Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an @math filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.
Mobile user's capacity is greatly augmented in the era of MEC. As a result, mobile service provisioning is expected to have further improved quality of experience (QoE) @cite_21 . To this end, various mobile edge architectures have been proposed. Tandom proposed to deploy edge resources within radio access networks. They characterized the relationship between latency and caching size, as well as latency and fronthaul capacity, from an information-theoretic perspective @cite_12 . Yang introduced an edge resource provisioning architecture based on cloud radio access network (C-RAN), and devised a cloud-edge interoperation scheme via software defined networking techniques @cite_16 . Tong designed a hierarchical edge architecture, aiming at making efficient use of edge resources when serving the peak loads from mobile users @cite_1 . As the 5G wireless network is expected to incorporate diverse access technologies, in this paper, we consider edge caching in the context of heterogeneous networks. Potential EN deployment can be capacity-augmented base stations, WiFi access points and other devices with excess resources.
@cite_21 @cite_1 @cite_12 @cite_16
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Edge caching and the centralization of baseband processing by means of the C-RAN architecture are among the most promising and transformative trends in the evolution of wireless networks. A key advantage of C-RAN is the possibility to perform cooperative transmission across multiple edge nodes, such as small cell base stations, thanks to centralized cloud processing. Cloud processing, however, comes at the cost of the potentially large delay entailed by fronthaul transmission between edge and cloud. In contrast, edge caching enables the low-latency transmission of popular multimedia content, but at the cost of constraining the operation of the edge nodes to decentralized transmission strategies with limited interference management capabilities. In order to accommodate the broad range of quality of service requirements of mobile broadband communication, in terms of spectral efficiency and latency, that are envisioned to be within the scope of 5G systems and beyond, this article considers a hybrid architecture, referred to as fog RAN (F-RAN), that harnesses the benefits of, and the synergies between, edge caching and C-RAN. In an F-RAN, edge nodes may be endowed with caching capabilities, while at the same time being controllable from a central cloud processor as in a C-RAN. In this article, an information-theoretic framework is presented that aims to characterize the main trade-offs between performance of an F-RAN, in terms of worst case delivery latency, and its resources: caching and fronthaul capacities. Mobile Edge Computing (MEC) plays a key role in the 5G roadmap, as a way to bring information technology (IT) services closer to the mobile users by empowering radio access points with additional functionalities, like caching or computation offloading capabilities. At the physical layer, some of the key technologies enabling very low latency mobile services are dense deployment of radio access points, massive MIMO and millimeter-wave (mmW) communications for radio access as well as for radio fronthaul backhaul. In this paper, we merge computation offloading techniques for mobile edge computing with mmW communications and show how the joint optimization of computation communication resources is crucial to design an energy efficient mobile edge computing system. In particular, we tackle the intermittency of mmW links by considering multi-link communications and show how to take advantage of preliminary estimation of blocking probabilities. Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges. The performance of mobile computing would be significantly improved by leveraging cloud computing and migrating mobile workloads for remote execution at the cloud. In this paper, to efficiently handle the peak load and satisfy the requirements of remote program execution, we propose to deploy cloud servers at the network edge and design the edge cloud as a tree hierarchy of geo-distributed servers, so as to efficiently utilize the cloud resources to serve the peak loads from mobile users. The hierarchical architecture of edge cloud enables aggregation of the peak loads across different tiers of cloud servers to maximize the amount of mobile workloads being served. To ensure efficient utilization of cloud resources, we further propose a workload placement algorithm that decides which edge cloud servers mobile programs are placed on and how much computational capacity is provisioned to execute each program. The performance of our proposed hierarchical edge cloud architecture on serving mobile workloads is evaluated by formal analysis, small-scale system experimentation, and large-scale trace-based simulations. Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios. In this paper, we present a Mobile Edge Computing (MEC) scheme for enabling network edge-assisted video adaptation based on MPEG-DASH (Dynamic Adaptive Streaming over HTTP). In contrast to the traditional over-the-top (OTT) adaptation performed by DASH clients, the MEC server at the mobile network edge can capture radio access network (RAN) conditions through its intrinsic Radio Network Information Service (RNIS) function, and use the knowledge to provide guidance to clients so that they can perform more intelligent video adaptation. In order to support such MEC-assisted DASH video adaptation, the MEC server needs to locally cache the most popular content segments at the qualities that can be supported by the current network throughput. Towards this end, we introduce a two-dimensional user Quality-of-Experience (QoE)-driven algorithm for making caching replacement decisions based on both content context (e.g., segment popularity) and network context (e.g., RAN downlink throughput). We conducted experiments by deploying a prototype MEC server at a real LTE-A based network testbed. The results show that our QoE-driven algorithm is able to achieve significant improvement on user QoE over 2 benchmark schemes. As a promising paradigm for the fifth generation wireless communication (5G) system, the fog radio access network (F-RAN) has been proposed as an advanced socially aware mobile networking architecture to provide high spectral efficiency (SE) while maintaining high energy efficiency (EE) and low latency. Recent advents are advocated to the performance analysis and radio resource allocation, both of which are fundamental issues to make F-RANs successfully rollout. This paper comprehensively summarizes the recent advances of the performance analysis and radio resource allocation in F-RANs. In particular, the advanced edge cache and adaptive model selection schemes are presented to improve SE and EE under maintaining a low latency level. The radio resource allocation strategies to optimize SE and EE in F-RANs are, respectively, proposed. A few open issues in terms of the F-RAN-based 5G architecture and the social-awareness technique are identified as well. With the fast development of Internet of Things (IoT), the fifth generation (5G) wireless networks need to provide massive connectivity of IoT devices and meet the demand for low latency. To satisfy these requirements, nonorthogonal multiple access (NOMA) has been recognized as a promising solution for 5G networks to significantly improve the network capacity. In parallel with the development of NOMA techniques, mobile edge computing (MEC) is becoming one of the key emerging technologies to reduce the latency and improve the quality of service (QoS) for 5G networks. In order to capture the potential gains of NOMA in the context of MEC, this paper proposes an edge computing aware NOMA technique which can enjoy the benefits of uplink NOMA in reducing MEC users’ uplink energy consumption. To this end, we formulate an NOMA-based optimization framework which minimizes the energy consumption of MEC users via optimizing the user clustering, computing and communication resource allocation, and transmit powers. In particular, similar to frequency resource blocks (RBs), we divide the computing capacity available at the cloudlet to computing RBs. Accordingly, we explore the joint allocation of the frequency and computing RBs to the users that are assigned to different order indices within the NOMA clusters. We also design an efficient heuristic algorithm for user clustering and RBs allocation, and formulate a convex optimization problem for the power control to be solved independently per NOMA cluster. The performance of the proposed NOMA scheme is evaluated via simulations. Mobile Edge Computing (MEC) is an emerging technology that aims at pushing applications and content close to the users (e.g. at base stations, access points, aggregation networks) to reduce latency, improve quality of experience, and ensure highly efficient network operation and service delivery It principally relies on virtualization-enabled MEC servers with limited capacity at the edge of the network. One key issue is to dimension such systems in terms of server size, server number and server operation area to meet MEC goals. In this paper, we propose a graph-based algorithm that, taking into account a maximum MEC server capacity, provides a partition of MEC clusters, which consolidates as many communications as possible at the edge. We use a dataset of mobile communications to evaluate it with real world spatio-temporal human dynamics. In addition to quantifying macroscopic MEC benefits, the evaluation shows that our algorithm provides MEC area partitions that largely offload the core, thus pushing the load at the edge (e.g., with 10 small MEC servers around 55 of the traffic stay at the edge), and that are well balanced through time. Mobile edge computing (MEC) is an emerging technology that aims at pushing applications and content close to the users (e.g., at base stations, access points, and aggregation networks) to reduce latency, improve quality of experience, and ensure highly efficient network operation and service delivery. It principally relies on virtualization-enabled MEC servers with limited capacity at the edge of the network. One key issue is to dimension such systems in terms of server size, server number, and server operation area to meet MEC goals. In this paper, we formulate this problem as a mixed integer linear program. We then propose a graph-based algorithm that, taking into account a maximum MEC server capacity, provides a partition of MEC clusters, which consolidates as many communications as possible at the edge. We use a dataset of mobile communications to extensively evaluate them with real world spatio-temporal human dynamics. In addition to quantifying macroscopic MEC benefits, the evaluation shows that our algorithm provides MEC area partitions that largely offload the core, thus pushing the load at the edge (e.g., with 10 small MEC servers between 55 and 64 of the traffic stay at the edge), and that are well balanced through time. One of the key features of 5G roadmap is mobile edge computing (MEC), as an effective way to bring information technology (IT) services close to the mobile user. Moving computation and caching resources at the edge of the access network enables low latency and high reliability services, as required in many of the verticals associated to 5G, such as Industry 4.0 or automated driving. Merging MEC with millimeter wave (mmW) communications provides a further thrust to enable low latency and high reliability services, thanks to the high data rate of mmW links and the ability to handle interference through massive beamforming. However, mmW links are prone to blocking events, which could limit the effectiveness of the mmW-MEC deployment. To overcome blocking events and robustify mmW-MEC, in this paper we propose and analyze the performance of two strategies to overcome the effect of blocking: i) overbooking of computation and communication resources, based on the statistics of blocking events, and ii) adopting multi-link communications. Abstract With the rapid increase in the development of the Internet of Things and 5G networks in the smart city context, a large amount of data (i.e., big data) is expected to be generated, resulting in increased latency for the traditional cloud computing paradigm. To reduce the latency, mobile edge computing has been considered for offloading a part of the workload from mobile devices to nearby edge servers that have sufficient computation resources. Although there has been significant research in the field of mobile edge computing, little attention has been given to understanding the placement of edge servers in smart cities to optimize the mobile edge computing network performance. In this paper, we study the edge server placement problem in mobile edge computing environments for smart cities. First, we formulate the problem as a multi-objective constraint optimization problem that places edge servers in some strategic locations with the objective to make balance the workloads of edge servers and minimize the access delay between the mobile user and edge server. Then, we adopt mixed integer programming to find the optimal solution. Experimental results based on Shanghai Telecom’s base station dataset show that our approach outperforms several representative approaches in terms of access delay and workload balancing. The demand for rich multimedia services over mobile networks has been soaring at a tremendous pace over recent years. However, due to the centralized architecture of current cellular networks, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic. Recently, we have observed the emergence of promising mobile content caching and delivery techniques, by which popular contents are cached in the intermediate servers (or middleboxes, gateways, or routers) so that demands from users for the same content can be accommodated easily without duplicate transmissions from remote servers; hence, redundant traffic can be significantly eliminated. In this article, we first study techniques related to caching in current mobile networks, and discuss potential techniques for caching in 5G mobile networks, including evolved packet core network caching and radio access network caching. A novel edge caching scheme based on the concept of content-centric networking or information-centric networking is proposed. Using trace-driven simulations, we evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks. Furthermore, we conclude the article by exploring new relevant opportunities and challenges. With the rapid development of Mobile Internet, online Video-on-Demand (VoD) services, primarily 4K video, grow tremendously with the key performance indicators of lower latency, higher bandwidth, and higher bitrate. However, due to the long-distance between the user equipment (UE) and Internet Service Provider, Quality-of-Service (QoS) in terms of low playback delay and high transmission rate cannot be guaranteed. Therefore, Mobile Edge Computing (MEC), at the edge of the cellular network, is highly recommended with the benefits of lower uncertainty and end-to-end latency. The UE can enjoy better customized services with more appropriate bitrates as a result. In this paper, we propose a practical framework of MEC-enabled cellular network with radio network-aware edge cache and a radio network-aware cache updating algorithm. The framework uses Dynamic Adaptive Streaming over HTTP (DASH) and Radio Network Information Service (RNIS) is leveraged to accelerate multi-media services. Under this framework, RNIS collects information and delivers it to the MEC server based on the context. A testbed based on a real 4G Long Term Evolution (LTE) Base Station is developed carrying out experiments under this framework. Compared to traditional networks, the result shows that our approach maintains a smooth high quality of experience (QoE). The cloud radio access network (C-RAN) is a promising network architecture for future mobile communications, and one practical hurdle for its large scale implementation is the stringent requirement of high capacity and low latency fronthaul connecting the distributed remote radio heads (RRH) to the centralized baseband pools (BBUs) in the C-RAN. To improve the scalability of C-RAN networks, it is very important to take the fronthaul loading into consideration in the signal detection, and it is very desirable to reduce the fronthaul loading in C-RAN systems. In this paper, we consider uplink C-RAN systems and we propose a distributed fronthaul compression scheme at the distributed RRHs and a joint recovery algorithm at the BBUs by deploying the techniques of distributed compressive sensing (CS). Different from conventional distributed CS, the CS problem in C-RAN system needs to incorporate the underlying effect of multi-access fading for the end-to-end recovery of the transmitted signals from the users. We analyze the performance of the proposed end-to-end signal recovery algorithm and we show that the aggregate measurement matrix in C-RAN systems, which contains both the distributed fronthaul compression and multiaccess fading, can still satisfy the restricted isometry property with high probability. Based on these results, we derive tradeoff results between the uplink capacity and the fronthaul loading in C-RAN systems. As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures, which bring network functions and contents to the network edge, are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks, including definition, architecture, and advantages. Next, a comprehensive survey of issues on computing, caching, and communication techniques at the network edge is presented. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks, such as cloud technology, SDN NFV, and smart devices are discussed. Finally, open research challenges and future directions are presented as well. Mobile edge computing (MEC) literally pushes cloud computing from remote datacenters to the life radius of end users. By leveraging the widely adopted ETSI network function virtualization (NFV) architecture, MEC provisions elastic and resilient mobile edge applications with proximity. Typical MEC virtualization infrastructure allows configurable placement policy to deploy mobile edge applications as virtual machines (VMs): affinity can be used to put VMs on the same host for inter-VM networking performance, while anti-affinity is to separate VMs for high availability. In this paper, we propose a novel model to track the availability and cost impact from placement policy changes of the mobile edge applications. We formulate our model as a stochastic programming problem. To minimize complexity challenge, we also propose a heuristic algorithm. With our model, the unit resource cost increases when there are less resources left on a host. Applying affinity would take up more resources of the host but saves network bandwidth cost because of co-location. When enforcing anti-affinity, experimental results show increases of both availability and inter-host network bandwidth cost. For applications with different resource requirements, our model is able to find their sweet points with the consideration of both resource cost and application availability, which is vital in a less robust MEC cloud environment. Ultra-high reliable communication and improved capacity are some of the major requirements of the 5th generation (5G) mobile and wireless networks. Achieving the aforementioned requirements necessitates avoiding radio link failures and the service interruption that occurs during the failures and their re-establishment procedures. Moreover, the latency associated with packet forwarding in classical handover procedures should be resolved. This paper proposes a multi-connectivity concept for a cloud radio access network as a solution for mobility related link failures and throughput degradation of cell-edge users. The concept relies on the fact that the transmissions from co-operating cells are co-ordinated for both data and control signals. Latency incurred due to classical handover procedures will be inherently resolved in the proposed multi-connectivity scheme. Simulation results are shown for a stand alone ultra dense small cells that use the same carrier frequency. It is shown that the number of mobility failures can considerably be decreased without a loss in the throughput performance gain of cell-edge users. As a crucial step moving towards the next generation of super-fast wireless networks, recently the fifth-generation (5G) mobile wireless networks have received a plethora of research attention and efforts from both the academia and industry. The 5G mobile wireless networks are expected to provision distinct delay-bounded quality of service (QoS) guarantees for a wide range of multimedia services, applications, and users with extremely diverse requirements. However, how to efficiently support multimedia services over 5G wireless networks has imposed many new challenging issues not encountered before in the fourth-generation wireless networks. To overcome these new challenges, we propose a novel network-function virtualization and mobile-traffic offloading based software-defined network (SDN) architecture for heterogeneous statistical QoS provisioning over 5G multimedia mobile wireless networks. Specifically, we develop the novel SDN architecture to scalably virtualize wireless resources and physical infrastructures, based on user’s locations and requests, into three types of virtual wireless networks: virtual networks without offloading, virtual networks with WiFi offloading, and virtual networks with device-to-device offloading. We derive the optimal transmit power allocation schemes to maximize the aggregate effective capacity, overall spectrum efficiency, and other related performances for these three types of virtual wireless networks. We also derive the scalability improvements of our proposed three integrated virtual networks. Finally, we validate and evaluate our developed schemes through numerical analyses, showing significant performance improvements as compared with other existing schemes. We present an Edge-as-a-Service (EaaS) platform for realising distributed cloud architectures and integrating the edge of the network in the computing ecosystem. The EaaS platform is underpinned by (i) a lightweight discovery protocol that identifies edge nodes and make them publicly accessible in a computing environment, and (ii) a scalable resource provisioning mechanism for offloading workloads from the cloud on to the edge for servicing multiple user requests. We validate the feasibility of EaaS on an online game use-case to highlight the improvement in the QoS of the application hosted on our cloud-edge platform. On this platform we demonstrate (i) low overheads of less than 6 , (ii) reduced data traffic to the cloud by up to 95 and (iii) minimised application latency between 40 -60 .