Social Network Aware Caching for 5G Radio Access Network

Dash, Shatarupa; Sahu, Bharat J. R.; Saxena, Navrati

doi:10.1080/02564602.2017.1384334

Cited by 6 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To avoid this, a heterogeneous C-RAN implementing cloud computing into HetNets (Figure 11) is proposed to gain a scaled cooperative signal processing and networking functionalities [130]. In [131], each BS is equipped with a local cache in the C-RAN, which can be a service-centric RAN. BS caching forms a dynamic cluster.…”

Section: ) Heterogeneous C-ran (H-cran)mentioning

confidence: 99%

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

et al. 2022

View full text Add to dashboard Cite

The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the third step, we concentrate on the application layer and introduce the applications that can take advantage of dynamic service placement. The fourth step investigates evaluation environments used to validate the solutions, including simulators and testbeds. We introduce real-world datasets such as edge server locations, mobility traces, and service requests used to evaluate the methods. We compile a list of open issues and challenges categorised by various viewpoints in the last step.

show abstract

Section: ) Heterogeneous C-ran (H-cran)mentioning

confidence: 99%

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The multi-objective cache placement optimization is formulated as a classification problem. The formulation of cache placement as a binary classification was investigated in [212]. The authors considered the input propriety of video contents and user context requesting the video contents are the input attributes that defined the space of cache decision.…”

Section: Introductionmentioning

confidence: 99%

Latency Efficient Cache Placement Using Learning Techniques in Mobile Edge Networks

Mohammed

2024

Preprint

View full text Add to dashboard Cite

Future wireless networks provide interesting research challenges with the exponential growth in mobile data traffic, the advent of new high computational and real-time applications that cause many-fold increases in traffic and require low latency from the network. The emerging need to bring data closer to users and minimize the traffic off the macrocell base station (MBS) introduces caches at the edge of the networks. Storing most popular files in user terminals (UTs) and small base stations (SBSs) caches inside the mobile edge networks (MENs) is a promising approach to the challenges that face future data-rich wireless networks. Caching in the mobile UT allows obtaining requested contents directly from its nearby UT caches through device-to-device communication. This thesis addresses several challenges faced in developing a solution for cache placement at the edge of the network due to continuous changes in content popularity, user mobility, and the number of users within each network. It also considers the challenges related to high computation requirements of future applications that need to satisfy power and delivery time constraints. This dissertation aims to overcome those challenges in developing new solutions by employing intelligence and machine learning techniques (ILT) for mobile edge networks. We formulate the cache placement problem as a latency-efficient cache placement optimization problem that considers four objectives, to place contents in SBSs and UT caches. The multi-objective function takes advantage of user mobility patterns to decide on each SBS and UT cache content. The function is resolved into a weighted fusion decision with four objectives. Three of them are related to user mobility computed from previous data sets, and one objective is related to content popularity. We study the impact of user mobility on increasing the cache hit rate, which decreases the latency of downloading the requested data content. The results show the effect of user mobility on reducing the total energy consumed for transmitting the contents to the UTs. We propose a new cache placement algorithm based on user locations, contact probability, communication range, contact duration, and content popularity, formulating cache placement decisions as a binary classification problem (to cache and not to cache). Artificial neural networks (ANN), support vector machine (SVM), and logistic regression (LR) are used to model cache placement decisions. We investigate the characteristics of the input features (attributes) and the properties of these characteristics that affect supervised machine learning approaches. The performance of the new cache placement models using supervised learning techniques is evaluated to study the sensitivity of classification decisions with the change of system parameters. Finally, we develop a semi-supervised self-training (SSST) classification model for the cache placement problem. We assess the proposed SSST algorithm through experiments with datasets on different learning techniques. The performance comparison of different machine learning models was carried out with the same datasets. For the hit rate, we investigated the sensitivity of the classification by the changes in the environment parameters to show the effectiveness of the proposed theme.

show abstract