Contract: DTFH6116D00035
TOPR: HOIT212116217
Submitted to
U.S. Department of Transportation (USDOT)
Federal Highway Administration ITS JPO
Prepared by
Booz Allen Hamilton
8283 Greensboro Drive
McLean, VA 22102
Last updated April 26th, 2024
- Version History
- 1 - Introduction
- 2 - Project Overview
- 3 - System Overview
- 4 - Architecture Pattern
- 5 - JPO ODE Micro-services Topology
- 6 - Appendix
| Version # | Implemented By | Revision Date | What Changed? |
|---|---|---|---|
| 1.0 | Hamid Musavi | ||
| Tony Chen | 1/11/2017 | Initial release of the preliminary/interim design | |
| 1.1 | Hamid Musavi | 4/17/2017 | Updated to reflect ORNL De-identification service |
| 1.2 | Hamid Musavi | 12/21/2018 | General Update |
The JPO Operational Data Environment (ODE) product is being developed under Agile Development Methodologies, using an open architecture approach, in an open source environment. This document describes the preliminary architectural design of the JPO ODE and its interfaces with external systems including the TMC applications, field devices and center services.
All stakeholders are invited to provide input to this document. Stakeholders should direct all input on this document to the JPO Product Owner at DOT, FHWA, JPO.
This document is a living document and will be updated throughout the life of the JPO ODE project to reflect the most recent changes in the ODE design and stakeholder feedback.
An Operational Data Environment is a real-time data acquisition and distribution software system that processes and routes data from Connected-X devices -- including connected vehicles (CV), personal mobile devices, infrastructure components, and sensors -- to subscribing applications to support the operation, maintenance, and use of the transportation system, as well as related research and development efforts.
The ODE is intended to complement a connected vehicle infrastructure by brokering, processing and routing data from various data sources, including connected vehicles, field devices, Transportation Management Center (TMC) applications and a variety of other data users. Data users include but not limited to transportation software applications, Research Data Exchange (RDE), and the Situational Data Exchange (SDX).
As a data provisioning service, the ODE can provision data from disparate data sources to software applications that have placed data subscription requests to the ODE. On the other direction, the ODE can accept data from CV applications and broadcast them to field devices through Road Side Units (RSU)s and the Situational Data Exchange (SDX) which in turn will transmit the data to Sirius XM satellites for delivery to the connected vehicles in the field.
While provisioning data from data sources to data users, the ODE also will perform necessary security / credential checks and, as needed, data validation and sanitization.
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Data validation is the process of making a judgment about the quality of the data and handling invalid data as prescribed by the system owners.
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Data sanitization is the modification of data as originally received to reduce or eliminate the possibility that the data can be used to compromise the privacy of the individual(s) that might be linked to the data.
JPO ODE is an open-source software application that will enable the transfer of data between field devices and backend TMC systems for operational, monitoring, and research purposes. The system will enable applications to submit data through a variety standard interfaces as illustrated in the figure below.
Figure 1 illustrates the JPO ODE technology stack and components within each technology layer.
Figure 1 - ODE Technology Stack
Data Integration later of JPO-ODE supports the producers and consumers of CV data as illustrated in Figure 2 below. It is possible that not all components or services shown in this diagram have been implemented. The implementation timeline for the identified interfaces will depend on the needs of the JPO ODE customers and the priority of these capabilities to the JPO-ODE product owner.
Figure 2 - ODE Data Integration Clients
The JPO-ODE architecture is designed to support the following mechanisms for inputting ASN.1 encoded Basic Safety Messages (BSM)s, Traveler Information Messages (TIM)s in a human readable Java Script Object Notation (JSON), environmental and various other system logs.
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Streaming Data Producers (Direct): Applications can directly interact with the messaging service through the use of the service's native API and publish messages to be processed by the ODE. This interface is suitable only to applications residing inside a private network domain.
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Streaming Data Producers (WebSocket): Applications can interact with the messaging service and publish messages to be processed by the ODE. This interface is suitable to all applications whether residing in the private network domain or in the cloud. For cloud applications Secure WebSocket (wss) protocol will be required.
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RESTful API Data Producers: Applications can connect with the ODE though a RESTful API and submit messages to the messaging service through HTTP POST commands. This interface is suitable to all applications whether residing in the private network domain or in the cloud. For cloud applications Secure HTTP (https) protocol should be utilized.
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File System Data Producers: Encoded message files and log files messages can be dropped into a shared file system location and systematically pulled in to the data broker. This interface is suitable to applications residing in the private network domain or in the cloud. This interface should only be utilized with Secure Copy (scp) protocol.
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Database Data Producer: A shared database where encoded messages are stored can also be connected directly into the ODE to monitor and process new records. This interface is suitable only to applications residing in the private network domain.
The JPO-ODE is designed to support the following mechanisms for outputting decoded BSM, Map, and Signal Phase & Timing (SPaT) data as well as encoded TIM data.
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Streaming Data Consumers (Direct): Applications can subscribe directly to the messaging service through the use of the messaging service's native API. This interface is suitable only to applications residing in the private network domain.
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Streaming Data Consumers (WebSocket): Applications can subscribe to the messaging service through the use of a standard WebSocket API. This interface is suitable to all applications whether residing in the private network domain or in the cloud. For cloud applications Secure WebSocket (wss) protocol should be utilized.
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Database Data Consumers: Data messages can be directly inserted into a shared application database and made available for queries.
ODE is envisioned to provide a management console for functions such as SNMP device management and provisioning. Other configuration functions can be included in a future management console.
JPO ODE architecture is loosely based on a micro-services architecture pattern. The micro-services architecture pattern is a highly scalable design pattern and a viable alternative to monolithic applications1 and service-oriented architectures.
1 In software engineering, a monolithic application describes a single-tiered software application in which the user interface and data access code are combined into a single program from a single platform. A monolithic application is self-contained, and independent from other computing applications.
The micro-services pattern consists of three major concepts:
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Separately deployed units: As illustrated in Figure 2, each component of the micro-services architecture is deployed as a separate unit, allowing for easy deployment, increased scalability, and a high degree of component decoupling.
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Service component: In micro-services architecture, we deal with service components, which can vary in granularity from a single module to a large portion of the application. Service components contain one or more modules (Java classes) that represent either a single-purpose function (e.g., decode BSMs from ASN.1) or an independent portion of a large business application (e.g., sanitize BSM data according to the client request).
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Distributed architecture: All the components within the architecture are fully decoupled from one other and accessed through a messaging service. This concept is what allows microservices architecture pattern achieve some of its superior scalability and deployment characteristics.
Figure 3 - Basic Micro-services architecture pattern
For JPO ODE, a centralized messaging topology is being envisioned. This topology (illustrated In Figure 3) uses a lightweight centralized message broker (e.g., Kafka). The lightweight message broker found in this topology does not perform any orchestration, transformation, or complex routing; rather, it is just a lightweight transport to access remote service components. The single point of failure and architectural bottleneck issues usually associated with a centralized broker are addressed through broker clustering.
Figure 4 - Centralized Messaging Topology
Broker clustering refers to the ability of the message broker to scale horizontally and proportionally with the demands of the connected applications and services, ensuring the reliability of the messages rerouted through the broker. If needed, the message brokers can be distributed across multiple nodes to continue to provide services despite outages of one or more nodes and be able to scale in and out automatically as the data volume scales down and up.
If broker clustering is utilized, however, messages will not be guaranteed to be delivered in the same order as they arrived. In that case another caching service or data store will be responsible for re-ordering the messages based on a sequence key.
Apache Kafka is the messaging framework that is incorporated in the JPO ODE implementation.
Figure 4 below highlights the concepts used in the Kafka implementation. Kafka has three key capabilities:
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Publish/Subscribe: It lets you publish and subscribe to streams of records. In this respect it is similar to a message queue or enterprise messaging system.
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Persistent and Reliable: It lets you store streams of records in a fault-tolerant way.
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Stream Processing: It lets you process streams of records as they occur.
Figure 5 - Kafka Concepts
In order to connect to Kafka, there are 4 core API's that systems can use to communicate with the broker.
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The Producer API allows an application to publish a stream of records to one or more Kafka topics.
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The Consumer API allows an application to subscribe to one or more topics and receive a stream of records. Multiple applications can subscribe to a single topic and process messages in parallel via Kafka's consumer group handling.
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The Streams API allows an application to act as a stream processor, consuming an input stream from one or more topics and producing an output stream to one or more output topics, effectively transforming the input streams to output streams.
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The Connector API allows building and running reusable producers or consumers that connect Kafka topics to existing applications or data systems. For example, a connector to a relational database might capture every change to a table.
The ODE utilizes these Kafka concepts and the framework has been designed as depicted in Figure 6.
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The input services represent the publisher into the system.
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The BSM decoder service consumes an encoded topic and published a decoded topic.
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Applications such as the TM Application and gateways consume the outputted decoded messages.
Figure 6 - Kafka Publish/Subscribe Model
Figure 6 below represents the micro-services topology envisioned for JPO ODE. It highlights the granularity of ODE micro-services and identifies the major architectural components with which these services interact.
Figure 7 - JPO ODE Micro-services Topology
Docker is utilized as the primary deployment mechanism to compartmentalize each of the designed micro-services into separate containers. Docker is used to package all components in a composite of containers each running a distinct service. The ODE application runs in one container, its submodules run in separate containers and other major frameworks such as Kafka run in their own separate containers.
| Term | Description |
|---|---|
| API | Application Program Interface |
| ASN.1 | Abstract Syntax Notation One (ASN.1) is a standard and notation that describes rules and structures for representing, encoding, transmitting, and decoding data in telecommunications and computer networking |
| JPO | Joint Program Office |
| Kafka | Apache Kafka is publish-subscribe messaging rethought as a distributed commit log. |
| SCP | Secure Copy |
| US DOT | Unites States Department of Transportation |
| WebSocket | WebSocket is designed to be implemented in web browsers and web servers, but it can be used by any client or server application. The WebSocket Protocol is an independent TCP-based protocol. Its only relationship to HTTP is that its handshake is interpreted by HTTP servers as an Upgrade request. |