What if your autonomous network could talk back to you? Telling you about when it’s going to fail, detect faults, reroute traffic to prevent outages. Would it provide insights right from its instantiation till its decommissioning? In fact, some of these capabilities are available right now, and the reason is the advent of big data analytics, machine learning, and eventually full-blown artificial intelligence.
Autonomous Systems technologies are playing an increasingly important role in our interconnected and digitalized world. The effect of autonomous systems will have consequences for many industry sectors as well as our daily lives. Enterprises are converging on intelligent machines for performing repetitive and time-consuming tasks and will become one of the biggest challenges for all as well as an opportunity for increased efficiency and cost reduction of immense proportions.
What’s pushing autonomous networks towards reality?
Artificial Intelligence is at the core of autonomous network systems. It will infuse them in different degrees – the more autonomous they are and the more challenging the environment they will be working in, the more AI will be needed.
The application of AI in Autonomous Systems will generally supersede the perception of the classic applications of AI. This signals a shift towards the embedding of AI and the making of a mature technology.
- The cloud is the binding factor that’s driving all this. Cloud is as simple as compute and storage resources interconnected by high-speed networks that host software-based applications that manipulate and present data to end-users.
- Cloud offers increased processing power that a single datacenter cannot provide to reach incredible performance to process the continuous stream of big data and is key reason why ubiquitous AI-centric applications are much closer to becoming a reality today.
Cloud offers unlimited storage of data spread across multiple processing platforms, which can be within the same data center or across many physically separated data centers. This allows for storing and manipulating previously inaccessible large amount of data gathered from the same networks interconnecting data centers, for powerful analytics to offer new insights that will lead to improved overall decision-making.
Embedded sensors are the foundation of AI that give insight into the network. They generate the raw data that’s fed into machine-learning algorithms to enable better-informed decisions and subsequent actions, either manual (humans) or autonomous (machines). With raw sensor data, the underlying network must be instrumented for network AI.
- Open APIs allow for standards-based access into instrumented networks where the data generated by the embedded sensors can be easily extracted and manipulated. The more data extracted from the network, better decision can be made.
Closing the network loop
The same highly instrumented networks that generates massive amount of big data to and from data centers can be used as input for the machine learning algorithms running on applications in one or more data centers via open APIs. This will allow networks to become increasingly self-aware, smarter, and more autonomous than they are today.
This closed loop where autonomous network that enable AI in the first place, will listen to itself via embedded sensors, transport raw data over itself to data centers, have it analyzed offline, and then use the outcome of big data analytics to make informed, autonomous decisions.
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