Caching Techniques in Next Generation Cellular Networks

Content caching will be an essential feature in the next generations of cellular networks. Indeed, a network equipped with caching capabilities allows users to retrieve content with reduced access delays and consequently reduces the traffic passing through the network backhaul. However, the deployment of the caching nodes in the network is hindered by the following two challenges. First, the storage space of a cache is limited as well as expensive. So, it is not possible to store in the cache every content that can be possibly requested by the user. This calls for efficient techniques to determine the contents that must be stored in the cache. Second, efficient ways are needed to implement and control the caching node. In this thesis, we investigate caching techniques focussing to address the above-mentioned challenges, so that the overall system performance is increased. In order to tackle the challenge of the limited storage capacity, smart proactive caching strategies are needed. In the context of vehicular users served by edge nodes, we believe a caching strategy should be adapted to the mobility characteristics of the cars. In this regard, we propose a scheme called RICH (RoadsIde CacHe), which optimally caches content at the edge nodes where connected vehicles require it most. In particular, our scheme is designed to ensure in-order delivery of content chunks to end users. Unlike blind popularity decisions, the probabilistic caching used by RICH considers vehicular trajectory predictions as well as content service time by edge nodes. We evaluate our approach on realistic mobility datasets against a popularity-based edge approach called POP, and a mobility-aware caching strategy known as netPredict. In terms of content availability, our RICH edge caching scheme provides an enhancement of up to 33% and 190% when compared with netPredict and POP respectively. At the same time, the backhaul penalty bandwidth is reduced by a factor ranging between 57% and 70%. Caching node is an also a key component in Named Data Networking (NDN) that is an innovative paradigm to provide content based services in future networks. As compared to legacy networks, naming of network packets and in-network caching of content make NDN more feasible for content dissemination. However, the implementation of NDN requires drastic changes to the existing network infrastructure. One feasible approach is to use Software Defined Networking (SDN), according to which the control of the network is delegated to a centralized controller, which configures the forwarding data plane. This approach leads to large signaling overhead as well as large end-to-end (e2e) delays. In order to overcome these issues, in this work, we provide an efficient way to implement and control the NDN node. We propose to enable NDN using a stateful data plane in the SDN network. In particular, we realize the functionality of an NDN node using a stateful SDN switch attached with a local cache for content storage, and use OpenState to implement such an approach. In our solution, no involvement of the controller is required once the OpenState switch has been configured. We benchmark the performance of our solution against the traditional SDN approach considering several relevant metrics. Experimental results highlight the benefits of a stateful approach and of our implementation, which avoids signaling overhead and significantly reduces e2e delays

Quality of experience-centric management of adaptive video streaming services : status and challenges

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years

Evaluation of HTTP/DASH Adaptation Algorithms on Vehicular Networks

Video streaming currently accounts for the majority of Internet traffic. One factor that enables video streaming is HTTP Adaptive Streaming (HAS), that allows the users to stream video using a bit rate that closely matches the available bandwidth from the server to the client. MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a widely used standard, that allows the clients to select the resolution to download based on their own estimations. The algorithm for determining the next segment in a DASH stream is not partof the standard, but it is an important factor in the resulting playback quality. Nowadays vehicles are increasingly equipped with mobile communication devices, and in-vehicle multimedia entertainment systems. In this paper, we evaluate the performance of various DASH adaptation algorithms over a vehicular network. We present detailed simulation results highlighting the advantages and disadvantages of various adaptation algorithms in delivering video content to vehicular users, and we show how the different adaptation algorithms perform in terms of throughput, playback interruption time, and number of interruptions

Life-cycle management and placement of service function chains in MEC-enabled 5G networks

Recent advancements in mobile communication technology have led to the fifth generation of mobile cellular networks (5G), driven by the proliferation in data traffic demand, stringent latency requirements, and the desire for a fully connected world. This transformation calls for novel technology solutions such as Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) to satisfy service requirements while providing dynamic and instant service deployment. MEC and NFV are two principal and complementary enablers for 5G networks whose co-existence can lead to numerous benefits. Despite the numerous advantages MEC offers, physical resources at the edge are extremely scarce and require efficient utilization. In this doctoral dissertation, we first attempt to optimize resource utilization at the network edge for the scenario of live video streaming. We specifically utilize the real-time Radio Access Network (RAN) information available at the MEC servers to develop a machine learning-based prediction solution and anticipate user requests. Consequently, Integer Linear Programming (ILP) models are used to prefetch/cache video contents from a centralized video server. Regarding the advantages of NFV technology for the deployment of NFs, the second problem that this dissertation address is the proper association of the users to the gNBs along with efficient placement of SFCs on the substrate network. Our primary purpose is to find a proper embedding of the SFC in a hierarchical 5G network. The problem is formulated as a Mixed Integer Linear Programming (MILP) model, having the objective to minimize service provisioning cost, link utilization, and the effect of VNF migration on users' perceived quality of experience. After rigorously analyzing the proposed SFC placement and considering mobile networks' dynamicity, our next goal is to develop an ILP-based model that minimizes the resource provisioning cost by dynamically embed and scale SFCs so that provisioning cost is minimized while user requirements are met

Mobility-Aware Edge Caching for Connected Cars

Content caching on the edge of 5G networks is an emerging and critical feature to support the thirst for content of future connected cars. Yet, the compactization of 5G cells, the finite edge storage capacity and the need for content availability while driving motivate the need to develop smart edge caching strategies adapted to the mobility characteristics of connected cars. In this paper, we propose a Mobility-Aware Probabilistic (MAP) scheme, which optimally caches content at edge nodes where connected vehicles mostly require it. Unlike blind popularity decisions, the probabilistic caching used by MAP considers vehicular trajectory predictions as well as content service time by edge nodes. We evaluate our approach on realistic mobility datasets and against popularity-based edge approaches. Our MAP edge caching scheme provides up to 40% enhanced content availability, 70% increased cache throughput, and 40% reduced backhaul overhead compared to popularity-based strategies