AIML & AUTONOMOUS NETWORKS

INTRODUCTION

We live in a world where data is key. And communication of said data is extremely important for the world to function. Data communication is required for communication between people, running operation systems. It could even be said that data communication is the backbone of this information-driven world.

Networks come into play when we start talking about data communication. Networks have become an integral part of digital enterprises today. Networks are nothing but a collection of devices capable of communicating that are connected to each other using links. The devices are called nodes and the medium connecting them is called links.

In this blog, we would like to take a look at autonomous networks. The word autonomous is derived from the word autonomy, which means free of the need to be controlled.

We could draw conclusions from that definition of autonomous networks: Autonomous networks are networks that run under minimal or no human control.

Our dynamical resource provisioning control plane facilitates network- and server-aware control to adapt and orchestrate the autonomous allocation of network and server resources in a synergistic manner. We show that our network architecture supports background and dynamic connections for different levels of QoS to meet the various bandwidth and latency requirements of incoming network connections. It also provides load-sensitive server management through VMmigrations, which operates alongside its network bandwidth provisioning capabilities. We built a testbed with emulated datacenters to demonstrate the functionalities offered by our control plane, and demonstrated its feasibility.

NETWORK CHALLENGES

Digital enterprises today have incorporated various tools into their day to day operations such as Iot, data streams, user experience platforms, connected devices and security firewalls. Though this being the case for most of today’s digital enterprises, Networks still face the issue of managing their complicated components. Though the pandemic has forced many operations to move to a remote work platform showing a rapid and unprecedented load on networking operations. This is making it harder and harder for human operators to keep up with as the load keeps on increasing. This could in the potential future lead to a bottleneck in the progression of technology and operations from around the world.

AI Enables Networks

Integrating Artificial intelligence and machine learning to implement autonomous networks ensures rapid scalability and increased agility into existing software-defined methods. A hardware dedicated approach helps make sure that the network architecture is free from manual intervention in its maintenance, configuration and monitoring. It can also help take in large quantities of data from various sources. These sources could include IOT devices

Autonomous networks can further drive machine reasoning to solve critical cases such as network provisioning and optimization, complex event processing, outage prevention, fault management, path selection. This dramatically reduces the cost that operations bear to solve these problems. Also, it saves operations a lot of time spent on error correction, the management of overloaded network Routes. This might not seem as much but it matters a lot to companies pushing aggressive growth.

Pandemic has resulted in a drastic growth in the number of users for networks, the enterprises running said networks need agility to further scale as they have to manage network capacity and resources. This helps enterprises manage, predict and maintain their networks in a much more efficient way.

Autonomous networks can station a machine reasoning approach affirmed on business intent as it relates to network behaviour with the help machine learning-based design. This basically means that enterprise networks can be a prominent part in aligning network behaviour based on business parameters that are unequivocally worked upon by executive policies. As shown in the image above, a high-level strategic intent is made by simultaneously using network policy frameworks, orchestration, and analytics in an entirely closed automation loop. In the beginning, the business or strategic intent is expressed as network goals. For this, policy frameworks, orchestration and analytics work together.

The business intent is charted to node-level requirements and further divided into sub-tasks, which are later addressed by closed-loop automation. A singular closed-loop automation cycle is enough to address small tasks. As for the larger tasks, the cycle will consist of a number of smaller iterations and learning cycles.

Autonomous networks that are designed with complex event processing capabilities can also be used to detect abnormal operating conditions based on the predefined business intent. If there is any sort of suspicious behaviour, it can monitor and change the user experience respectively. Such multi-dimensional checking allows the network to protect the enterprise and the customer and find the start of the issue for long-term problem-solving.

Similar intent-based networking can quickly conduct several business-centric activities such as rapid application testing, assurance troubleshooting, generating actionable remedial insights, and so on.

Intelligent networks can tackle the outreach and analytical power of machine learning for becoming self-servicing and self-healing. The machine reasoning part of autonomous networks is the main leap forward as it allows IT, leaders, to provide IT services in real-time across enterprises with greater agility and responsiveness. In such an implementation, enterprise systems gain the ability to be constantly improving, self-correcting, and a minimal burden on the human workforce. With artificial intelligence and machine learning-powered applications, autonomous networks can also radically transform core areas of network management like data center virtualization, provision computing resources, and storage management to create a truly autonomous enterprise of the future.

CONCLUSION

Network automation shows a lot of promise especially in lowering the cost of operations, this has created a lot of buzz among the telecommunication community. This is because autonomous networks can autonomously configure, remove, add, deploy, and retire services to use for physical and virtual devices. The end of the current digital era is nearsighted and the question now is where the future of networks is headed. This will decide how the networks of the future will be designed, operated, maintained and managed. The landscape for telecommunications and networks is quite dynamic and ever-changing. Autonomous systems could play a role in helping organizations cope with this ever-changing landscape with the agility and speed that today’s. For many operators, the end stages of digital transformation are now in sight and the question turns to future networks which are rapidly and radically changing the way networks are designed, operated, and managed. For many operators, the end stages of digital transformation are now in sight and the question turns to future networks which are rapidly and radically changing the way networks are designed, operated, and managed. world demands. Autonomous networks must also provide advanced capabilities and improved performance.

With the combined efforts of:

Siddhesh Borse

Saachi Shrikhande

Soham Sattegiri

Aniruddha Shrikhande