In Information-Centric Networking (ICN), accurately predicting content popularity can improve the performance of caching. Therefore, based on Software Defined Network (SDN), this paper proposes DLCPP (Deep-Learning-based Content Popularity Prediction) to achieve the popularity prediction. DLCPP adopts the switch’s computing resources and links in the SDN to build a distributed and reconfigurable deep learning network. For DLCPP, we initially determine the metrics that can reflect changes in content popularity. Second, each network node collects the spatial-temporal joint distribution data of these metrics. Then, the data are used as input to Stacked Auto-Encoders (SAE) in DLCPP to extract the spatiotemporal features of popularity. Finally, we transform the popularity prediction into a multi-classification problem through discretizing the content popularity into multiple classifications. The Softmax classifier is used to achieve the content popularity prediction. Some challenges for DLCPP are also addressed, such as determining the structure of SAE, realizing the neuron function on an SDN switch, and deploying DLCPP on an OpenFlow-based SDN. At the same time, we propose a lightweight caching scheme that integrate cache placement and cache replacement—caching based on popularity prediction and cache capacity (CPC). Abundant experiments demonstrate good performance of DLCPP, and it achieves close to 2.1%~15% and 5.2%~40% accuracy improvements over neural networks (NN) and auto regressive (AR) respectively. Benefitting from DLCPP’s better prediction accuracy, CPC can yield a steady improvement of caching performance over other dominant cache management frameworks.

By replacing traditional hardware-based middleboxes with software-based Virtual Network Functions (VNFs) running on general-purpose servers, network function virtualization represents a promising technique to reduce the cost of service creation and increase the agility of network operations. Typically, Service Function Chains (SFCs) are adopted to orchestrate dynamical network services and facilitate management of network applications. Recently, SFC parallelism that implements parallel processing of VNFs has been investigated to further improve SFC service quality. However, the unreasonable service graph of parallel processing in existing parallelized SFCs (PSFCs) might cause excessive resource consumption; incoordination between PSFC deployment and scheduling also increases the queuing delay of VNFs and degrades PSFC performance. In this article, an adaptive parallel processing optimization mechanism (APPM) is proposed to self-adaptively adjust the service graph of PSFCs and intelligently solve the joint problem of PSFC deployment and scheduling. Specifically, APPM uses a parallelism optimization algorithm (POA) based on the bin packing problem with soft bin capacity to optimize the structure of the PSFC service graph. Afterward, APPM employs a joint optimization algorithm based on reinforcement learning (JORL) to jointly deploy and schedule the PSFCs optimized by POA via the online perception of environment status. Simulation results showed that POA reduces the SFC parallelism degree and resource consumption by about 35%; JORL lowers SFC delay by reducing the queuing delay and has better overall performance than the state of the art algorithms even with limited resources.

In datacenter networks, bandwidth-demanding elephant flows without deadline and delay-sensitive mice flows with strict deadline coexist. They compete with each other for limited network resources, and the effective scheduling of such mix-flows is extremely challenging. We propose a deep reinforcement learning with private link approach (DRL-PLink), which combines the software-defined network and deep reinforcement learning (DRL) to schedule mix-flows. DRL-PLink divides the link bandwidth and establishes some corresponding private-links for different types of flows to isolate them such that the competition among different types of flows can decrease accordingly. DRL is used to adaptively and intelligently allocate bandwidth resources for these private-links. Furthermore, to improve the scheduling policy, DRL-PLink introduces the novel clipped double Q-learning, exploration with noise, and prioritized experience replay technology for DDPG to address function approximation error, to induce lager and more randomness for exploration, as well as more effective and efficient experience replay in DRL respectively. The experiment results under actual datacenter network workloads (including Web search and data mining workload) indicate that DRL-PLink can effectively schedule mix-flows at a small system overhead. Compared with ECMP, pFabric, and Karuna, the average flow completion time of DRL-PLink decreased by 77.79%, 65.61%, and 23.34% respectively, when the deadline meet rate is increased by 16.27%, 0.02%, and 0.836% respectively. Additionally, DRL-PLink can also well achieve load balance between paths.

Recently, Programmable Data Planes (PDPs) have brought new opportunities to unprecedented innovations in network protocols and architectures. Among them, programmable data plane intelligence, including achieving networking for Machine Learning (ML) and ML for networking with PDP, has attracted great interest from researchers. Motivated by this research trend, this article presents a survey of recent advances in the use of PDP in terms of machine learning. We first introduce PDP intelligence and the benefits of assisting ML with PDP. Then, the difficulties of realizing it are shown, which are caused by PDP's limited capabilities. After that, we present recent advances to cope with the difficulties, where each work is analyzed from multiple perspectives. Finally, we show the opportunities and challenges for further research.

Monitoring the state of wind turbine blades in real-time using sensors is crucial for early fault diagnosis. Several studies have been conducted to predict the failure of wind turbine blades based on data measured by sensors. These methods rely on accuracy of the sensor-monitoring data; even minor abnormalities can lead to misjudgment of the blade condition and cause serious consequences in service. Nevertheless, self-diagnosing schemes for sensor faults are less researched. The data measured by all sensors on the same wind turbine blade constitutes a spatiotemporal joint distribution dataset, which forms a data correlation pattern. Therefore, this paper proposes a sensor fault self-diagnosing scheme that does not depend on any labeled fault data. First, a sensor data prediction model based on deep learning is built by mining the inherent relevance between sensors. Second, a sensor fault is detected when the residual between the measured sensor value and the predicted value exceeds the control limit. The experimental results for a real-world wind turbine blade show that the model has good prediction and fault diagnosis performance.

In software-defined data-center networks, Elephant flow/Mice flow/Coflow coexist and multiple resources (bandwidth, cache and computing) coexist. However, the conventional routing methods cannot overcome the large gap between different performance requirements of flow and efficient resource allocation. Therefore, this paper proposes DRL-R (Deep Reinforcement Learning-based Routing) to bridge this gap. First, we recombine multiple resources (node’s cache, link’s bandwidth) by quantifying the contribution score of them reducing the delay. This actually converts the performance requirements of flow into resource requirements of this flow, hence, the routing problem can be converted into a job-scheduling problem in resource management. Second, a DRL agent deployed on an SDN controller continually interacts with the network for adaptively performing reasonable routing according to the network state, and optimally allocating network resources for traffic. We employ Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) to build the DRL-R. Finally, we demonstrate the effectiveness of DRL-R through extensive simulations. Benefitted from continually learning with a global view, DRL-R can improve throughput highest up to 40% and flow completion time highest up to 47% over OSPF. DRL-R can improve the load balance of link highest up to 18.8% over OSPF. Additionally, DDPG has better performance than DQN.