Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks

This article explores one of the key enablers of beyond $4$G wireless networks leveraging small cell network deployments, namely proactive caching. Endowed with predictive capabilities and harnessing recent developments in storage, context-awareness and social networks, peak traffic demands can be substantially reduced by proactively serving predictable user demands, via caching at base stations and users' devices. In order to show the effectiveness of proactive caching, we examine two case studies which exploit the spatial and social structure of the network, where proactive caching plays a crucial role. Firstly, in order to alleviate backhaul congestion, we propose a mechanism whereby files are proactively cached during off-peak demands based on file popularity and correlations among users and files patterns. Secondly, leveraging social networks and device-to-device (D2D) communications, we propose a procedure that exploits the social structure of the network by predicting the set of influential users to (proactively) cache strategic contents and disseminate them to their social ties via D2D communications. Exploiting this proactive caching paradigm, numerical results show that important gains can be obtained for each case study, with backhaul savings and a higher ratio of satisfied users of up to $22\%$ and $26\%$, respectively. Higher gains can be further obtained by increasing the storage capability at the network edge.Comment: accepted for publication in IEEE Communications Magazin

Cooperative video transmission strategies via caching in small-cell networks

Small-cell network is a promising solution to the high video traffic. However, it has some fundamental problems, i.e., high backhaul cost, quality of experience (QoE) and interference. To address these issues, we propose a cooperative transmission strategy for video transmission in small-cell networks with caching. In the scheme, each video file is encoded into segments using a maximum distance separable rateless code. Then, a portion of each segment is cached at a certain small-cell base station (SBS), so that the SBSs can cooperatively transmit these segments to users without incurring high backhaul cost. When there is only one active user in the network, a greedy algorithm is utilized to deliver the video-file segment from the SBS with good channel state to the user watching videos in real time. This reduces video freezes and improves the QoE. When there exist several active users, interference will appear among them. To deal with interference, interference alignment (IA) is adopted. Based on the scheme for a single user, the greedy algorithm and IA are combined to transmit video-file segments to these users, and the performance of the system can be significantly improved. Simulation results are presented to show the effectiveness of the proposed scheme

Evaluating the impact of caching on the energy consumption and performance of progressive web apps

Context. Since today mobile devices have limited battery life, the energy consumption of the software running on them can play a strong role with respect to the success of mobile-based businesses. Progressive Web Applications (PWAs) are built using common web technologies like HTML, CSS, and JavaScript and are commonly used for providing a better user experience to mobile users. Caching is the main technique used by PWA developers for optimizing network usage and for providing a meaningful experience even when the user's device is offline. Goal. This paper aims at assessing the impact of caching on both the energy consumption and performance of PWAs. Method. We conducted an empirical experiment targeting 9 real PWAs developed by third-party developers. The experiment is designed as a 1 factor-2 treatments study, with the usage of caching as the single factor and the status of the cache as treatments (empty vs populated cache). The response variables of the experiment are (i) the energy consumption of the mobile device and (ii) the page load time of the PWAs. The experiment is executed on a real Android device running the Mozilla Firefox browser. Results. Our results show that PWAs do not consume significantly different amounts of energy when loaded either with an empty or populated cache. However, the page load time of PWAs is significantly lower when the cache is already populated, with a medium effect size. Conclusions. This study confirms that PWAs are promising in terms of energy consumption and provides evidence that caching can be safely exploited by PWA developers concerned with energy consumption. The study provides also empirical evidence that caching is an effective technique for improving the user experience in terms of page loading time of PWAs

Joint Optimization for Social Content Delivery in Wireless Networks

Over the last decade, success of social networks has significantly reshaped how people consume information. Recommendation of contents based on user profiles is well-received. However, as users become dominantly mobile, little is done to consider the impacts of the wireless environment, especially the capacity constraints and changing channel. In this dissertation, we investigate a centralized wireless content delivery system, aiming to optimize overall user experience given the capacity constraints of the wireless networks, by deciding what contents to deliver, when and how. We propose a scheduling framework that incorporates content-based reward and deliverability. Our approach utilizes the broadcast nature of wireless communication and social nature of content, by multicasting and precaching. Results indicate this novel joint optimization approach outperforms existing layered systems that separate recommendation and delivery, especially when the wireless network is operating at maximum capacity. Utilizing limited number of transmission modes, we significantly reduce the complexity of the optimization. We also introduce the design of a hybrid system to handle transmissions for both system recommended contents ('push') and active user requests ('pull'). Further, we extend the joint optimization framework to the wireless infrastructure with multiple base stations. The problem becomes much harder in that there are many more system configurations, including but not limited to power allocation and how resources are shared among the base stations ('out-of-band' in which base stations transmit with dedicated spectrum resources, thus no interference; and 'in-band' in which they share the spectrum and need to mitigate interference). We propose a scalable two-phase scheduling framework: 1) each base station obtains delivery decisions and resource allocation individually; 2) the system consolidates the decisions and allocations, reducing redundant transmissions. Additionally, if the social network applications could provide the predictions of how the social contents disseminate, the wireless networks could schedule the transmissions accordingly and significantly improve the dissemination performance by reducing the delivery delay. We propose a novel method utilizing: 1) hybrid systems to handle active disseminating requests; and 2) predictions of dissemination dynamics from the social network applications. This method could mitigate the performance degradation for content dissemination due to wireless delivery delay. Results indicate that our proposed system design is both efficient and easy to implement