Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. 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. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

/pdf/a-comprehensive-survey-on-machine-learning-for-networking-3t5h3kbpe8.pdf

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Since its formal introduction to IP networks in 1993 as a viable complementary approach for congestion control, there has been a steady stream of research output with respect to Active Queue Management (AQM). This survey attempts to travel the trajectory of AQM research from 1993 with the first algorithm, Random Early Detection (RED), to current work in 2011. In this survey we discuss the general attributes of AQM schemes, and the design approaches taken such as heuristic, control-theoretic and deterministic optimization. Of interest is the role of AQM in QoS provisioning particularly in the DiffServ context, as well as the role of AQM in the wireless domain. For each section, example algorithms from the research literature are presented.

Active Queue Management: A Survey

This paper provides a new deterministic Q-learning with a presumed knowledge about the distance from the current state to both the next state and the goal. This knowledge is efficiently used to update the entries in the Q-table once only by utilizing four derived properties of the Q-learning, instead of repeatedly updating them like the classical Q-learning. Naturally, the proposed algorithm has an insignificantly small time complexity in comparison to its classical counterpart. Furthermore, the proposed algorithm stores the Q-value for the best possible action at a state and thus saves significant storage. Experiments undertaken on simulated maze and real platforms confirm that the Q-table obtained by the proposed Q-learning when used for the path-planning application of mobile robots outperforms both the classical and the extended Q-learning with respect to three metrics: traversal time, number of states traversed, and 90° turns required. The reduction in 90° turnings minimizes the energy consumption and thus has importance in the robotics literature.

A Deterministic Improved Q-Learning for Path Planning of a Mobile Robot

pdf/when-machine-learning-meets-congestion-control-a-survey-and-3rwp94n76c.pdf

When machine learning meets congestion control: A survey and comparison

The evaluation and benchmarking processes of active queue management (AQM) methods are complicated and challenging. Several evaluation criteria/metrics must be considered before an AQM method can y...

Multi-Criteria Evaluation and Benchmarking for Active Queue Management Methods: Open Issues, Challenges and Recommended Pathway Solutions

Although RED has been widely used with TCP, however it has several known drawbacks [1]. The BLUE algorithm that benefits from a different structure has tried to compensate some of them in a successful way [2]. A quick review on active queue management algorithms from the very beginning indicates that most of them tried to improve classic algorithms. Some of them use network traffic history to achieve more flexibility and prediction ability while others use algorithms such as fuzzy logic to address scalability problem and high input load. Our proposed approach benefits from both: Using fuzzy logic to deal with high input load and embedding expert knowledge into the algorithm while optimizing router decisions with reinforcement learning fed by network traffic history. We call this approach "DEEP BLUE" as is consist of an improved version of BLUE algorithm. Derived from BLUE, our algorithm uses packet drop rate and link idle events to manage congestion. Our experiments using OPNET simulator shows that this scheme works faster and more efficient than original BLUE.

/pdf/deep-blue-a-fuzzy-q-learning-enhanced-active-queue-4eoknfrapq.pdf

Deep Blue: A Fuzzy Q-Learning Enhanced Active Queue Management Scheme

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Deep Blue: A Fuzzy Q-Learning Enhanced Active Queue Management Scheme