Mobility-Aware Traffic Offloading via Cooperative Coded Edge Caching

Caching on the edge has been recognized as an effective solution to tackle the backhaul constraint of network densification. However, most related works ignored user mobility in wireless networks, which is unreasonable under the background of network densification. For a more flexible and context-aware caching decision, the concept of caching on the edge can be extended to mobile edge computing (MEC) that enables computation and storage resources at mobile edge networks. With MEC servers deployed on base stations, a huge amount of collected radio access network context data can be analyzed and utilized to render a caching scheme adaptive to user’s context-aware information. In this regard, a novel mobility-aware coded probabilistic caching scheme is proposed for MEC-enabled small cell networks (SCNs). Different from previous mobility-aware caching schemes, user mobility and distributed storage are incorporated into a conventional probabilistic caching scheme, with the aim of throughput maximization. Based on stochastic geometry theory and a modified mobility model of discrete random jumps, the explicit expression of throughput is derived. Due to the complexity of the expression, two light-weight heuristic algorithms are provided to numerically obtain the optimal solutions. Moreover, a significant trade-off among the gains of mobility diversity, content diversity, and channel selection diversity is discussed, and we further numerically analyze how such a trade-off is influenced by user mobility, content popularity, and backhaul capacity, with some fundamental insights into the application of the proposed scheme in MEC-enabled SCNs. The superiority of our proposed scheme is demonstrated by the comparisons with the classical M most popular caching scheme and the conventional probabilistic caching scheme. Numerical results show that our proposed caching scheme achieves higher throughput than those of the other two, especially when users of intense mobility request contents, of which the popularity profile is not skewed, in MEC-enabled SCNs with poor backhaul capacity, indicating that the proposed caching scheme is a promising solution for network densification.

Caching at the user devices is a key technology to alleviate backhaul load and improve users' quality of experience in mobile edge networks. However, due to the concern of privacy and limited battery life, users are not willing to share their resources. In this study, the authors jointly design the caching strategy and incentive scheme to encourage the users to share their storage resources and improve the social welfare of the cellular network. First, the user equipments (UEs) are classified into different types according to their preference to share cache resources. Then, based on contract theory, an optimisation problem with necessary constraints is formulated aiming at maximising the base station (BS)'s utility. The reward the BS pays for a UE is based on the total amount of contents it shares with other UEs, which is related to the caching policy of UEs. A heuristic caching strategy is proposed and the optimal contract is obtained. Finally, the system performance of the proposed solution is analysed, simulation results show that the incentive scheme can improve the willingness of users to share contents, and the proposed caching strategy contributes more to the social welfare compared with two baseline caching schemes: fair caching and random caching.

We investigate the near-optimal video layer placement in cache-enabled small-cell networks to provide on-demand video services with differential perceptual experiences. By employing scalable video coding, each video file is encoded into multiple layers, which are then stored in the local cache of small-cell base stations (SBSs). Based on the proposed caching scheme, we define the concept of average offloaded traffic (AOT), which quantitatively indicates the total amount of data traffic that can be sourced from the local cache of SBSs. Relying on stochastic geometry, we derive the expression for the successful transmission probability, based on which the closed form of AOT is obtained. Afterwards, under the cache size constraint of each SBS, the AOT maximization problem is formulated. Due to the NP-hard property of this integer programming problem, it is first relaxed into the convex form. Then, a greedy-strategy based heuristic algorithm is proposed, which can yield the near-optimal layer caching indicators. Numerical results validate our derivations and demonstrate that the proposed layer caching scheme outperforms the benchmark in terms of the AOT performance.

Mobile edge storage is an emerging computing paradigm which enables users to enjoy high quality of experience when they access cloud services. However, extremely fast increasing multimedia data traffic from cloud to mobile users comes with the challenges of low-latency and high-bandwidth over cloud computing model. How to design cache strategies by exploiting terminal nodes' utility is still a challenging issue. In this paper, by exploiting 5G technology and cooperative edge computing, we propose a cooperative edge caching framework to tackle three issues simultaneously in the application scenarios of massive short video services: (1) reducing data transportation latency to improve mobile uses' quality of experience; (2) mitigating data traffic pressure on backbone network; (3) decreasing the workload on cloud servers. Firstly, we define a cooperative edge caching network model, in which many edge nodes equipped with capacities of storage, user admittance and Internet access form an ad hoc network and each node serves multiple mobile users. Secondly, we design a non-cooperative game model to investigate the cooperation behavior among the edge nodes, we allow each edge node server as a player and make decision of cache (storage) initialization and replacement strategies. Thirdly, we find there is a Nash Equilibrium in the proposed game. Finally, we design a distributed algorithm to achieve the equilibrium according to our theoretical analysis. Experiment results show that the proposed game-based edge caching strategy can improve mobile uses' quality of experience by 20% latency decreasing and reduce 80% backbone traffic and cloud workload.

Mobile edge cache (MEC)-enabled air-to-ground integrated Internet of Vehicles (IoV) technology can solve wireless network backhaul congestion and high latency, but security problems such as eavesdropping are often ignored when designing cache strategies. In this paper, we propose a joint design of cache strategy and physical layer transmission to improve the security offloading ratio of MEC-enabled air-to-ground IoV. By using the random geometry theory and Laplace transform, we derive the closed-form expression of the network security offloading ratio, which is defined as the probability that the request vehicle (RV) successfully finds the required file around it and obtains the file with a data rate larger than a given threshold. During the file acquisition process, we collectively consider the impact of the successful connection and secure transmission in the vehicle wireless communication. Then, we establish an optimization problem for maximizing the network security offloading ratio, in which the cache strategy and the secure transmission rate are jointly optimized. Furthermore, we propose an alternating optimization algorithm to solve the joint optimization problem. Simulation experiments verify the correctness of our theoretical derivation, and prove that the proposed cache strategy is superior to other existing cache strategies.

The explosion of traffic caused by the rapid growth of multimedia services of Internet of Vehicles (IoV) has brought heavy load to mobile networks. The edge caching of the Internet of vehicles is considered as a promising technology. When the existing content caching strategy is used in the vehicle network, it faces the challenge of high content caching delay caused by the high-speed mobility of vehicle users and insufficient social relations. To address these challenges, this paper proposes a Cooperative Edge Caching Scheme based on Mobility Prediction and Society Aware (CCMPSA). In this strategy, the Long Short-Term Memory (LSTM) network is used to predict the location of the vehicle at the next moment, the vehicle cache nodes are selected according to the social relationship reflected by the similarity of interest and communication probability between the vehicles, and the dynamic decision of the content cache problem is realized by deep reinforcement learning. The simulation results show that the performance of the proposed strategy is better than random caching and non-cooperative caching algorithms, and it not only reduces the content transmission delay and improves the cache hit ratio, but also improves the experience quality of the whole system.KeywordsCaching decisionsDeep reinforcement learningThe edge cachingThe long short-term memory networkInternet of Vehicles

This paper addresses the four enabling technologies, namely multi-user sparse code multiple access (SCMA), content caching, energy harvesting, and physical layer security for proposing an energy and spectral efficient resource allocation algorithm for the access and backhaul links in heterogeneous cellular networks. Although each of the above mentioned issues could be a topic of research, in a real situation, we would face a complicated scenario where they should be considered jointly, and hence, our target is to consider these technologies jointly in a unified framework. Moreover, we propose two novel content delivery scenarios: 1) single frame content delivery (SFCD), and 2) multiple frames content delivery (MFCD), where the time duration of serving user requests is divided into several frames. In the first scenario, the requested content by each user is served over one frame. However, in the second scenario, the requested content by each user can be delivered over several frames. We formulate the resource allocation for the proposed scenarios as optimization problems where our main aim is to maximize the energy efficiency of access links subject to the transmit power and rate constraints of access and backhaul links, caching and energy harvesting constraints, and SCMA codebook allocation limitations. Due to the practical limitations, we assume that the channel state information values between eavesdroppers and base stations are uncertain and design the network for the worst case scenario. Since the corresponding optimization problems are mixed integer non-linear and nonconvex programming, NP-hard, and intractable, we propose an iterative algorithm based on the well-known alternate and successive convex approximation methods. In addition, the proposed algorithms are studied from the computational complexity, convergence, and performance perspectives. Moreover, the proposed caching scheme outperforms the existing traditional caching schemes like random caching and most popular caching. We also study the effect of joint and disjoint considerations of enabling technologies for the performance of next-generation networks. On the one hand, our proposed caching strategy increases slightly the computational complexity by 3%, 1.1%, and 2% compared to no caching, SFCD, and disjoint solution, respectively. On the other hand, the proposed caching scheme achieves a performance gain of 43%, 9.4%, and 51.3% compared to the three state-of-the-art schemes.

With the wide application of the Internet of Things, the number of network edge devices is increasing rapidly, resulting in huge network traffic that brings huge challenges to the current network. Aiming at the problem of heavy network load, researchers proposed some caching strategies. However, current strategies have some limitations. These existing caching strategies are usually global within the whole network. Most of them are to reduce the network delay and allow users to obtain content more quickly and easily. This paper proposes a network caching strategy based on edge and cloud coordination (ECC).The strategy divides the caching network into two parts (core and edge) to discuss different caching strategies. By choosing reasonable caching strategies in the core network and the edge network to implement different caching goals for different areas. Besides, the paper sets up PN nodes for coordinating and optimizing the cache resources between the edge and the core. Experimental results show that ECC has significant advantages in Server Load Reduction Ratio, Average Hop Reduction Ratio and Cache Redundancy compared with existing methods.

To alleviate the pressure brought by the explosion of mobile video traffic on present cellular networks, small cell base stations (SBSs) with caching ability are introduced. In this letter, we consider the caching strategy of scalable video coding streaming over heterogeneous wireless network containing SBSs. We formulate an integer programming problem to minimize the average download time under the constraint of cache size at each SBS, and show that finding the optimal caching placement strategy is NP-hard. Heuristic solution was proposed based on the convex programming relaxation, which reveals the structural properties of cache allocation for each video. Simulation results demonstrate that our proposed caching strategies acquire significant performance gain compared with conventional caching policy.

Massive multiple input multiple output (MIMO) and full duplex (FD) communication are being considered as potential candidates for the spectrum efficient 5G wireless networks. In this paper, we develop a tractable model for downlink (DL) and uplink (UL) transmission in K-tier heterogeneous cellular networks (HCNs) with massive MIMO macrocells and full duplex (FD) small cells for spectrum efficiency. In the considered HCNs, the performance of the mobile user (MU) is limited by several sources of interference, specifically due to FD nature of small cell base stations (SBSs). A stochastic geometry based model of the proposed HCNs is provided which allows to derive the DL and UL rate coverage probabilities of such a system. Monte Carlo simulations confirm the accuracy of the analytical results, while numerical results reveal that equipping large number of MIMO antennas at macro base stations (MBSs) enhances the DL rate coverage probability of a random MU in HCNs. The results show that to achieve the maximum joint DL and UL performance gain in HCNs with FD small cells, both SBSs' density and SBSs' transmit power should be optimized. Moreover, the UL performance can be improved by decreasing the SBSs receivers sensitivity and increasing the UL power control factor.

Content caching in mobile edge networks has stirred up tremendous research attention. However, most existing studies focus on predicting content popularity in mobile edge servers (MESs). In addition, they overlook how the content is cached, especially how to cache the content with user devices. In this paper, we propose CMU, a three-layer (Cloud-MES-Users) content caching framework and investigate the performance of different caching strategies under this framework. A user device who has cached the content can offer the content sharing service to other user devices through device-to-device communication. In addition, we prove that optimizing the transmission performance of CMU is an NP-hard problem. We provide a solution to solve this problem and describe how to calculate the number of distributed caching nodes under different parameters, including time, energy and storage. Finally, we evaluate CMU through a numerical analysis. Experiment results show that content caching with user devices could reduce the requests to Cloud and MESs, and decrease the content delivery time as well.

Content Centric Mobile Ad-hoc NETwork (CCMANET) applies the advantages of Content Centric networking (CCN) into Mobile Ad Hoc Network(MANET) to overcome the drawbacks of low efficiency and unstable in transmission. Caching scheme is one of the key components of CCMANET. However, the caching scheme in CCMANET has not been well explored. In this paper, a novel cooperative caching scheme based on generalized dominating set and local content popularity for CCMANET is proposed. First, a virtual backbone in CCMANET is constructed by generalized dominating set to make the arbitrary topology become two-level hierarchy and a collaborative cache placement scheme between the two hierarchies is designed. Second, we propose a method of computing the local content popularity and a cache replacement scheme based on the local content popularity. Simulation results show that the proposed caching scheme can effectively reduces both path stretch value and server load, and improves the hit ratio compared with the existing caching schemes.

Information-Centric Networking (ICN) is widely accepted as the network architecture for the next generation Internet. Caching strategy is one of the most important issues of ICN and has received much attention in recent years. In this paper we focus on caching strategy for Scalable Video Coding (SVC) in ICN. We first develop the mathematical models of user behavior, video popularity, user experience and caching efficiency. Then we propose a new caching strategy named PoProb, which determines the caching probability of a video according to its popularity. Simulation of PoProb is conducted using AMuSt simulator based on the ndnSIM simulation platform. Simulation results show that compared with existing caching strategies, PoProb can improve user experience and cache efficiency to a certain extent.

Support vector machines (SVMs) can solve structured multioutput learning problems such as multilabel classification, multiclass classification, and vector regression. SVM training is expensive, especially for large and high-dimensional data sets. The bottleneck of the SVM training often lies in the kernel value computation. In many real-world problems, the same kernel values are used in many iterations during the training, which makes the caching of kernel values potentially useful. The majority of the existing studies simply adopt the least recently used (LRU) replacement strategy for caching kernel values. However, as we analyze in this article, the LRU strategy generally achieves high hit ratio near the final stage of the training but does not work well in the whole training process. Therefore, we propose a new caching strategy called EFU (less frequently used), which replaces the EFU kernel values that enhance least frequently used (LFU). Our experimental results show that EFU often has 20% higher hit ratio than LRU in the training with the Gaussian kernel. To further optimize the strategy, we propose a caching strategy called hybrid caching for the SVM training (HCST), which has a novel mechanism to automatically adapt the better caching strategy in different stages of the training. We have integrated the caching strategy into ThunderSVM, a recent SVM library on many-core processors. Our experiments show that HCST adaptively achieves high hit ratios with little runtime overhead among different problems including multilabel classification, multiclass classification, and regression problems. Compared with other existing caching strategies, HCST achieves 20% more reduction in training time on average.

With small cell base stations (SBSs) densely deployed in addition to\nconventional macro base stations (MBSs), the heterogeneous cellular network\n(HCN) architecture can effectively boost network capacity. To support the huge\npower demand of HCNs, renewable energy harvesting technologies can be\nleveraged. In this paper, we aim to make efficient use of the harvested energy\nfor on-grid power saving while satisfying the quality of service (QoS)\nrequirement. To this end, energy-aware traffic offloading schemes are proposed,\nwhereby user associations, ON-OFF states of SBSs, and power control are jointly\noptimized according to the statistical information of energy arrival and\ntraffic load. Specifically, for the single SBS case, the power saving gain\nachieved by activating the SBS is derived in closed form, based on which the\nSBS activation condition and optimal traffic offloading amount are obtained.\nFurthermore, a two-stage energy-aware traffic offloading (TEATO) scheme is\nproposed for the multiple-SBS case, considering various operating\ncharacteristics of SBSs with different power sources. Simulation results\ndemonstrate that the proposed scheme can achieve more than 50% power saving\ngain for typical daily traffic and solar energy profiles, compared with the\nconventional traffic offloading schemes.\n