Mobile Edge Caching Research Articles

Low Cost Video Streaming through Mobile Edge Caching: Modelling and Optimization

Caching content at the edge of mobile networks is considered as a promising way to deal with the data tsunami. In addition to caching at fixed base stations or user devices, it has been recently proposed that an architecture with public or private transportation acting as mobile relays and caches might be a promising middle ground. While such mobile caches have mostly been considered in the context of delay tolerant networks, in this paper we argue that they could be used for low cost video streaming without the need to impose any delay on the user. Users can prefetch video chunks into their playout buffer from encountered vehicle caches (at low cost) or stream from the cellular infrastructure (at higher cost) when their playout buffer empties while watching the content. Our main contributions are: (i) to model the playout buffer in the user device and analyze its idle periods which correspond to bytes downloaded from the infrastructure; (ii) to optimize the content allocation to mobile caches; and to minimize the expected number of non-offloaded bytes. We perform trace-based simulations to support our findings showing that up to 60 percent of the original traffic could be offloaded from the main infrastructure.

Cooperative Edge Caching in User-Centric Clustered Mobile Networks

With files proactively stored at base stations (BSs), mobile edge caching enables direct content delivery without remote file fetching, which can reduce the end-to-end delay while relieving backhaul pressure. To effectively utilize the limited cache size in practice, cooperative caching can be leveraged to exploit caching diversity, by allowing users served by multiple base stations under the emerging user-centric network architecture. This paper explores delay-optimal cooperative edge caching in large-scale user-centric mobile networks, where the content placement and cluster size are optimized based on the stochastic information of network topology, traffic distribution, channel quality, and file popularity. Specifically, a greedy content placement algorithm is proposed based on the optimal bandwidth allocation, which can achieve $(1-{1/e})$ -optimality with linear computational complexity. In addition, the optimal user-centric cluster size is studied, and a condition constraining the maximal cluster size is presented in explicit form, which reflects the tradeoff between caching diversity and spectrum efficiency. Extensive simulations are conducted for analysis validation and performance evaluation. Numerical results demonstrate that the proposed greedy content placement algorithm can reduce the average file transmission delay up to 45 percent compared with the non-cooperative and hit-ratio-maximal schemes. Furthermore, the optimal clustering is also discussed considering the influences of different system parameters.

Security in Mobile Edge Caching with Reinforcement Learning

Mobile edge computing usually uses caching to support multimedia contents in 5G mobile Internet to reduce the computing overhead and latency. Mobile edge caching (MEC) systems are vulnerable to various attacks such as denial of service attacks and rogue edge attacks. This article investigates the attack models in MEC systems, focusing on both the mobile offloading and the caching procedures. In this article, we propose security solutions that apply reinforcement learning (RL) techniques to provide secure offloading to the edge nodes against jamming attacks. We also present lightweight authentication and secure collaborative caching schemes to protect data privacy. We evaluate the performance of the RL-based security solution for mobile edge caching and discuss the challenges that need to be addressed in the future.

Data Quality Guarantee for Credible Caching Device Selection in Mobile Crowdsensing Systems

Mobile crowdsensing systems (MCSs) present a flexible and economical alternative to traditional infrastructure-based large-scale sensing through the recruitment of personal mobile devices as data sources. As this becomes a popular sensing approach, it will impact the capacity of typical centralized cellular communication infrastructures widely adopted by MCS applications and any costs accrued. Following the trend toward edge processing, mobile edge caching offloads data and services from the system core to reduce service latency and bandwidth occupation. However, in the MCS case the edge devices are owned by the general public and are therefore more vulnerable to data or calculation manipulation by the user. We now better understand sensor data and user trustworthiness but have no way to determine which devices can also be trusted (i.e., act as a credible caching device). In this article, we treat the quality of sensing data reported by each user as an indication of their possibility of providing credible caching services. Specifically, we conduct a comprehensive study of the data quality problem with regard to cache-enabled MCSs, and develop an incentivization method to encourage users to actively provide high-quality data. That is, quality-aware behavior evaluation is core to the credible caching device selection process. Results of extensive simulations based on realworld data verify the effectiveness of our design. We also highlight several promising research directions that remain open for further elaboration.

QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

Caching at mobile edge servers can smooth temporal traffic variability and reduce the service load of base stations in mobile video delivery. However, the assignment of multiple video representations to distributed servers is still a challenging question in the context of adaptive streaming, since any two representations from different videos or even from the same video will compete for the limited caching storage. Therefore, it is important, yet challenging, to optimally select the cached representations for each edge server in order to effectively reduce the service load of base station while maintaining a high quality of experience (QoE) for users. To address this, we study a QoE-driven mobile edge caching placement optimization problem for dynamic adaptive video streaming that properly takes into account the different rate-distortion (R–D) characteristics of videos and the coordination among distributed edge servers. Then, by the optimal caching placement of representations for multiple videos, we maximize the aggregate average video distortion reduction of all users while minimizing the additional cost of representation downloading from the base station, subject not only to the storage capacity constraints of the edge servers, but also to the transmission and initial startup delay constraints of the users. We formulate the proposed optimization problem as an integer linear program to provide the performance upper bound, and as a submodular maximization problem with a set of knapsack constraints to develop a practically feasible cost benefit greedy algorithm. The proposed algorithm has polynomial computational complexity and a theoretical lower bound on its performance. Simulation results further show that the proposed algorithm is able to achieve a near-optimal performance with very low time complexity. Therefore, the proposed optimization framework reveals the caching performance upper bound for general adaptive video streaming systems, while the proposed algorithm provides some design guidelines for the edge servers to select the cached representations in practice based on both the video popularity and content information.

CDS-MEC: NFV/SDN-based Application Management for MEC in 5G Systems

This paper presents and evaluates the first open-source Network Function Virtualization (NFV)/Software Defined Networking (SDN)-based Mobile Edge Computing (MEC) platform. Our platform solves the Mobile Edge (ME) management issues with respect to Application (App) provisioning and traffic management. First, the ME Apps are managed as Virtual Network Functions (VNFs) on top of the virtual environment through the Juju VNF Manager (VNFM). Second, we develop an SDN controller to manage traffic on the ME System. Third, unlike other relevant architectures of ME systems, we use the control plane (i.e., S1 interface) to derive appropriate states for traffic management. Finally, we evaluate our solution in two use-cases: ME caching and Information Centric (ICN)/Delay Tolerant (DTN) Public Safety communication (PS). The MEC caching framework displays improved user Quality of Experience, e.g., latency, in comparison to direct communication, while the PS solution provides a residual mean of communication for rescue teams, when the network core (EPC) and a Public Data Network (PDN) are unavailable.

QoE-Traffic Optimization Through Collaborative Edge Caching in Adaptive Mobile Video Streaming

Multi-access edge computing has been proposed as a promising approach to localize the access of mobile clients to the network edges, therefore, reducing significantly the traffic congestion on the backhaul network. Due to time-varying wireless channel condition, the video caching at the mobile edges for dynamic adaptive video streaming over HTTP (DASH) needs to be efficiently handled to alleviate the high bandwidth demand on the backhaul network and improve the quality of experience (QoE) of end users. We investigate the impact of collaborative mobile edge caching on joint QoE and backhaul data traffic by proposing the joint QoE-traffic optimization with collaborative edge caching which introduces the BFTR (backhaul/fronthaul traffic ratio) parameter adjustable by the mobile network operator. We then design a self-tuned bitrate selection algorithm with low complexity to solve the optimization problem and further propose an efficient cache replacement strategy called retention-based collaborative caching. Through simulation-based evaluations, we show a noticeable gain in the percentage of cache miss and specify some threshold for BFTR parameter after which the significant reduction in the data traffic with further improvement in average video bitrate is obtained using collaborative caching. Our findings help mobile edge system developers design an efficient collaborative caching mechanism for 5G networks.