Content Popularity Prediction and Caching for ICN: A Deep Learning Approach With SDN

Liu, Waixi; Zhang, Jie; Liang, Zhongwei; Peng, Lingxi; Cai, Jun

doi:10.1109/access.2017.2781716

Cited by 113 publications

(65 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the WSNs network, the SDN controller collects the WSN network node to input the user request data to the SSAE network every k time slot. As reported in [6,10], we set the SSAEs deep learning with 3 hidden layers. We compare the time slot k from the set {2, 4, 6, 8, 10}, which means that the input dimensions range from 400 to 2000.…”

Section: Ssaes Architecture Structurementioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Based Proactive Caching for Effective WSN‐Enabled Vision Applications

et al. 2019

Self Cite

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) have a wide range of applications scenarios in computer vision, from pedestrian detection to robotic visual navigation. In response to the growing visual data services in WSNs, we propose a proactive caching strategy based on Stacked Sparse Autoencoder (SSAE) to predict content popularity (PCDS2AW). Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network. Then, the SSAE network structure parameters and network model parameters are optimized through training. The proactive cache strategy implementation procedure is divided into four steps. (1) The SDN controller is responsible for dynamically collecting user request data package information in the WSNs network. (2) The SSAEs predicts the packet popularity based on the SDN controller obtaining user request data. (3) The SDN controller generates a corresponding proactive cache strategy according to the popularity prediction result. (4) Implement the proactive caching strategy at the WSNs cache node. In the simulation, we compare the influence of spatiotemporal data on the SSAE network structure. Compared with the classic caching strategy Hash + LRU, Betw + LRU, and classic prediction algorithms SVM and BPNN, the proposed PCDS2AW proactive caching strategy can significantly improve WSN performance.

show abstract

Section: Ssaes Architecture Structurementioning

confidence: 99%

“…To address the congestion issue, proactive caching [5,6], which wildly used in ICN, is introduced into the WSNs [7]. Data package caching will improve the performance of WSNs, including reducing packet latency, network traffic, and BER of transmission.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Proactive Caching for Effective WSN‐Enabled Vision Applications

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, in practice, CP profile is time-varying and not known in advance, therefore, it needs to be estimated from the past observations of the content requests. Deep learning based prediction is employed with huge training data in [8], [9]. In [10], auto regressive (AR) prediction cache is used to predict the number of requests in the time series.…”

Section: Introductionmentioning

confidence: 99%

Online Content Popularity Prediction and Learning in Wireless Edge Caching

Garg

Sellathurai

Bhatia

et al. 2020

IEEE Trans. Commun.

View full text Add to dashboard Cite

Caching popular contents in advance is an important technique to achieve low latency and reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process, optimal content placement caching probabilities are obtained to maximize the average success probability (ASP) for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. In this paper, we first propose two online prediction (OP) methods for forecasting CP viz., popularity prediction model (PPM) and Grassmannian prediction model (GPM), where the unconstrained coefficients for linear prediction are obtained by solving constrained non-negative least squares. To reduce the higher computational complexity per online round, two online learning (OL) approaches viz., weighted-follow-theleader and weighted-follow-the-regularized-leader are proposed, inspired by the OP models. In OP, ASP difference (i.e, the gap between the ASP achieved by prediction and that by known content popularity) is bounded, while in OL, sub-linear MSE regret and linear ASP regret bounds are obtained. With MovieLens dataset, simulations verify that OP methods are better for MSE and ASP difference minimization, while the OL approaches perform well for the minimization of the MSE and ASP regrets.

show abstract

“…Thus, another key challenge is to avoid the excessive allocation of resources for private link sets of one type of flow, and to avoid situations when available resources for another type of flow are insufficient. Essentially, unlike other works (such as References and ), this paper employs DRL to address this challenge.…”

Section: Introductionmentioning

confidence: 99%

Mix‐flow scheduling using deep reinforcement learning for software‐defined data‐center networks

Liu¹,

Cai

Wang

et al. 2019

Internet Technology Letters

Self Cite

View full text Add to dashboard Cite

For a mix‐flow scenario in software‐defined data‐center networks, how to simultaneously achieve the different performance requirements of the different types of flows is a considerable challenge. This paper proposes a mix‐flow scheduling scheme based on deep reinforcement learning (DRL). This paper establishes three private link sets for three types of flows. Then, DRL is employed to adaptively and intelligently allocate bandwidth for each type of flow according to the traffic variations across time and space. A novel metric is designed as a function of DRL's reward to guide the process of simultaneously maximizing the deadline meet rate for mice flows (MF) and minimizing the flow completion time for elephant flows. Within these three private link sets, three flow‐scheduling strategies (ie, priority‐based allocation for MF, stable matching‐based allocation for elephant flows with unknown sizes, and proportion‐based allocation for elephant flows with known sizes) are employed. A simulation demonstrates the effectiveness of the proposed scheme compared with previous methods (Fincher and pFabric). DRL‐Flow's overhead also is minimal to satisfy the scalability well and is deployable in a large‐scale network.

show abstract

Content Popularity Prediction and Caching for ICN: A Deep Learning Approach With SDN

Cited by 113 publications

References 42 publications

Deep Learning Based Proactive Caching for Effective WSN‐Enabled Vision Applications

Deep Learning Based Proactive Caching for Effective WSN‐Enabled Vision Applications

Online Content Popularity Prediction and Learning in Wireless Edge Caching

Mix‐flow scheduling using deep reinforcement learning for software‐defined data‐center networks

Contact Info

Product

Resources

About