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Streetline: A smart parking technology company helps to solve parking problems using on-premise Kubernetes with OpenEBS
技术
- 基础设施即服务 (IaaS) - 云计算
- 平台即服务 (PaaS) - 连接平台
适用行业
- 城市与自治市
- 运输
适用功能
- 商业运营
- 物流运输
用例
- 基础设施检查
- 智慧城市运营
服务
- 云规划/设计/实施服务
- 系统集成
挑战
Streetline, a leading company providing parking intelligence software and services, was facing challenges with its first-generation software that was written to run on VMs in enterprise data centers. As the company grew, it started to add services on the cloud and recognized the potential of containerization and Kubernetes as their future platform. However, due to preferences from their customers for their data to be physically segregated from the public cloud, Streetline did not select a hosted offering from a cloud vendor. The team examined CoreOS, Rancher, and other methods to run Kubernetes distributions. They decided to run their own distribution of Kubernetes. Once Kubernetes was deployed and the operations fairly well automated, the focus turned to ways to minimize operational overhead while deploying a variety of stateful workloads including Redis, MySQL, Elasticsearch, and GitLab.
关于客户
Streetline is one of the leading companies providing parking intelligence software and services to governments, enterprises, transit agencies, universities and others worldwide. The company has grown rapidly from its founding in 2005 and along the way has continually innovated its unique technology to remain the leader in smart parking, providing accurate parking policy and availability. Streetline is one of the first in the market to move toward infrastructure-less solutions using patented technology. Streetline products are disrupting the approach to smart parking by expertly using Machine Learning to provide accurate and reliable real-time utilization and parking, tools to help with guidance, parking governance, policy, on-demand pricing, urban planning and more.
解决方案
Streetline selected the open source OpenEBS from MayaData as the container attached storage for their development and operations use cases. OpenEBS was primarily compared to a traditional clustered storage system that was marketed at Cloud Native although it was developed over ten years before the emergence of Kubernetes and widespread containerization. Kelvin conducted a series of tests to include abruptly taking down nodes, having Kubernetes respond to the failed node, and ensuring that the data and service were still available from the underlying storage and then through the workloads. Both OpenEBS - using the Jiva engine initially - and the alternative open source cluster solution proved their resilience. In addition, the storage alternatives were evaluated in their ability to deploy stateful workloads quickly, automatically and be simple to manage. In this consideration, OpenEBS seemed quite different than the alternative solution.
运营影响
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