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Project Aurras - MVP - Phase 1

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Project Overview 📄

Overview

Aurras is a middleware that acts as an event processor and a low code workflow orchestration platform. Aurras is being pitched as a next-generation system for enabling decentralized push notification. This middleware solution listens to events from blockchain applications and propagates them to a registered pool of MQTT brokers. The broader architecture consist of parachain from which the middleware listens for the events.

Aurras as a part of Substrate / Kusama / Polkadot / Web 3 Ecosystem

  • A core part of Decentralized Push Notification Architecture
  • Can be used for business-critical and production-grade IoT devices to listen to events from blockchain
  • Can be used with microservices across industry verticals
  • Can be expanded to get integrated with data analytics, knowledge visualization, and machine and deep learning (ML/DL) algorithms

Project Details

Architecture

Technologies

  1. Apache OpenWhisk is an open source, distributed Serverless platform that executes functions (fx) in response to events at any scale. OpenWhisk manages the infrastructure, servers and scaling using Docker containers. The OpenWhisk platform supports a programming model in which developers write functional logic (called Actions), in any supported programming language, that can be dynamically scheduled and run in response to associated events (via Triggers) from external sources (Feeds) or from HTTP requests
  2. Apache Kafka is an open-source distributed event streaming platform. Kafka works well as a replacement for a more traditional message broker. Message brokers are used for a variety of reasons (to decouple processing from data producers, to buffer unprocessed messages, etc.). In comparison to most messaging systems, Kafka has better throughput, built-in partitioning, replication, and fault-tolerance which makes it a good solution for large scale message processing applications.
  3. Docker
  4. Substrate
  5. Rust
  6. Go

Components

  1. Event Sources (Substrate Event Feed)
    This is the source for events from the blockchain and the events will be sent to the OpenWhisk system. The MVP event source will be implemented using polkadot-js/api. For the actual implementation, we will be using a custom version of the substrate archive. Substrate archive will be indexing the blocks and an event actor will be decoding the events and data from the block and posting them to the OpenWhisk system. The indexer will use a NoSQL database to store the last indexed block and metadata. Using the substrate indexer as our event source will help us in solving many challenges ranging from fault tolerance to scalability to uniquely identifying multiple events with the same payload within a single block. The indexer will be connected to a substrate-based chain and RocksDB where the chain data is stored. When a new block is produced by the chain, the indexer collects the raw blocks from the RocksDB and sends it to the event actor. The event actor processes the block. The block data is decoded using the parity scale codec and mapped with the data types provided to the runtime. Once the event is decoded, the event actor posts a trigger to the event trigger manager.

  2. Event Trigger Manager
    Event trigger manager is composed of multiple actions including using a database to store trigger URLs and their respective auths, and Kafka provider, consumer and producer. Once the event manager receives an event from the event feed, this data is produced to a topic. The feed action in the trigger manager lets the user hook into the system. That is, once an event is indexed to a particular topic, it can invoke a particular action. While creating the workflow, users can choose the event trigger as feed and provide necessary parameters from which chain it should be listening to and from which block it should start listening. Under the hood, a feed action is invoked with create lifecycle, which accepts the mandatory parameters the lifecycle, auth, trigger name, and other optional parameters of the event source. The feed action invokes the related actions of creating the entry in the database, adding to the Kafka consumer group, etc. The next component in the event trigger manager is a persistent connection to Kafka where it is used to produce and consume the stream of data. Once data is received in Kafka, the event trigger manager invokes the action to check the consumer groups for that particular topic and if found any, the trigger for the users under that particular group is invoked, which in turn invokes the workflow action.

  3. Workflow Composer
    Workflow composer consists of an async Rust library to compose multiple triggers, deployment configuration generator, and whisk deployment tool. For creating the workflow, the only input is the configuration file which is a YAML file. The workflow composition is laid out in the YAML which in turn takes care of the deployment and composing the triggers. Once a workflow is deployed to a namespace it creates a specific topic unique workflow id in Kafka. Workflow configuration comprises the input URL of workflow tasks, primarily GitHub repo, the sequence of processing tasks, and argument structure. Arguments must match the task input parameters.

  4. Web API Gateway and Backend Service This is the end user facing component to utilize the workflow. This component comprises of a backend application which is responsible for user registration, selecting the workflow, managing / creating workflow using friendly APIs, providing input parameters. API gateway / Machine gateway where the external world can connect to the Aurras system.

Ecosystem Fit

We have identified many great teams had to build their own implementation of event listeners/monitoring tools. We intend to make this aspect easier for the community to avoid most of the boilerplate so that they can just focus on workflow actions/tasks which are related to their project.

Similar projects

What makes us different is

As a part of Web 3 Community and Ecosystem

  • First class support to connect blockchains
  • Deployment and definition of workflows will be done through easy to write YAML configuration.
  • Workflow tasks can be written in multiple languages - NodeJS, Go, Java, Scala, PHP, Python, Ruby, Ballerina, .NET and Rust (We prefer and Recommend WASM compatible Rust)
  • First class support to MQTT Endpoints.

Team 👥

Team members

  • Dr. C. Pethuru Raj Ph.D - Principal Advisor, Chief Architect and Vice-President, Site Reliability Engineering (SRE) Division - Reliance Jio Platforms Ltd.
  • Muhammed Irfan K - Team Lead, Fullstack Developer, CTO & Co-Founder - HugoByte AI Labs
  • Hanumantappa Budihal - Solutions Architect, DevOps - HugoByte AI Labs

We will be hiring one Rust Developer

Contact

  • Registered Address:
    HugoByte AI Labs Private limited
    1st & 2nd Floor, IBIS Hotel, Hosur Road, Bommanahalli, Bengaluru
    Bangalore, Karnataka, India, 560068
  • Registered Legal Entity:
    HugoByte AI Labs Private limited
    CIN: U72900KA2020PTC140106

Team's experience

  • Dr. C. Pethuru Raj Ph.D
    Dr. C. Pethuru Raj is currently working as Chief Architect and Vice president in the Site Reliability Engineering (SRE) Center of Excellence, Reliance Jio Platforms Ltd. (JPL) Bangalore. Previously worked as a cloud infrastructure architect in the IBM Global Cloud Center Of Excellence (CoE), IBM India Bangalore for four years. Before that, He had a long stint as TOGAF-certified enterprise architecture (EA) consultant in Wipro Consulting Services (WCS) Division, Also worked as a lead architect in the corporate research (CR) division of Robert Bosch Bangalore. In total, He has gained more than 17 years of IT industry experience and 8 years of research experience. Completed the CSIR-sponsored Ph.D. degree at Anna University, Chennai, and continued with the UGC-sponsored postdoctoral research in the Department of Computer Science and Automation, Indian Institute of Science, Bangalore. Thereafter, He was granted a couple of international research fellowships (JSPS and JST) to work as a research scientist for 3.5 years in two leading Japanese universities. Published more than 30 research papers in peer-reviewed journals such as IEEE, ACM, Springer-Verlag, Inderscience, etc. Having authored 25 books thus far that focus on some of the emerging technologies such as IoT, Cognitive Analytics, Blockchain, Digital Twin, Containerization, Data Science, Microservices Architecture, fog/edge computing, etc. Also have contributed more than 30 book chapters thus far for various technology books edited by highly acclaimed and accomplished professors and professionals. He is currently advising the team at HugoByte AI Labs on impactful technology innovations.

  • Muhammed Irfan K
    Muhammed Irfan K is a strategic thinker and implementer of innovative ideas with an emphasis on educating and bringing back privacy to the people. Having Co-Founded HugoByte AI Labs and Serving as CTO. He envisions a world where AI is open, distributed, and democratized by building a decentralized infrastructure and policy framework to regulate it. He has extensive experience in designing and building Highly Scalable Enterprise Architecture, Leading Team, Full-Stack Development, Microservices, Machine Learning.

  • Hanumantappa Budihal
    Hanumantappa Budihal has 4 years of experience as an Application Developer and Azure DevOps Engineer. His technical skills include .NET Core Framework, Azure DevOps, SQL Developer, ASP .NET MVC, REST Web API, WPF, Microsoft Azure, Cognitive Services, Algorithms Design for a complex business requirement, and Watson Discovery, with good knowledge of Application Architecture and Design pattern implementation and data analysis skills. He is a hard worker moreover eager to learn new skills. He has also worked on cognitive application development using various IBM Bluemix services such as conversation, Natural language understanding, Watson discovery, and developed few chatbots using Api.ai and Microsoft bot framework. He is currently working as a Solutions Architect in HugoByte AI Labs.

Team Code Repos

Source codes will reside in

Repos for further reference

Team Profiles

Development Roadmap 🔩

Overview

  • Description Development of Project Aurras - MVP - Phase 1
  • Total Estimated Duration: 210 Person Days
  • Full-time equivalent (FTE): Milestone 1 - 1.5; Milestone 2 and 3 - 3
  • Total Costs: 1.16 BTC

Milestone 1 — Event Source - Substrate Event Feed

  • Estimated Duration: 30 Working Days
  • FTE: 1.5
  • Costs: 0.25 BTC
NumberDeliverableSpecification
0a.LicenseApache 2.0
0b.DocumentationDocumentation includes Inline Code Documentation, Configuration Documentation, Event Post Action Deployment guide, Docker and Docker compose setup documentation, Openwhisk Setup Documentation, Readme file
0c.Testing GuideThe code will have unit-test coverage (min. 50%) to ensure functionality and robustness. In the guide we will describe how to run these tests
1a.Substrate Event Feed: ConfigurationReading Configuration based on Environment, Override Configuration if Environment Variables provided, Configrations for Chain endpoint Sections and Methods from extrinsic to Exclude, types, Openwhisk Endpoint, Openwhisk Auth Key, Trigger Endpoint, Kafka Topic and Brokers
1b.Substrate Event Feed: Chain ModuleConnects to the chain, Add custom type to chain intialization if provided, Subscribes to system.events
1c.Substrate Event Feed: Event ModuleFilters Events based on excludes provided, Post Events to trigger Endpoint
1d.Substrate Event Feed: Docker ImageDockerfile for Substrate Event Feed Package
1e.Substrate Event Feed: Docker Compose ConfigurationAdd Substrate Event Feed Service in Docker Compose Configuration
1f.Substrate Event Feed: Helm Chart ConfigurationHelm Chart Configuration for Substrate Event Feed for Kubernetes
2.Event Manager: Event Post ActionMinimal JS Implementation of Event POST Action with Payload as Kafka Topic, Broker and Event Data such as section, method and Event Payload

Milestone 2 — Event Manager - Part 1

  • Estimated Duration: 35 Working Days
  • FTE: 3
  • Costs: 0.58 BTC
NumberDeliverableSpecification
0a.LicenseApache 2.0
0b.DocumentationDocumentation includes Inline Code Documentation, Configuration Documentation, Kafka and Zookeeper Deployment guide, wskdeploy guide, Readme file
0c.Testing GuideThe code will have unit-test coverage (min. 50%) to ensure functionality and robustness. In the guide we will describe how to run these tests
1a.Kafka Provider: ForkFork Existing openwhisk-package-kafka
1b.Kafka Provider: Helm Chart ConfigurationHelm Chart Configuration for Kafka Provider for Kubernetes
2.Database ServiceImplement DB Service, DB Integration, Connect DB provided through configuration variables, Dockerfile for Containerization, Docker Compose Configuration
3a.Event Manager: Event Source RegistrationIntegrate with DB service - CouchDB, Register Source with Chain Name, Chain Specification, Create Unique topic for provided Section-Method, Return created topics - Section-Method Map
3b.Event Manager: Kafka provider feed actionIntegrate with DB service - CouchDB, Add CREATE, READ, UPDATE, DELETE lifecycle methods for trigger, Validate parameters (Section-Method, broker, isJSONData, isBinaryValue, isBinaryKey), Get unique topic from DB using Section-Method param,Record topic and related trigger to DB on CREATE lifecycle
3c.Event Manager: Deployment ConfigDeployment Config for wskdeploy tool
3d.Event Manager: Intermediate ContainerDockerfile for Containerization, Container with wsk cli and wskdeploy util, Script to add Openwhisk auth key, Deploy Openwhisk Actions and Create Triggers and Rules
3e.Event Manager: Docker Compose ConfigurationDocker Compose Configuration for Event Manager: Intermediate Container
3f.Event Manager: Helm Chart ConfigurationHelm Chart Configuration for Event Manager: Intermediate Container for Kubernetes

Milestone 3 — Event Manager - Part 2

  • Estimated Duration: 20 Days
  • FTE: 3
  • Costs: 0.33 BTC
NumberDeliverableSpecification
0a.LicenseApache 2.0
0b.DocumentationDocumentation includes Inline Code Documentation, Configuration Documentation, Readme file
0c.Testing GuideThe code will have unit-test coverage (min. 50%) to ensure functionality and robustness. In the guide we will describe how to run these tests
0d.ArticleWe will write a Medium article that explains the work done as part of the grant.
1a.Event Manager: Kafka Produce ActionValidate for parameters, Connect to provided brokers, Produce data to provided topic, Add deployment config to wskdeploy config
1b.Event Manager: Kafka provider feed actionIntegrate with DB service - CouchDB, Delete trigger from DB on DELETE lifecycle, Read trigger from DB on READ lifecycle, Update trigger from DB on UPDATE lifecycle
2a.Substrate Event Feed: ConfigurationCouchDB Config, Available Sections and Methods of the chain to create unique topics
2b.Substrate Event Feed: Event Source RegistrationIntegrate with DB service - CouchDB, Save Unique ID in DB for topic provided through Event Manager: Event Source Registration

Future Plans

We have planned immediate grant as second phase of our development (Draft with Complete deliverables can be found here https://github.com/MuhammedIrfan/Open-Grants-Program/blob/master/applications/project_aurras_mvp.md)

Immediate Plans in the timeline Includes

  • Develop full fledge system
  • Implement Custom Version of Substrate Archive
  • Add more operators
  • Add Analytics and Monitoring
  • MQTT Endpoints
  • Decentralized push notification

Our Roadmap includes

  • Decentralized Pub/Sub Network to replace Apache Kafka in Aurras
  • WASI Runtime for Openwhisk

Additional Information ➕

Any additional information that you think is relevant to this application that hasn't already been included.

Possible additional information to include:

  • What work has been done so far?
    This is our first grant as a part of bringing innovation to the web3 Ecosystem.
  • Are there are any teams who have already contributed (financially) to the project?
    No
  • Have you applied for other grants so far?
    No