Network faults lead to financial liability and defamation in reputation of network providers. Despite these advances, network operations and management still remains cumbersome, and network faults are prevalent primarily due to human error. It is important to note that a trained ML model can be deployed for inference on less capable devices e.g.
For instance, Cloud Computing offers seemingly infinite compute and storage resources, while Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) provide accelerated training and inference for voluminous data. Recent advances in computing offer storage and processing capabilities required for training and testing ML models for the voluminous data. This encourages the application of ML that not only identifies hidden and unexpected patterns, but can also be applied to learn and understand the processes that generate the data. Undoubtedly, there is a colossal amount of data in todays’ networks, which is bound to grow further with emerging networks, such as the Internet of Things (IoT) and its billions of connected devices. Most importantly, the success of ML techniques relies heavily on data. Evidently, as we look forward to automating more aspects of our lives, ranging from home automation to autonomous vehicles, ML techniques will become an increasingly important facet in various systems that aid in decision making, analysis, and automation.Īpart from the advances in ML techniques, various other factors contribute to its revival. For example, search engines extensively use ML for non-trivial tasks, such as query suggestions, spell correction, web indexing and page ranking.
Ordinarily, we often use technological tools that are founded upon ML. For instance, ML in health care has greatly improved the areas of medical imaging and computer-aided diagnosis. Recent advances in ML have made these techniques flexible and resilient in their applicability to various real-world scenarios, ranging from extraordinary to mundane. Early ML techniques were rigid and incapable of tolerating any variations from the training data. Recently, ML is enjoying renewed interest. This instigates a shift in the traditional programming paradigm, where programs are written to automate tasks. The patterns learnt are used to analyze unknown data, such that it can be grouped together or mapped to the known groups. In essence, the goal of ML is to identify and exploit hidden patterns in “training” data. It goes beyond simply learning or extracting knowledge, to utilizing and improving knowledge over time and with experience. Machine learning (ML) enables a system to scrutinize data and deduce knowledge. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. There are various surveys on ML for specific areas in networking or for specific network technologies. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains.
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