{"id":25970,"date":"2020-07-08T22:26:24","date_gmt":"2020-07-08T20:26:24","guid":{"rendered":"https:\/\/www.intellias.com\/?p=25970"},"modified":"2024-07-16T12:41:44","modified_gmt":"2024-07-16T10:41:44","slug":"optimizing-cloud-infrastructure-for-automotive-map-compilation","status":"publish","type":"post","link":"https:\/\/intellias.com\/optimizing-cloud-infrastructure-for-automotive-map-compilation\/","title":{"rendered":"Cloud Optimization Services for Automotive Map Compilation"},"content":{"rendered":"<h2 id=\"business-challenge\">Business challenge<\/h2>\n<p>Since 2015, Intellias has been a technology partner of the world\u2019s top provider of mapping data and location-based services, delivering software engineering, consulting, DevOps, <a href=\"https:\/\/intellias.com\/cloud-development-services\/\">cloud optimization services<\/a>, and many more. As an integral part of one project for this client, the Intellias team adapted the architecture of an automotive-grade navigation solution to comply with the Navigation Data Standard (NDS). The key objective of NDS is to standardize map formats to enable compatibility and interoperability among a growing variety of NDS-compliant applications and devices.<\/p>\n<p>The NDS map format relies on SQLite technology. Thus, the database for the navigation solution actually consists of several product databases, which are further divided into update regions. This supports flexible and consistent versioning for NDS databases and makes it possible to integrate data from different suppliers into one map.<\/p>\n<p>Our client\u2019s initial database was deployed on Oracle Exadata, but our team discovered some disadvantages in the course of its maintenance, which could be eliminated with our cloud optimization services.<\/p>\n<ul>\n<li><b>Limited scalability<\/b><br \/>\nExadata required our client to buy new virtual machines for every three to four projects and featured considerable delivery times (reaching half a year) for new projects.<\/li>\n<li><b>Low performance<\/b><br \/>\nOracle Exadata could compile three update regions in 24 hours. In addition, compiling one region influenced the performance of other product databases.<\/li>\n<li><b>High costs<\/b><br \/>\nExpensive Exadata storage turned out to be inefficient for the project, as data was not used after compilation.<br \/>\nThe development and production environments were duplicated.<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-25971\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2020\/07\/Optimization_text-image.jpg\" alt=\"Cloud Optimization Services for Automotive Map Compilation\" width=\"770\" height=\"450\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2020\/07\/Optimization_text-image.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2020\/07\/Optimization_text-image-300x175.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2020\/07\/Optimization_text-image-768x449.jpg 768w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/p>\n<h2 id=\"cloud-optimization-services-delivered\">Cloud optimization services delivered<\/h2>\n<p>Once we identified the key business drivers for migration, the next milestone of our cloud optimization services was to select a cloud environment that would provide the most value in terms of efficiency, scalability, operability, and cost savings. We assessed the Amazon Relational Data Service (RDS) and Amazon Elastic Cloud Compute (EC2) and opted for EC2 to get more flexibility and control over the database.<\/p>\n<p>Our team also presented our client with an effective <a href=\"https:\/\/intellias.com\/automotive-cloud-devops\/\">cloud automotive strategy<\/a> for continuous cloud optimization according to a three-stage plan. Any improvements to production are driven by a thorough analysis by the DevOps team and a trial run in the development environment with the cloud optimization services. This cloud optimization strategy ensures quick, seamless, and on-time continuous improvement without any loss of data or other issues in production.<\/p>\n<h3>Cloud optimization and migration from Oracle Exadata to Amazon EC2<\/h3>\n<p>During the active development phase and cloud optimization of the project, Intellias found an opportunity to dramatically improve the project\u2019s overall performance and scalability as well as reduce cloud automotive infrastructure costs by migrating the existing infrastructure to Amazon Web Services. Migrating the map compilation pipeline, clipping environments, and testing framework to <b>Amazon EC2<\/b> resulted in<\/p>\n<ul>\n<li>cutting the overall build generation time in half, which allowed for cloud optimization of the map build timeframe with nightly builds;<\/li>\n<li>running an unlimited number of projects in parallel in fully isolated cloud automotive environments;<\/li>\n<li>launching new projects in two weeks instead of six months ;<\/li>\n<li>cutting the compilation cost by 55% with automotive management cloud software.<\/li>\n<\/ul>\n<h3>Continuous improvement of\u00a0automotive management cloud software and infrastructure<\/h3>\n<p>Our team of DevOps engineers is constantly looking for opportunities to bring additional value to our client by focusing on continuous improvement and operational efficiency of the\u00a0automotive management cloud software. To optimize infrastructure costs and project processes, we established cloud optimization services, daily monitoring of instances, system-wide performance, resource utilization, operational health, and cloud spend. Every morning, we thoroughly analyze logs, metrics, events, and automated notifications to transform actionable insights from Amazon into tangible performance improvements and cost reductions.<\/p>\n<h3>S3 storage efficiency<\/h3>\n<p>One of our approaches to <a href=\"https:\/\/intellias.com\/cloud-cost-optimization-service\/\">optimizing the cost of cloud<\/a> automotive infrastructure is conducting a regular S3 storage audit, resulting in changes to storage lifecycle management. In order to achieve cost efficiency and cloud optimization and avoid unnecessary spending, we follow best practices for the S3 buckets lifecycle, configuring AWS to downgrade the objects class and archive or delete unused data. As part of security enhancements and storage rationalization, we also restricted access to S3 buckets for team members who don\u2019t contribute to project development.<\/p>\n<h3>Weekly releases<\/h3>\n<p>When planning for the transition from project development to maintenance in the scope of our cloud optimization services, the challenge was to find the golden formula enabling us to effectively deliver value on an ongoing basis with high quality and reduced cost. To ensure that customers receive updated maps in a timely manner and to save costs on infrastructure, we decided to adopt a continuous integration approach, eliminating the issue of production builds, and switch to weekly releases, combining map compilation for all updated regions with the execution of auto-tests for cloud automotive services.<\/p>\n<h3>Deterministic builds<\/h3>\n<p>To minimize the number of failed builds, reduce the compilation time, and decrease the size of the update package, we introduced a deterministic compiler for cloud optimization that allows us to verify only parts of the map affected by the latest commits. Having this feature in place, we reduced the scope of map testing and efforts per nightly build by more than a third.<\/p>\n<h3>Monitoring compilation time<\/h3>\n<p>Reducing compilation time is an essential part of optimizing cloud automotive infrastructure resources and one of the ways to achieve considerable cost savings. We built a service responsible for shutting down resources if the compilation time exceeds 12 hours or the resources aren\u2019t required. Implementing this feature allowed us to balance resource availability to reduce costs and provide our DevOps engineers with an automated tool for detecting build issues that can be escalated to the development team for further analysis and resolution.<\/p>\n<h3>Optimizing instances<\/h3>\n<p>To bring on extra capacity only when needed and cut costs for cloud automotive infrastructure, we used next-generation Amazon EC2 instances, including Reserved Instances, On-Demand Instances, and Spot Instances. This cloud optimization saved $200 per instance used, which is 82 percent of the original cost.<\/p>\n<h2 id=\"business-outcome-with-cloud-optimization-services\">Business outcome with cloud optimization services<\/h2>\n<p>We surpassed our goals and optimized the project infrastructure to reap all possible benefits of cloud optimization technology. The whole project migration from Oracle Exadata to Amazon EC2 took over a year and was followed by continuous improvements to the cloud infrastructure and automotive management cloud software. By leveraging the AWS cloud and establishing a continuous improvement process, we significantly strengthened database performance and scalability while <b>reducing the overall total cost of cloud infrastructure ownership by 98.5%.<\/b><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We reduced the overall total cost of ownership for cloud infrastructure by 98.5% by wisely leveraging AWS cloud services <\/p>\n","protected":false},"author":17,"featured_media":64101,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[5],"tags":[12,37,910,23],"class_list":["post-25970","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-study","tag-automotive-tag","tag-cloud-devops","tag-mobility","tag-navigation","technologies-ansible","technologies-apache-hadoop","technologies-aws","technologies-aws-cloud","technologies-bitbucket","technologies-gerrit","technologies-git","technologies-java","technologies-linux","technologies-oracle-database","technologies-pl-sql","technologies-python","technologies-sqlite","technologies-terraform"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.4 (Yoast SEO v23.2) - 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