Caching In Networks Research Articles

On the Performance of UAV-Aided Content Caching in Small-Cell Networks with Joint Transmission

Unmanned aerial vehicles (UAVs), featured by the high-mobility and high-quality propagation environment, have shown great potential in wireless communication applications. In this paper, a novel UAV-aided small-cell content caching network is proposed and analyzed, where joint transmission (JT) is considered in the dense small-cell networks and mobile UAVs are employed to shorten the serving distance. The system performance is evaluated in terms of the average cache hit probability and the ergodic transmission rate. From the analytical results, we find that (i) the proposed UAV-aided small-cell network shows superior caching performance and, even with a small density of UAVs the system’s cache hit probability, can be improved significantly; (ii) the content’s optimal caching probability to maximize the cache hit probability is proportional to the (K+1)-th root of its request probability, where K is the number of small-cell base stations that serve each user by JT; (iii) caching the most popular content in UAVs may lead to a low transmission rate due to the limited resource offered by the low-density UAVs. Simulation results are presented to validate the theoretical results and the performance gain achieved by the optimal caching strategy.

Two-Layer Network Caching for Different Service Requirements

Network caching is a technique used to speed-up user access to frequently requested contents in complex data networks. This paper presents a two-layer overlay network caching system for content distribution. It is used to define some caching scenarios with increasing complexity, which refers to real situations, including mobile 5G connectivity. For each scenario our aim is to maximize the hit ratio, which leads to the formulation of NP-complete optimization problems. The heuristic solutions proposed are based on the theory of the maximization of monotone submodular functions under matroid constraints. After the determination of the approximation ratio of the greedy heuristic algorithms proposed, a numerical performance analysis is shown. This analysis includes a comparison with the Least-Frequently Used (LFU) eviction strategy adapted to the analyzed systems. Results show very good performance, under the hypotheses of either known or unknown popularity of contents.

Joint Optimization of Cooperative Edge Caching and Radio Resource Allocation in 5G-Enabled Massive IoT Networks

The fifth-generation of wireless communication (5G) is a promising paradigm toward massive interconnectivity within Internet-of-Things (IoT) networks. However, because the data traffic throughput sharply increases with the number of IoT devices, a tremendous burden on the backhaul links and core networks results. With this in mind, mobile edge caching is an effective method that can relieve stress of the backhaul links, while decreasing the service latency. The purpose of this study is to analyze the problem of jointly optimizing cooperative edge caching and radio resource allocation in 5G-enabled massive IoT networks. For that, a joint optimization long-term nonlinear integer programming problem is posed. This class of problems is known to be NP-hard; thus, to reduce the problem complexity, a divide and conquer scheme will be applied—the task at hand will be divided into two subproblems: 1) cooperative edge caching and 2) radio resource allocation. The cooperative edge caching subproblem is formulated as a constrained Markov decision process. Herein, a deep reinforcement learning method to optimize the caching decisions for all the edge nodes. Then, based on the resulting optimal caching decisions, the radio resource allocation subproblem for each edge node is posed as an NLIP problem, and an improved branch-and-bound method is proposed to yield the optimal radio resource allocation decisions for each edge node. Extensive simulations were performed to confirm that the proposed methods have the capability of enhancing the content caching hit ratio, while lessening the content retrieving delays for 5G-enabled massive IoT networks—improving over various baseline algorithms.

Cooperative content caching and power allocation strategy for fog radio access networks

Fog radio access networks (F-RANs) architecture is regarded as a prominent solution to deploy the caching and computing functions at the edge nodes of the network. However, the ever-increasing data traffic demand and the latency requirements of emerging applications pose new challenges to F-RANs. To minimize content fetching latency, we propose a cooperative content caching and power allocation scheme, which proactively caches the content at the network edge and enables users to dynamically obtain the desired content either from Fog-Access points (F-APs) or proximate user equipments (UEs) through Device-to-Device (D2D) communication. Furthermore, the proposed scheme allocates appropriate transmit power to D2D user equipments (DUEs) so that the transmission rate can be maximized. Specifically, the cooperative content caching issue is initially formulated as a probability-triggered combinatorial multi-armed bandit (CMAB) framework. By considering the user preference and content popularity prediction, an enhanced multi-agent reinforcement learning algorithm is proposed to obtain an optimal caching strategy. Besides, to minimize the content fetching latency and guarantee that each UE can retrieve the desired content, the power allocation problem is then modeled as maximizing the sum data rates of users. Finally, a Q-learning based power allocation strategy is derived. The simulation results based on the dataset from MovieLens reveal that compared with baseline methods, our proposed cooperative caching and power allocation scheme can not only reduce the content fetching latency but also increase the cache hit rate.

A UAV-Enabled Data Dissemination Protocol With Proactive Caching and File Sharing in V2X Networks

In Vehicle-to-Everything (V2X) networks, where all vehicles and infrastructures are interconnected for information sharing, data dissemination is increasingly playing a significant role in superior and pluralistic communication services. To empower the efficiency of data dissemination, in this paper, we propose a novel unmanned aerial vehicle (UAV)-enabled scheduling protocol consisting of a proactive caching policy and a file sharing strategy in V2X networks. In the proactive caching process, we deploy UAVs as flying base stations (BSs) with caching capability, where we propose a UAV dynamic trajectory scheduling (DTS) algorithm to optimize the caching duration. Whereas in the file sharing strategy, based on the previous vehicular caching status, we provide a framework of file sharing cycle for data dissemination scheduling and employ a channel prediction algorithm to alleviate communication overhead. Moreover, we propose a relay ordering algorithm to effectively improve the file sharing process. Simulation results demonstrate that, our proposed scheduling protocol can enhance the efficiency of data dissemination and achieve an improved network performance in terms of caching process, system throughput, and file sharing latency in V2X networks.

Cross-Layer Resource Allocation for UAV-Assisted Wireless Caching Networks With NOMA

Unmanned aerial vehicle (UAV) assisted wireless caching networks (WCN) have been recognized as a promising way to reduce the network load and improve the energy efficiency in the sixth generation (6 G) communication systems. Aiming to improve spectrum efficiency and system capacity, we apply non-orthogonal multiple access (NOMA) in UAV-assisted WCN to serve multiple users on the same spectrum simultaneously and propose the cross-layer resource allocation strategy including the scheduling of UAVs, the grouping of users, and the allocation of power. First, the ρ-K-means algorithm is proposed to assign users to multiple clusters and deploy UAVs according to the distance from UAVs to the base station in the UAV deployment layer. Then, the base station broadcasts the popular files to UAVs via NOMA in the content placement layer. Based on the existing fixed power allocation strategy, we propose a statistic quality of service (QoS) based fixed (SQF) power allocation method to take the statistic QoS of the popular files into consideration and improve the energy efficiency through introducing the discount factor. On the basis of SQF, an instantaneous QoS based adaptive (IQA) strategy allocates power according to the instantaneous QoS of the popular files to reduce the file outage probability. Furthermore, we propose an improved method that is a cross-layer based optimal (CLO) power allocation strategy to maximize the system hit probability. Finally, in the content delivery layer, users in each cluster are grouped according to the channel gain from users to UAVs. In addition, each UAV serves two users on the same time-frequency resource block based on the cognitive radio inspired power allocation for the NOMA user pairs. Simulation results confirm that the proposed ρ-K-means algorithm and CLO strategy reduce the file outage probability and improve the hit probability.

Fair Caching Networks

We study fair content allocation strategies in caching networks through a utility-driven framework, where each request achieves a utility of its caching gain rate. The resulting problem is NP-hard. Submodularity allows us to devise a deterministic allocation strategy with an optimality guarantee factor arbitrarily close to 1-1/e. When 0 < α ≤ 1, we further propose a randomized strategy that attains an improved optimality guarantee, (1 - 1/e)1-α, in expectation. Through extensive simulations over synthetic and real-world network topologies, we evaluate the performance of our proposed strategies and discuss the effect of fairness.

Jointly Optimizing Throughput and Content Delivery Cost Over Lossy Cache Networks

Cache optimization, i.e., determining the optimal content placement and routing paths, is essential for obtaining high performance of cache-enabled networks. This paper studies the problem of optimizing system throughput and content delivery cost over cache networks with lossy links (i.e., ICN-based wireless IoT systems), where content is divided into packet-level chunks, and packets may be lost in transmission. We first propose a new performance metric - the expected overall content routing cost for satisfied requests (RCS), for better characterizing content delivery cost under packet losses. RCS at the same time possesses the attractive mathematical property of super-modularity. We then formulate an optimization problem for the task through jointly optimizing content caching and request routing, and analyze it under fixed-routing scenario. The formulated problem is NP-hard and we prove it is reducible to the one of minimizing content routing cost without packet losses. We establish rules for the reduction, and leverage existing efficient algorithm to solve the problem. We also propose a potential-based online algorithm that is simple and adaptive to traffic changes and packet losses. The effectiveness of our mechanism is validated through extensive simulations over a wide array of network topologies.

Rendering-Aware VR Video Caching Over Multi-Cell MEC Networks

Delivering high fidelity virtual reality (VR) video over mobile networks is very challenging since VR applications usually require very high bandwidth and ultra low latency. With the evolution of 5G mobile networks, multi-cell multi-access edge computing (MEC) networks enable low latency data communication. However, even in this setting, the requirements of VR applications are tough to meet. To optimize the end-to-end latency for VR video delivery over multi-cell MEC networks, we propose a rendering-aware tile caching scheme. As a first step we propose collaborative tile caching in 5G MEC networks by enabling multiple cell sites to share caches so that the network caching performance is improved. Hence, the amount of redundant data delivered and eventually the latency are both significantly reduced. Second, our scheme offloads viewport rendering of VR video to the MEC server and closely couples this with cache placement, allowing thus the rendering-induced latency to be reduced. Finally, a low-delay request routing algorithm is integrated with the proposed cache placement scheme to further optimize the end-to-end latency of VR video delivery. Extensive simulation results show that the proposed rendering-aware caching scheme can achieve better latency performance than the state-of-the-art decoupled caching/rendering schemes.

Power allocation for video segment based caching strategy in F-RAN architecture

The concept of edge network caching has been proposed to alleviate the excessive pressure on the core networks. Furthermore, video segment caching technology, a method to cut the whole video into segments and cache them separately, has brought a novel idea to solve the caching problem in the smaller space for massive data. The adoption of segment caching in edge networks will divide the simple video transmission process into two coupling stages because of separate data caching, which leads to more complicated resource allocation. In this paper, this problem is discussed, and its mathematical model is established to minimize the energy consumption of video transmissions. By introducing an efficient prediction window of channel fading, an optimal dynamic scheduling algorithm based on Q-learning is proposed to minimize power consumption while ensuring smooth video streaming. The proposed Q-learning algorithm is simulated and the impacts of channel state, target video bit rate and large-scale channel parameter are evaluated. Simulation results show that the proposed method can effectively reduce the total power consumption while ensuring the smooth playback of video service, thanks to the fact that the proposed method is intelligent which can effectively utilize idle resources in favorable channel states.

Radio Resource Sharing and Edge Caching with Latency Constraint for Local 5G Operator: Geometric Programming Meets Stackelberg Game

The rapidly increasing demand in indoor small cell networks has given rise to the concept of local 5G operator (OP) for local service delivery. In this regard, we develop a novel game-theoretic framework with geometric programming to model and analyze cache-enabled small cell base stations (SBSs) with infrastructure sharing for local 5G OP networks. In such a network, the local 5G OP provides wireless network in indoor area and rent out the infrastructure which are RAN and cache storage to multiple mobile network operators (MNOs) while guarantee the quality-of-experience (QoE) at the users (UEs) of MNOs. We formulate a Stackelberg game model where the local 5G OP is the leader and the MNOs are the followers. The local 5G OP aims to maximize its profit by optimizing its infrastructure rental fee, and the MNOs aim to minimize their renting cost of infrastructure by minimizing the cache intensity subject to latency constraint at each UE. Here, the cache intensity is defined as the product of the number of SBSs per unit area and the number of popular files stored in each SBS. The optimization problems of the local 5G OP and the MNOs are transformed into geometric programming. Accordingly, the subgame perfect equilibrium of Stackelberg game is obtained through the succesive geometric programming (SGP) method. Since the MNOs share their rented infrastructure, for cost sharing, we apply the concept of Shapley value to divide the cost among the MNOs. We show that the cost sharing problem can be mapped into a simplified “airport runway cost sharing problem”, in which the Shapley value can be computed efficiently. Finally, we present an extensive performance evaluation that reveals interesting insights into designing resource sharing with edge caching in local 5G OP networks.

Selfish Caching Games on Directed Graphs

Caching networks can reduce the routing costs of accessing contents by caching contents closer to users. However, cache nodes may belong to different entities and behave selfishly to maximize their own benefits, which often lead to performance degradation for the overall network. While there has been extensive literature on allocating contents to caches to maximize the social welfare, the analysis of selfish caching behaviors remains largely unexplored. In this paper, we model the selfish behaviors of cache nodes as selfish caching games on arbitrary directed graphs with heterogeneous content popularity. We study the existence of a pure strategy Nash equilibrium (PSNE) in selfish caching games, and analyze its efficiency in terms of social welfare. We show that a PSNE does not always exist in arbitrary-topology caching networks. However, if the network does not have a mixed request loop, i.e., a directed loop in which each edge is traversed by at least one content request, we show that a PSNE always exists and can be found in polynomial time. Furthermore, we can avoid mixed request loops by properly choosing request forwarding paths. We then show that the efficiency of Nash equilibria, captured by the price of anarchy (PoA), can be arbitrarily poor if we allow arbitrary content request patterns, and adding extra cache nodes can make the PoA worse, i.e., cache paradox happens. However, when cache nodes have homogeneous request patterns, we show that the PoA is bounded even allowing arbitrary topologies. We further analyze the selfish caching games for cache nodes with limited computational capabilities, and show that an approximate PSNE exists with bounded PoA in certain cases of interest. Simulation results show that increasing the cache capacity in the network improves the efficiency of Nash equilibria, while adding extra cache nodes can degrade the efficiency of Nash equilibria.

Spatial Concentration of Caching in Wireless Heterogeneous Networks

We propose a decentralized caching policy for wireless heterogeneous networks that makes content placement decisions based on pairwise interactions between cache nodes. We call our proposed scheme γ-exclusion cache placement (GEC), where a parameter γ controls an exclusion radius that discourages nearby caches from storing redundant content. GEC takes into account item popularity and the nodes' caching priorities and leverages negative dependence to relax the classic 0-1 knapsack problem to yield spatially balanced sampling across caches. We show that GEC guarantees a better concentration (reduced variance) of the required cache storage size than the state of the art, and that the cache size constraints can be satisfied with high probability. Given a cache hit probability target, we compare the 95% confidence intervals of the required cache sizes for three caching schemes: (i) independent placement, (ii) hard exclusion caching (HEC), and (iii) the proposed GEC approach. For uniform spatial traffic, we demonstrate that GEC provides approximately a 3x and 2x reduction in required cache size over (i) and (ii), respectively. For non-uniform spatial traffic based on realistic peak-hour variations in urban scenarios, the gains are even greater.

Function Approximation Based Reinforcement Learning for Edge Caching in Massive MIMO Networks

Caching popular contents in advance is an important technique to achieve low latency and reduced backhaul congestion in future wireless communication systems. In this article, a multi-cell massive multi-input-multi-output system is considered, where locations of base stations are distributed as a Poisson point process. Assuming probabilistic caching, average success probability (ASP) of the system is derived for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. Further, modeling CP variations across time as a Markov process, reinforcement Q-learning is employed to learn the optimal content placement strategy to optimize the long-term-discounted ASP and average cache refresh rate. In the Q-learning, the number of Q-updates are large and proportional to the number of states and actions. To reduce the space complexity and update requirements towards scalable Q-learning, two novel (linear and non-linear) function approximations-based Q-learning approaches are proposed, where only a constant (4 and 3 respectively) number of variables need updation, irrespective of the number of states and actions. Convergence of these approximation-based approaches are analyzed. Simulations verify that these approaches converge and successfully learn the similar best content placement, which shows the successful applicability and scalability of the proposed approximated Q-learning schemes.

SO‐VMEC: Service offloading in virtual mobile edge computing using deep reinforcement learning

AbstractService offloading poses interesting challenges in current and next‐generation networks. The classical network optimization algorithms are still painstakingly tune heuristics to get a sufficient solution. Classical approaches use data as input in order to output near‐optimal solutions. These techniques show exponential computational time and deal only with small network scale. Therefore, we are motivated by replacing this tedious process with recent learning techniques to learn the behavior of the classical optimization algorithms while enhancing both the quality of service and satisfying the resources requirements of next‐generation applications. Deep reinforcement learning (DRL) and machine learning (ML) can improve service offloading and network caching. An optimal service offloading in virtual mobile edge computing (SO‐VMEC) use case algorithm is proposed using integer linear programming (ILP). Moreover, a service offloading protocol is presented to support the use case. We leverage software defined networking (SDN) and network function virtualization (NFV) concepts to control and virtualize network components. Then, a DRL‐based offloading is proposed to deal with dense Internet of Things (IoT) networks. Extensive evaluations and comparison to state of the art techniques are carried out. Results show the efficiency of the proposed algorithms in terms of service offloading, resource utilization, and networking.

Quality of Experience in ICN: Keep Your Low- Bitrate Close and High-Bitrate Closer

Recent studies into streaming media delivery suggest that performance gains from ubiquitous caching in Information-Centric Networks (ICN) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieving multimedia content. Bitrate adaptation mechanisms, that drive video streaming, clash with caching mechanisms in ways that affect users' Quality of Experience (QoE). Cache performance also diminishes as consumers dynamically select content encoded at different bitrates. In this article we use this evidence to draw a novel insight: in adaptive streaming over ICN, bitrates should be prioritized alongside popularity and hit rates. We build on this insight to propose RippleCache as a family of cache placement schemes that safeguard high-bitrate content at the edge and push low-bitrate content into the network core. Doing so reduces contention of cache resources, as well as congestion in the network. To validate RippleCache claims we construct two separate implementations. We design RippleClassic as a benchmark solution that optimizes content placement by maximizing a measure for ICNs shown to have high correlation with QoE. In addition, our lighter-weight RippleFinder is then re-designed with distributed execution for application in large-scale systems. RippleCache performance gains are reinforced by evaluations in NS-3 against state-of-the-art baseline approaches, using standard measures of QoE as defined by the DASH Industry Forum. Our results demonstrate that RippleClassic and RippleFinder deliver content that suffers less oscillation and rebuffering, all while achieving the highest levels of video quality; thus indicating overall improvements to QoE.

Probabilistic Caching and Dynamic Delivery Policies for Categorized Contents and Consecutive User Demands

Wireless caching networks have been extensively researched as a promising technique for supporting the massive data traffic of multimedia services. Many of the existing studies on real-data traffic have shown that users of a multimedia service consecutively request multiple contents and this sequence is strongly dependent on the related list of the first content and/or the top referrer in the category. This paper thus introduces the notion of “temporary preference”, characterizing the behavior of users who are highly likely to request the next content from a certain target category (i.e., related content list). Based on this observation, this paper proposes both probabilistic caching and dynamic delivery policies for categorized contents and consecutive user demands. The proposed caching scheme maximizes the minimum of the cache hit rates for all users. In the delivery phase, a dynamic helper association policy for receiving multiple contents in a row is designed to reduce the delivery latency. By comparing with the content placement optimized for one-shot requests, numerical results verify the effects of categorized contents and consecutive user demands on the proposed caching and delivery policies.

On the Fundamental Limits of Fog-RAN Cache-Aided Networks With Downlink and Sidelink Communications

Maddah-Ali and Niesen (MAN) in 2014 showed that coded caching in single bottleneck-link broadcast networks allows serving an arbitrarily large number of cache-equipped users with a total link load (bits per unit time) that does not scale with the number of users. Since then, the general topic of coded caching has generated enormous interest both from the information theoretic and (network) coding theoretic viewpoint, and from the viewpoint of applications. Building on the MAN work, this paper considers a particular network topology referred to as cache-aided Fog Radio Access Network (Fog-RAN), that includes a Macro-cell Base Station (MBS) co-located with the content server, several cache-equipped Small-cell Base Stations (SBSs), and many users without caches. Some users are served directly by the MBS broadcast downlink, while other users are served by the SBSs. The SBSs can also exchange data via rounds of direct communication via a side channel, referred to as “sidelink”. For this novel Fog-RAN model, the fundamental tradeoff among (a) the amount of cache memory at the SBSs, (b) the load on the downlink (from MBS to directly served users and SBSs), and (c) the aggregate load on the sidelink is studied, under the standard worst-case demand scenario. We propose a converse bound whose key novelty is to jointly bound the downlink load an the sidelink load. For the achievability, by leveraging the network topology, we propose two classes of memory-loads point, where the SBS sidelink load is minimum and the MBS downlink load is minimum, respectively. By memory-sharing between these two classes of memory-loads points, some exact or order optimality results are obtained. Several existing models (e.g., Device-to-Device coded caching, single bottleneck-link coded caching with shared caches, single bottleneck-link caching coded caching with cache-less users) are recovered as special cases of this network model and by-product results of independent interest are given. Finally, the role of topology-aware versus topology-agnostic caching is discussed.

Frozen Cache: Mitigating Filter Effect and Redundancy for Network of Caches

Information-Centric Networking (ICN) architecture leverages the network of caches’ idea to bring content closer to consumers to ultimately reduce the load on content servers and prevent unnecessary packet re-transmissions. Nevertheless, the performance of existing cache management schemes mainly developed for a single cache is inadequate for a network of caches. There are many factors such as data dependencies, data redundancy, the limited size of caches, poor replacement policies, and many other factors that negatively impact a network of caches. Besides, traffic correlation among different caches on the network influences the performance of the network of caches. One of the essential correlations is the edge filtering effect. In the presence of data redundancy, the edge filtering effect even becomes more severe. The cache filtering effect happens when all arriving requests inspect the first cache for data. Therefore, the subsequent caches in the network receive only those requests that could not find data (cache-miss) from the edge cache. In this paper, we propose Frozen-cache to mitigate the filtering effect. This policy repeatedly freezes content in a cache to allow subsequent caches to receive popular content. A lightweight coordinated scheme incorporated with Frozen-cache policy to cope with the data redundancy problem. Based on our experiments obtained from realistic scenarios, the Frozen-cache idea highly outperforms state of the art caching schemes. Depending on the network setup, this superiority varies from 25% to 700%.

Information Resilience in a Network of Caches With Perturbations

Caching in a network of caches has been widely investigated for improving information/content delivery efficiency (e.g., for reducing content delivery latency, server load and bandwidth utilization). In this work, we look into another dimension of network of caches – enhancing resilience in information dissemination rather than improving delivery efficiency. The underlying premise is that when information is cached at more locations, its availability is increased and thus, in turn, improve information delivery resiliency. This is especially important for networks with perturbations (e.g., node failures). Considering a general network of caches, we present a collaborative caching framework for maximizing the availability of the information. Specifically, we formulate an optimization problem for maximizing the joint utility of caching nodes in serving content requests in perturbed networks. We first solve the centralized version of the problem and then propose a distributed caching algorithm that approximates the centralized solution. We compare our proposal against different caching schemes under a range of parameters, using both real-world and synthetic network topologies. The results show that our algorithm can significantly improve the joint utility of caching nodes. With our distributed caching algorithm, the achieved caching utility is up to five times higher than greedy caching scheme. Furthermore, our scheme is found to be robust against increasing node failure rate, even for networks with a high number of vulnerable nodes.