ASSIA’s Approach to Machine Learning/Artificial Intelligence

Part 1: A Primer


Arthur Samuel in 1959 defined machine learning as the “field of study that gives computers the ability to learn without being explicitly programmed”. Typically, machine learning and artificial intelligence (AI) are used interchangeably in the industry although AI is a superset concept that is not just limited to machine learning.  Only recently, the advances in cost, processing power and storage on devices and network equipment, have delivered the possibility of AI techniques previously only available for super computer clusters.

In a typical AI/machine learning system for network management, the following steps form the foundations of any prescriptions or predictions that get generated by it:

  • Start with observing a user or a system
  • Describe, categorize and make sense of the observation
  • Attempt to define the behavior of the user or system by establishing a set of baselines
  • Monitor and detect deviations from baselines of defined behaviors
  • Predict future behavior, prescribe trouble-shooting solutions, and self-optimize

Applying these AI techniques to better monitor, detect, troubleshoot and optimize Wi-Fi networks, is part of ASSIA’s core engineering capabilities. While the feasibility of real-world applications of AI techniques is there, the data scientist generalists approach falls short of delivering tangible value to service providers. This is where ASSIA brings real-world performance and value accepted by service providers to the field of AI driven network management.

Basic vocabulary for machine-learning/AI

Data is the input to the overall system and in the context of Wi-Fi management can be anything from physical layer performance metrics, device characteristics and environment, alerts end-user inputs, and Wi-Fi client performance indicators.

A feature is an individual data element relevant to the specific machine learning model and use case. It can be directly extracted from the data or derived after passing the raw data through a pre-processing algorithm. For example, Wi-Fi throughput between an access point and a client can be derived from traffic counters.

A model maps the input features to the desired output or label.  In Wi-Fi management, an output may be the best channel to pick in the user’s environment or the user’s perceived quality of experience score.

Labels augment individual unlabeled data with meaningful tags that are informative. Machine learning models can be applied to the data so that new unlabeled data can be presented to the model and a likely label can be guessed or predicted for that piece of unlabeled data.

Supervised machine learning is a “teaching” technique to develop a relationship between a known set of outputs (labels) and their inputs (features). Once the model is developed, it can be used to predict the output for a new set of inputs.

Training prepares a supervised machine learning model using features extracted from training data along with the appropriate labels. Training can be performed periodically with the update of training data or features.

Unsupervised machine learning is “self-learning,” and no prior training, or preparation is required before it’s deployed. The algorithm automatically discovers relevant structure and relationships among the inputs.

Baselining is a method to determine “expected” behavior for an entity like a user or a system. Once these baselines are established, the models look for deviations from the baselines.

How ASSIA makes machine learning/AI possible

ASSIA’s CloudCheck solution makes AI possible using its agent-cloud architecture. The agent resides on the home Wi-Fi router, collecting the data necessary for meaningful AI. The data is processed and stored in the cloud, allowing for powerful machine learning/AI applications.

For many years, three main issues hindered proper data collection necessary for meaningful cloud-based AI applications in last-mile and Wi-Fi network management:

  • Non-exhaustive platform coverage:There are a variety of CPEs with various chipsets in use in service providers’ networks. It is important to collect data from all types of nodes for network level machine learning. ASSIA solves this problem with its hardware-agnostic CloudCheck agent that provides similar data collection across more than 100 CPE models.
  • Insufficient sampling across time, and network nodes:Overnight data collection that is prevalent in the industry is insufficient for identifying problems in network nodes. Furthermore, it is gravely inadequate for machine learning applications. CloudCheck solves this problem by providing a much higher frequency of data collection. This data is stored and transported efficiently so as to minimize network load.
  • Data collection methods that don’t scale:While trying to collect more data, more frequently, in a more intelligent way, we can’t (yet) assume unlimited computational resources. CloudCheck uses a smart way of mixing event and time triggered data collections that takes into account the dynamic nature of the observed trouble patterns. This resolves the scalability problem of the growing data collection needs without missing critical information.

ASSIA has developed patented data collection techniques that enable application of AI-based algorithms in the real-world environment.

ASSIA’s CloudCheck solution makes use of this data in unsupervised ways to learn baseline behaviors along various dimensions. The learnt policies or thresholds are either fed back to the CloudCheck agent, so it can report deviations from the thresholds to trigger an optimization action, or the policies are used by server algorithms to generate diagnostic outputs. The diagnostic outputs are further used in a supervised learning model that assesses customer satisfaction.

In the next article we will explore specific applications of how ASSIA applies AI and machine learning in Wi-Fi Management.