Figure 12 illustrates CPU resource usage when scaling from a c32.xlarge (the first peak) to a c34.xlarge (the plateau) under the same load. The final peak illustrates CPU consumption with 1.5x load on a c3.4.xlarge. Figure 11 and Table 16 also show the corresponding response and transactions per second of the additional 1.5x load on the more powerful c43.xlarge instance. 19 Magento licensing costs and models will directly affect horizontal scale. 20 db.m3.large with 1000 provisioned IOPs Tenzing Magento on AWS Performance Case Study 14 P a g e
15 CONCLUSION Based on the tests conducted, the AWS test site has successfully accomplished the established requirements. A build of the Magento Store on AWS satisfied all objectives and requirements and in preliminary comparisons showed better performance than the current production site. Tenzing Magento on AWS Performance Case Study 15 P a g e
16 NEXT STEPS A public cloud platform such as AWS provides a number of alternative configuration options to satisfy dynamic requirements as well as cost-effective options for availability and disaster recovery. Given the positive results of the beta testing Tenzing teams will continue to investigate the potential applications and benefits of AWS infrastructure for Magento merchants. Tenzing also aims to combine its commerce expertise to further improve performance on AWS. One example would be by incorporating advanced managed services into the monitoring program for AWS. APM Assure is a current Tenzing product and provides tremendous benefit by breaking down an entire transaction stack into each procedure call. It then orders by method time allowing the reviewer to simply scroll down the list and view anything that may be out of order. Using APM Assure on the test site, 2 potential performance concerns were revealed: 1. The following procedure may need review due to the number of times the procedure is called. The following seems to indicate that images are being opened and/or saved while just browsing the site. Figure 7: APM Assure Results: HOT SPOT Example 1 NOTE: Upon further investigation, the first Hot Spot disappeared once Compilation was enabled within the Magento Admin site. The second Hot Spot remains. Tenzing Magento on AWS Performance Case Study 16 P a g e
17 2. The procedure labelled GetSortAttributes is being called multiple times per transaction and may need optimization. Figure 8: APM Assure Results: HOT SPOT Example 2 While just an example from a test site, this data attempts to demonstrate of the level of visibility and added value that Tenzing can provide. Tenzing Magento on AWS Performance Case Study 17 P a g e
18 GLOSSARY Auto Scaling Auto Scaling allows scaling of Amazon EC2 capacity up or down automatically according to defined conditions. With Auto Scaling, the number of Amazon EC2 instances increases seamlessly during demand spikes to maintain performance, and decreases automatically during demand lulls to minimize costs. EBS: Elastic Block Storage Amazon Elastic Block Store (Amazon EBS) provides persistent block level storage volumes for use with Amazon EC2 instances in the AWS Cloud. Each Amazon EBS volume is automatically replicated within its Availability Zone to protect you from component failure, offering high availability and durability. EC2: Elastic Cloud Compute EC2 allows users to rent virtual computers on which to run their own computer applications. EC2 allows scalable deployment of applications by providing a Web service through which a user can boot an Amazon Machine Image to create a virtual machine, which Amazon calls an "instance", containing any software desired. A user can create, launch, and terminate server instances as needed, paying by the hour for active servers, hence the term "elastic". EC2 provides users with control over the geographical location of instances that allows for latency optimization and high levels of redundancy. ElastiCache ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory caches, instead of relying entirely on slower disk-based databases. ElastiCache supports two open-source caching engines: memcached and Redis. ELB: Elastic Load Balancer Elastic Load Balancing automatically distributes incoming application traffic across multiple Amazon EC2 instances. It enables greater levels of fault tolerance in applications, seamlessly providing the required amount of load balancing capacity needed to distribute application traffic. Elastic Load Balancing detects unhealthy instances and automatically reroutes traffic to healthy instances until the unhealthy instances have been restored. RDS: Relational Database Service Amazon Relational Database Service is a distributed relational database service by Amazon.com. It is a web service running "in the cloud" and provides a relational database for use in applications. It is aimed at simplifying the setup, operation, and scaling a relational database. Tenzing Magento on AWS Performance Case Study 18 P a g e
19 APPENDIX 7.1 Test Results Table 14: Response in Milliseconds Single c32.xlarge Figure 9: Transactions per second Single c32.xlarge Tenzing Magento on AWS Performance Case Study 19 P a g e
20 Table 15: Response in Milliseconds Single c34.xlarge Figure 10: Transactions per second Single c34.xlarge Table 16: Response in Milliseconds Single c34.xlarge 1.5x load) Tenzing Magento on AWS Performance Case Study 20 P a g e
21 Figure 11: Transactions per second Single c34.xlarge 1.5x load) Figure 12: CPU Consumption (c32.xlarge / c34.xlarge) Database / RDS Resource Name Average Used Maximum Used CPU 4% 9% Memory 2GB 2GB Read IOPs Write IOPs Table 17: Database Resource Consumption Tenzing Magento on AWS Performance Case Study 21 P a g e
22 7.3 AWS Simple Calculator / Pricing Small Configuration: $ /month Large Configuration: $ /month Traffic Metrics / Assumptions Traffic Metrics/ Assumptions Note: If statistics are not available, please provide any estimates or projections. How many total unique visitors do you expect the current site can accommodate at peak? I am unsure, but GA says the site averages about 2,500 unique visitors/day with no issues How many total transactions / orders do you expect the current site can accommodate at peak? I am unsure, but GA says the site averages about 40 orders/day with no issues Figure 14: Excerpt from page 3 Magento Beta Questionnaire.doc Tenzing Magento on AWS Performance Case Study 22 P a g e
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Cloud and the future of Unemployment Sean Rhody, CTO Capgemini Government Solutions Agenda Current State Frustrations Evolving Tax Solutions PaaS, SaaS, IaaS and you Changing the Model Q&A 1 Current State
WEBAPP PATTERN FOR APACHE TOMCAT - USER GUIDE Contents 1. Pattern Overview... 3 Features 3 Getting started with the Web Application Pattern... 3 Accepting the Web Application Pattern license agreement...
15-319 / 15-619 Cloud Computing Recitation 5 September 29 th & October 1 st 2015 1 Overview Administrative Stuff Last Week s Reflection Project 2.1, OLI Modules 5 & 6, Quiz 3 New concepts This week s schedule
Best Practices for Siebel on AWS Contributors The following individuals and organizations contributed to this document Ashok Sundaram, Solutions Architect, Amazon Web Services Milind Waikul, CEO, Enterprise
SCALEOUT SOFTWARE Using In-Memory Data Grids for Global Data Integration by Dr. William Bain, ScaleOut Software, Inc. 2012 ScaleOut Software, Inc. 12/27/2012 B y enabling extremely fast and scalable data
1. Computation Amazon Web Services Amazon Elastic Compute Cloud (Amazon EC2) provides basic computation service in AWS. It presents a virtual computing environment and enables resizable compute capacity.