Incentivized Social-Aware Proactive Device Caching With User Preference Prediction

Ren, Jiazhi; Tian, Hui; Lin, Yuanzhuo; Fan, Shaoshuai; Nie, Gaofeng; Wu, Hao; Zhang, Fan

doi:10.1109/access.2019.2942440

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…Reference [ 21 ] mentions that the traffic fluctuation of small cells is much greater than that of large ones since small cells and cannot benefit from the law of large numbers. Reference [ 22 ] proposed that global popularity can only represent the average content request trend of many users. Therefore, it is more reasonable to use user preferences than global popularity to maximize the click-through rate.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Mobile Edge Computing with Efficient Load Balancing Using Load Estimation in Ultra-Dense Network

Chen

Zhu

Liu

et al. 2021

Sensors

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With the exponential growth of mobile devices and the emergence of computationally intensive and delay-sensitive tasks, the enormous demand for data and computing resources has become a big challenge. Fortunately, the combination of mobile edge computing (MEC) and ultra-dense network (UDN) is considered to be an effective way to solve these challenges. Due to the highly dynamic mobility of mobile devices and the randomness of the work requests, the load imbalance between MEC servers will affect the performance of the entire network. In this paper, the software defined network (SDN) is applied to the task allocation in the MEC scenario of UDN, which is based on routing of corresponding information between MEC servers. Secondly, a new load balancing algorithm based on load estimation by user load prediction is proposed to solve the NP-hard problem in task offloading. Furthermore, a genetic algorithm (GA) is used to prove the effectiveness and rapidity of the algorithm. At present, if the load balancing algorithm only depends on the actual load of each MEC, it usually leads to ping-pong effect. It is worth mentioning that our method can effectively reduce the impact of ping-pong effect. In addition, this paper also discusses the subtask offloading problem of divisible tasks and the corresponding solutions. At last, simulation results demonstrate the efficiency of our method in balancing load among MEC servers and its ability to optimize systematic stability.

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Section: Related Workmentioning

confidence: 99%

Enhancing Mobile Edge Computing with Efficient Load Balancing Using Load Estimation in Ultra-Dense Network

Chen

Zhu

Liu

et al. 2021

Sensors

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“…For modelling simplification, the following optimization models are under the assumption that the user's interested content is already known, which indicates becomes a binary indicator to bridge user and content . The user preference prediction has been extensively studied in the context of caching, such as [25] and [26], and this technique is beyond the scope as well as complimentary of this paper. The influence of the predicted accuracy on system energy consumption is discussed in Section IV.…”

Section: Optimization Modelmentioning

confidence: 99%

Energy-Efficient Proactive Caching with Multipath Routing

Wang¹,

Friderikos²

2021

Preprint

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The ever-continuing explosive growth of on-demand content requests has imposed great pressure on mobile/wireless network infrastructures. To ease congestion in the network and increase perceived user experience, caching popular content closer to the end-users can play a significant role and as such this issue received significant attention over the last few years. Additionally, energy efficiency is treated as a fundamental requirement in the design of next-generation mobile networks. However, there has been little attention to the overlapping area between energy efficiency and network caching especially when considering multipath routing. To this end, this paper proposes an energy-efficient caching with multipath routing support. The proposed scheme provides a joint anchoring of popular content into a set of potential caching nodes with optimized multi-path support whilst ensuring a balance between transmission and caching energy cost. The proposed model also considers different content delivery modes, such as multicast and unicast. Two separated Integer-Linear Programming (ILP) models are formulated for each delivery mode. To tackle the curse of dimensionality we then provide a greedy simulated annealing algorithm, which not only reduces the time complexity but also provides a competitive performance as ILP models. A wide set of numerical investigations has shown that the proposed scheme is more energy-efficient compared with other widely used approaches in caching under the premise of network resource limitation.

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“…Such applications require Ultra-Reliable Low Latency Communications (URLLC) and high user QoE. Next-generation 5G technologies envision to support the requirements of the modern applications with disruptive data communication technologies (for example, mmWave communication [12]), caching at various layers of the network [14], and edge network architecture [51]. Therefore, to cope with ever-increasing bandwidth demands of emerging applications and constrained transit, backhaul, fronthaul, and radio access networks (RAN), intelligent usercentric edge caching is indispensable [17], [16].…”

Section: A Motivationmentioning

confidence: 99%

“…The low-level design considerations will be debated and illustrated in forthcoming sections. Socialawareness, [133] mobility patterns [127], and user preferences [14] are input to an ML framework that can be federated among multiple nodes, hosted at distributed MEC servers, C-RAN, or centralized CDN. The intelligent and optimal decisions are fed-back to the caches that are managed by network virtualization techniques [89], [134].…”

Section: State-of-the-art: Machine Learning Based Cachingmentioning

confidence: 99%

“…Therefore, Next-generation-networks (NGN), i.e., 5G evolved with the help of emerging communication technologies, such as millimeter-wave (mmWave) communication and massive Multiple Input Multiple Output (MIMO) [12], [13]. Users in 5G networks often have similar interests, and content can be stored locally (cache) to reduce access latency and load on backhaul, core, and transit networks [14]. Without caching, a large amount of backhaul traffic is consumed by transmitting duplicate copies of popular content [15].…”

Section: Introductionmentioning

confidence: 99%

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Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Shuja,

Bilal,

Alasmary

et al. 2020

Preprint

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Edge networks provide access to a group of proximate users who may have similar content interests. Caching popular content at the edge networks leads to lower latencies while reducing the load on backhaul and core networks with the emergence of high-speed 5G networks. Edge networks are complex and dynamic. User mobility, preferences, and content popularity are the dominant dynamic features of the edge networks. Temporal and social features of content, such as the number of views and likes are applied to estimate the popularity of content from a global perspective. However, such estimates may not be mapped to an edge network with particular social and geographic characteristics. In edge networks, machine learning techniques can be applied to predict content popularity based on user preferences, user mobility based on user location history, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network to lower latencies and increase cache hits. This article surveys the application of machine learning techniques for caching content in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (application and objective), (b) caching strategy (policy, location, and objective), and edge network (type and delivery strategy). We further survey supporting concepts for optimal edge caching decisions that require the application of machine learning. These supporting concepts are social-awareness, popularity prediction, and community detection in edge networks. A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning towards caching in edge networks.

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Incentivized Social-Aware Proactive Device Caching With User Preference Prediction

Cited by 10 publications

References 32 publications

Enhancing Mobile Edge Computing with Efficient Load Balancing Using Load Estimation in Ultra-Dense Network

Enhancing Mobile Edge Computing with Efficient Load Balancing Using Load Estimation in Ultra-Dense Network

Energy-Efficient Proactive Caching with Multipath Routing

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

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