A Deep Reinforcement Learning-Based Framework for Content Caching

Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, this work presents a DRL-based framework with Wolpertinger architecture for content caching at the base station. The proposed framework is aimed at maximizing the long-term cache hit rate, and it requires no knowledge of the content popularity distribution. To evaluate the proposed framework, we compare the performance with other caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU), and First-In First-Out (FIFO) caching strategies. Meanwhile, since the Wolpertinger architecture can effectively limit the action space size, we also compare the performance with Deep Q-Network to identify the impact of dropping a portion of the actions. Our results show that the proposed framework can achieve improved short-term cache hit rate and improved and stable long-term cache hit rate in comparison with LRU, LFU, and FIFO schemes. Additionally, the performance is shown to be competitive in comparison to Deep Q-learning, while the proposed framework can provide significant savings in runtime.Comment: 6 pages, 3 figure

Mobility management: deployment and adaptability aspects through mobile data traffic analysis

The expected boost in mobile data traffic and the evolution towards the next generation of networks are making cellular operators reconsider whether current approaches for handling mobility could be improved, according to the characteristics of the mobile traffic that actually flows through real networks. In this work, we make use of extensive analysis of real network traces to infer the main characteristics of mobile data traffic for a particular operator. Our analysis focuses on the features related to mobility, i.e., location information, number of handovers, or duration of the data traffic exchange. New techniques to gather the mobility characteristics of the user based on data and control packets correlation are designed and applied to compare the gains of deploying different mobility management approaches.The research leading to these results has received funding from the EU Seventh Framework Programme (FP7/2007-2013) under grant agreement 318115 (Connectivity management for eneRgy Op- timised Wireless Dense networks, CROWD). The work of Antonio de la Oliva has also been funded by the EU H2020 5G-Crosshaul Project (grant no. 671598)

A review on green caching strategies for next generation communication networks

© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching

Leveraging Big Data Analytics for Cache-Enabled Wireless Networks

International audienceWhile 5G wireless networks are expected to handle the ever growing data avalanche, classical deployment/optimiza-tion approaches such as hyper-dense deployment of base stations or having more bandwidth are cost-inefficient, and are therefore seen as stopgaps. In this regard, context-aware approaches which exploits human predictability, recent advances in storage, edge/cloud computing and big data analytics are needed. In this article, we approach this problem from a proactive caching perspective where gains of cache-enabled base stations in 5G wireless are studied. In particular, huge amount of real data from a telecom operator in Turkey is collected/processed on a big data platform, and an analysis is carried out for content popularity estimation for caching, aiming to improve users' experience in terms of request satisfactions and offloading the backhaul. Subsequently, with this mobile traffic data collected from many base stations within several hours of time interval and the estimation of content popularity via machine learning tools, we investigate the gains of proactive caching via numerical simulations. The results show that proactive caching fulfils 100% of user request satisfaction and offloads 98% of the backhaul, in a setting of 16 base stations with 15.4 Gbyte of storage size (87% of the total catalog size) and 10% of content ratings