81 research outputs found
Multidimensional content modeling and caching in D2D edge networks
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Future Internet is going to be shaped by networked multimedia services with exploding video traffic becoming the dominant payload. That evolution requires a remedial shift from the connection-oriented architecture to a content-centric one. Another technique to address this capacity crunch is to improve spectral utilization through new networking paradigms at the wireless network edge. To this end, Device-to-Device (D2D) communications has the potential for boosting the capacity and energy efficiency for content-centric networking. To design and implement efficient content-centric D2D networks, rigorous content modeling and in-network caching mechanisms based on such models are crucial. In this work, we develop a multidimensional content model based on popularity, chunking and layering, and devise caching schemes through this model. Our main motivation is to improve the system performance via our caching strategies. The numerical analysis shows the interplay among different system parameters and performance metrics: while our schemes perform slightly poorer in terms of system goodput, they also decrease the system energy expenditure. Overall, this improvement dominates the loss in the goodput, leading to greater energy efficiency compared to the commonly-used caching technique Least Recently Used (LRU)
Fine-grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks
The 5G networks have extensively promoted the growth of mobile users and
novel applications, and with the skyrocketing user requests for a large amount
of popular content, the consequent content delivery services (CDSs) have been
bringing a heavy load to mobile service providers. As a key mission in
intelligent networks management, understanding and predicting the distribution
of CDSs benefits many tasks of modern network services such as resource
provisioning and proactive content caching for content delivery networks.
However, the revolutions in novel ubiquitous network architectures led by
ultra-dense networks (UDNs) make the task extremely challenging. Specifically,
conventional methods face the challenges of insufficient spatio precision,
lacking generalizability, and complex multi-feature dependencies of user
requests, making their effectiveness unreliable in CDSs prediction under 5G
UDNs. In this paper, we propose to adopt a series of encoding and sampling
methods to model CDSs of known and unknown areas at a tailored fine-grained
level. Moreover, we design a spatio-temporal-social multi-feature extraction
framework for CDSs hotspots prediction, in which a novel edge-enhanced graph
convolution block is proposed to encode dynamic CDSs networks based on the
social relationships and the spatio features. Besides, we introduce the
Long-Short Term Memory (LSTM) to further capture the temporal dependency.
Extensive performance evaluations with real-world measurement data collected in
two mobile content applications demonstrate the effectiveness of our proposed
solution, which can improve the prediction area under the curve (AUC) by 40.5%
compared to the state-of-the-art proposals at a spatio granularity of 76m, with
up to 80% of the unknown areas
Cooperative caching and video characteristics in D2D edge networks
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Device-to-device (D2D) transmissions in wireless edge networks are promising for optimizing system-wide energy consumption and improving system service capacity. Cooperative content caching similarly serves efficiency goals for data-intensive applications in edge networks. In this work, we propose two cooperative cache replacement algorithms in D2D networks to support these techniques: i) distance-based ii) priority-class based. Video content dissemination in an edge network is our main use-case. In such content traffic, video characteristics have a significant impact on the system behavior. Therefore, we also investigate the effect of content scene change dynamics in our system. Distance based cooperation outperforms LRU, MIN-ACC and SXO in terms of goodput while priority-class based approach consumes less energy than MIN-ACC with almost the same consumption as LRU, especially under fast changing scene regime. Besides, it is energy-wise slightly more rewarding than SXO for the fastest-changing scene case
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
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