Artificial Intelligence (AI) driven Infrastructure

With developments in Cloud Computing and Software Defined Infrastructure, IT infrastructure has become on-demand, abstract and flexible. But still infrastructure is not intelligent. It is dependent on human intervention to understand the relationship between different IT components, understand the data trends and act accordingly. AI can change this and lead to error-free, self driven infrastructure.

Role of cloud computing in Infrastructure

Thanks to cloud computing, we are living in a world where the challenges of on-demand infrastructure is a past memory. Cloud computing has transformed every area of Enterprise IT, but the impact has been the most significant in the area of infrastructure. This has been possible because of cloud with its huge infrastructure capacity and automation has broken the ceiling of most quality benchmarks such as scalability, availability, elasticity, and performance.

Software Defined Data-Center

Software Defined Data-Center (SDDC) is leading to better infrastructure flexibility by pooling, aggregating and delivering the infrastructure capabilities as a software code. A SDI (Software Defined Infrastructure) abstracts the software from the hardware layer. Thus, the intelligence is moved from hardware components to a software stack. Based on software and a high degree of automation, a SDI is designed to build and control an infrastructure mostly without human interaction.

Recently I have worked on building a cloud-based platform, where all the possible infrastructure management (provision, scaling, user management etc.) is abstracted into a handful of API calls. These APIs can be consumed from the software itself or modern UI channels such as facebook/ slack chat or voice-based Alexa. Under the hood lies the complexities of multiple cloud environments, integration, services, security, monitoring, self-healing environments etc.

Thus a SDI-based infrastructure works independently from a hardware environment. However, a SDI is everything but intelligent! It is based on developer-defined rules (based on user’s previous experience) into hard-coded commands for taking certain actions automatically.

AI Defined Infrastructure (AIDI)

Enterprise AI applications are expected to grow at a compound annual growth rate (CAGR) of 64.3% in the 10 years (from $358 million in 2016 to $31.2 billion by 2025). Thus it is highly essential for IT and business to take early strides in this direction.

AI is defined in many ways. But in the current context, the intelligence of AI means a system which can self-learn the relationship between different IT components, understand the data pattern and the context, and act accordingly. The learning of its own environment makes a difference between the SDI and AIDI.

AI systems are dependent on large structured/ unstructured data which becomes a challenge in most of the AI based implementation. One huge advantage that infrastructure has is the availability of a lot of data in terms of user requests/ complaints, system logs etc.

Use cases of AI defined Infrastructure

Without any human intervention, AIDI can manage following tasks,

Planning:

1. Analyze the demand trends and predict the infrastructure requirements and plan accordingly.
2. Match the requirements with the available infrastructure.

Build:

1. Deploy the necessary resources as per workload requirements.
2. De-allocating the resources when they are not needed anymore.
3. Configure the infrastructure components.

Run & Maintain:

1. Analyze the data pattern which indicates the behavior of the system to make a model of the quality parameters w.r.t. system behavior. There is no need to define the rules, set threshold, as AI uses training to build their own model.
2. Use the model to achieve the most optimum quality parameters such as availability, scalability, storage.
3. Know about anomalies by knowing how the system behavior doesn’t look like. Identify anomalies such as intrusion detection, fraud/ fault points and save the infrastructure from abuse or going down.
4. In situations like cyber attack, where the gap between identification and mitigation is very important, AI can detect the threat and act immediately. We are already using a similar system, which checks your mail and detects if it contains anything suspicious and stops it from harming your system.
5. React or proactively act based on the single/group of infrastructure components. Autonomously take action to enable error-free infrastructure.
6. Reduce the cost of IT infrastructure by using the most optimal components. (e.g. instead of using 3 small server instances, choose to operate with 1 medium instance).

Improving the AI system

Though designing AI system based on past system data can be helpful, but additional improvement can be brought by,

1. Consume the knowledge from the experts, best practices to become more intelligent.
2. Learn by connecting with other AI systems. New working models can be easily built by techniques such as transfer learning from existing matured AI system (either from the same or different domain).
3. Never stop learning from new knowledge pool.
4. Optimize the knowledge as per business priorities.

Other ways AI is interacting with Infrastructure

Apart from being the savior for infrastructure management, there are some interesting ways in which AI is impacting the infrastructure.

1. Increased demand for infrastructure. AI requires huge, parallel computing power (such as GPUs) and large storage capacity.
2. The SDLC for Machine Learning is different from traditional application development. With machine learning, the development may happen on local setup, but training is recommended on cloud based GPUs. There is a lot of switching required between these environments. A good DevOps is essential considering this.
3. There are some black hats as well who will use AI into their job of threatening the IT. There will be an increase in disruptions and loss because of AI-based cyberattacks.
4. Infrastructure helpdesk support is currently manned by various levels of IT support executives. AI can take over the role of supporting their users 24 x 7 x 365 with a smart assistant system. Continuous improvements in Natural Language Processing and Sentiment Analysis is promising such systems.
5. Reduce data-center electricity usage significantly by using AI systems. Google has observed reduction by 40% of such cost by using AI.
6. Use robotics guided by AI to handle operations which needs hand-and-feet support.

Is it Easy to implement AI based Infrastructure?

If AI can learn itself without needing to code, it must be very easy to develop such AI systems, right? Wrong! Though AI doesn’t need much of programming for building the model, data scientists need to choose machine learning algorithm or design a deep neural network. Also, a lot of considerations needs to be given in choosing quality data, algorithms and hyperparameters that define the design of neural networks.

Unlike rule-based traditional systems, AI systems can be error-prone as they have learned from the past data and the new data may be completely defying their model logic. A good domain knowledge is highly essential for improving the accuracy of the AI-based systems.

AI defined Enterprise Architecture

In this post, we have discussed how AI defined infrastructure enables IT in changing the infrastructure behavior from a today’s semi-dynamic to a true real-time IT environment in an automated way.

This autonomous way of planning, building, running and maintaining the entire infrastructure let IT operations and developers deploy IT resources like a server, storage, network, databases and other ready services in the most efficient way — by using the knowledge of experts and implementing intelligence based on the past data. Though this is not limited to infrastructure alone but have the ability to transform the end-to-end Enterprise Architecture. We will discuss this in the future blog of AI defined Enterprise Architecture.

References:

AI-defined infrastructure: be prepared for a new generation of business models and applications

Introducing the AI-defined infrastructure