Cache Nodes Research Articles

Deep Reinforcement Learning for Trustworthy and Time-Varying Connection Scheduling in a Coupled UAV-Based Femtocaching Architecture

The paper is motivated by the urgent need, imposed by the COVID-19 pandemic, for trustworthy access to secure communication systems with the highest achievable availability and minimum latency. In this regard, we focus on an ultra-dense wireless network consisting of Femto Access Points (FAPs) and Unmanned Aerial Vehicles (UAVs), known as caching nodes, where there are more than one possible caching node to handle user’s request. To efficiently cope with the dynamic topology of wireless networks and time-varying behavior of ground users, our focus is to develop an efficient connection scheduling framework, where ground users are autonomously trained to determine the optimal caching node, i.e., UAV or FAP. Our aim is to minimize users’ access delay by maintaining a trade-off between the energy consumption of UAVs and the occurrence of handovers. To achieve these objectives, we formulate a multi-objective optimization problem and propose the Convolutional Neural Network (CNN) and Q-Network-based Connection Scheduling (CQN-CS) framework. More specifically, to solve the constructed multi-objective connection scheduling problem, a deep Q-Network model is developed as an efficient Reinforcement Learning (RL) approach to train ground users to handle their requests in an optimal and trustworthy fashion within the coupled UAV-based femtocaching network. The effectiveness of the proposed CQN-CS framework is evaluated in terms of the cache-hit ratio, user’s access delay, energy consumption of UAVs, handover, lifetime of the network, and cumulative rewards. Based on the simulation results, the proposed CQN-CS framework illustrates significant performance improvements in companion to Q-learning and Deep Q-Network (DQN) schemes across all the aforementioned aspects.

Information‐Centric Networking Cache Placement Method Based on Cache Node Status and Location

Information‐Centric Networking with caching is a very promising future network architecture. The research on its cache deployment strategy is divided into three categories, namely, noncooperative cache, explicit collaboration cache, and implicit collaboration cache. Noncooperative caching can cause problems such as high content repetition rate in the web cache space. Explicit collaboration caching generally reflects the best caching effect but requires a lot of communication to satisfy the exchange of cache node information and depends on the controller to perform the calculation. On this basis, implicit cooperative caching can reduce the information exchange and calculation between cache nodes while maintaining a good caching effect. Therefore, this paper proposes an on‐path implicit cooperative cache deployment method based on the dynamic LRU‐K cache replacement strategy. This method evaluates the cache nodes based on their network location and state and selects the node with the best state value on the transmission path for caching. Each request will only select one or two nodes for caching on the request path to reduce the redundancy of the data. Simulation experiments show that the cache deployment method based on the state and location of the cache node can improve the hit rate and reduce the average request length.

A Cache Placement Strategy Based on Entropy Weighting Method and TOPSIS in Named Data Networking

Named data networking (NDN) aims to change the traditional content delivery method and caching by router nodes caching and participating in forwarding. NDN-caching can reduce the expected flood of global data traffic by providing cache storage at intermediate nodes for transmitted content objects, making data broadcasting in an efficient way. In this paper, a novel caching strategy based on entropy weighting method and TOPSIS is proposed for efficient content dissemination to improve NDN’s in-network caching performance. Firstly, the consumer’s request process is modeled by the entropy weighting method and TOPSIS to obtain the best cache node for the cache object according to the real-time status of the node. Secondly, two cache replacement algorithms (composed of an active cache replacement algorithm and a passive cache replacement algorithm) are proposed to reduce the cache redundancy on the delivery path and improve the utilization of data packets in the nodes. Finally, an effective cache mechanism and data packet migration scheme are proposed to further improve the cache performance according to the different types of cache nodes. The performance evaluation shows that the proposed scheme performs better in terms of cache hit rate, latency, and link load compared with some existing strategies.

DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

Caching popular content at the network edge, such as roadside units (RSUs), is a promising solution that enhances the user’s quality-of-experience (QoE) and reduces network traffic. In this regard, the most challenging issue is to correctly predict the future popularity of contents and effectively store them in the cache of edge nodes. Thus, in this paper, we propose a distributed proactive caching scheme at the edge to optimize the content retrieval cost and improve the QoE of the mobile users. This proactive content caching scheme, namely Distributed Collaborative Learning (DCoL), is a non-parametric content popularity prediction mechanism in a distributed setting. Next, we show the advantage of DCoL as two folds: (i) it leverages distributed content popularity information to develop local content caching strategy, and (ii) it exploits the regional database using the long short-term memory (LSTM)-based prediction model to capture the dependency between requested contents. Simulation results using real datasets demonstrate that our scheme yields 8.9% and 18% gains, respectively, in terms of the cache hit efficiency and content retrieval cost, compared with a competitive centralized baseline, and outperforms other traditional caching strategies.

Dynamic Hierarchical Caching Resource Allocation for 5G-ICN Slice

Network slicing and Multiple-Access Edge Computing (MEC) are key technologies in fifth-generation (5G) networks. The flexible programmability of network slicing and the decentralization of MEC facilitate the deployment of Information-Centric Networking (ICN). The caching feature of ICN can provide users with low-latency data services. Although many existing works have addressed the cache deployment problem or the cache optimization problem, most of them do not consider the issue of caching resource allocation in the dynamic and hierarchical environment. Dynamic deployment of cache nodes can improve the operator’s revenue as much as possible while accurately allocating the caching resources can reduce the user-requested latency. Therefore, in this study, a problem of the operator’s expected revenue maximization is presented in an environment combining dynamic deployment of the MECs and the caching-enabled node ICN-Gateway (ICN-GW). To solve this problem, we propose an optimal stopping theory (OST)-based dynamic hierarchical caching resources allocation (ODH-CRA) algorithm. The algorithm consists of three parts. Firstly, an Integer Linear Programming (ILP) solution is proposed to determine the optimal deployment of the MECs. This method determines the optimal location and number of the MECs by considering deployment costs and service requirement costs synthetically. Secondly, a redeployment technique based on the OST is proposed to determine the best redeployment time of the MECs according to the values of latency violations and the service latency requirements. Finally, an improved elite genetic algorithm (IEGA) is proposed to find the optimal solution of the hierarchical caching resource allocation. This method searches the optimal scheme by maximizing the operator’s revenue joint caching costs and energy consumption. Ultimately, we perform a series of simulation experiments to compare the proposed method’s performance to dynamic and hierarchical methods. Our solution can effectively reduce the latency for users’ requesting, improve the revenue of ICN Communication Service Provider (ICSP), and provide an effective caching resource allocation scheme for the next generation of Internet of Things (IoT) networks.

Named Data Networking‐Based On‐Demand Secure Vehicle‐To‐Vehicle Communications

The detection of secure vehicles for content placement in vehicle to vehicle (V2V) communications makes a challenging situation for a well‐organized dynamic nature of vehicular ad hoc networks (VANET). With the increase in the demand of efficient and adoptable content delivery, information‐centric networking (ICN) can be a promising solution for the future needs of the network. ICN provides a direct retrieval of content through its unique name, which is independent of locations. It also performs better in content retrieval with its in‐network caching and named‐based routing capabilities. Since vehicles are mobile devices, it is very crucial to select a caching node, which is secure and reliable. The security of data is quite important in the vehicular named data networking (VNDN) environment due to its vital importance in saving the life of drivers and pedestrians. To overcome the issue of security and reduce network load in addition to detect a malicious activity, we define a blockchain‐based distributive trust model to achieve security, trust, and privacy of the communicating entities in VNDN, named secure vehicle communication caching (SVC‐caching) mechanism for the placement of on‐demand data. The proposed trust management mechanism is decentralized in nature, which is used to select a trustworthy node for cluster‐based V2V communications in the VNDN environment. The SVC‐caching strategy is simulated in the NS‐2 simulator. The results are evaluated based on one‐hop count, delivery ratio, cache hit ratio, and malicious node detection. The results demonstrate that the proposed technique improves the performance based on the selected parameters.

ECC: Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing

Due to the explosion of network data traffic and IoT devices, edge servers are overloaded and slow to respond to the massive volume of online requests. A large number of studies have shown that edge caching can solve this problem effectively. This paper proposes a distributed edge collaborative caching mechanism for Internet online request services scenario. It solves the problem of large average access delay caused by unbalanced load of edge servers, meets users’ differentiated service demands and improves user experience. In particular, the edge cache node selection algorithm is optimized, and a novel edge cache replacement strategy considering the differentiated user requests is proposed. This mechanism can shorten the response time to a large number of user requests. Experimental results show that, compared with the current advanced online edge caching algorithm, the proposed edge collaborative caching strategy in this paper can reduce the average response delay by 9%. It also increases the user utility by 4.5 times in differentiated service scenarios, and significantly reduces the time complexity of the edge caching algorithm.

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.

An effective congestion control scheme based on early offload for space delay/disruption tolerant network

The storage resources and communication resources of spatial network nodes are generally very limited, and they have the characteristics of large time delay and cannot guarantee the existence of end-to-end path all the time. Messages may reside in the intermediate nodes for a long time, which makes the congestion of spatial network inevitable and leads to the decline of network performance. In order to solve this problem, an effective congestion control scheme for space delay/disruption networks based on early unloading is proposed. In this scheme, the change rate of node cache is monitored at all times, and measures are taken in advance when congestion is about to occur, so that messages are transmitted through the non-optimal path prior to the optimal path, so as to alleviate the node storage pressure. Simulation results indicate that the presented scheme can achieve higher success rate of message delivery and more uniform network traffic distribution.

Estimation of Node Cache Occupancy in Satellite Storage Network

The satellite network with cache function becomes the future development trend. How to efficiently use the node cache of the satellite network has become a research hotspot. A theoretical problem that needs to be solved in the latest research on effective utilization of cache is the estimation of cache occupancy. Based on the martingale encapsulation theory, combined with the channel conditions, access protocols, and queuing strategies in the satellite storage network, a satellite node occupancy estimation method and theoretical bounds are proposed in a two-layer satellite network of GEO and LEO. The accuracy of the martingale encapsulation method in estimating the cache occupancy is estimated by simulations. The simulation results show that our method is precise. The cache occupancy estimation method we proposed provides a theoretical basis for the optimal allocation of satellite cache resources.

Multi-Path Selection and Congestion Control for NDN: An Online Learning Approach

In Named Data Networking (NDN) architecture, data can be obtained from multiple content sources (i.e., producers or caching nodes) with multiple paths, making the traditional end-to-end (i.e., TCP/IP) congestion control scheme invalid. In addition, the NDN multi-path discovery and management are still an open issue as the dynamic network topology changes. In this article, we propose a multi-path congestion control mechanism, named MPCC, which includes two major components, i.e., multi-path discovery and multi-path congestion control. Particularly, for multi-path discovery, we first devise a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path tag</i> to uniquely mark each sub-path in the forwarding process, and then propose a tag-aware forwarding strategy to discover and manage sub-paths. For multi-path selection and congestion control, we first integrate the metrics of packet loss, bandwidth, round trip time, and path centrality, for path assessment, based on which, we then leverage the Upper Confidence Bound (UCB) algorithm to select sub-paths in order to maximize the network throughput. In addition, for selected sub-paths, we have devised a sub-path window adaptation algorithm to avoid multi-path congestions. At last, we implement MPCC in ndnSIM and conduct extensive experiments for performance evaluation. Our results demonstrate that MPCC can discover all sub-paths in real-time for the multi-path scenario, and can effectively avoid multi-path congestions with improving throughput and reducing transmission time.

Cache allocation optimisation of user relationship group based on reinforcement learning

In order to alleviate network congestion and reduce request delay, device-to-device caching technology will be an important part of modern communication networks. People always browse the Internet for things they are interested in. However, different people have different points of interest. Therefore, how to choose a suitable cache node when people share content is a challenge. In this study, an optimal cache node selection algorithm based on virtual delay is proposed, where the multi-armed bandit model is used to obtain optimised cache decision based on the interest differences of users. Each candidate user may become a cache node, and the algorithm selects the user who minimises the overall delay as the node. When multiple candidate users exist, it is necessary to cooperate between multiple candidate users whose cache space is limited in order to maximise the cache hit rate. However, since each candidate user acts as a cache node has a defferent service efficiency for different requests from surrounding users, it is necessary to effectively distinguish cooperative cache users. This study proposes a master–slave node cooperative cache model based on the optimal cache node selection. The experimental results confirm that the proposed schemes achieve lower overall delay performance.

Deep Learning for Wireless Coded Caching With Unknown and Time-Variant Content Popularity

Coded caching is effective in leveraging the accumulated storage size in wireless networks by distributing different coded segments of each file in multiple cache nodes. This paper aims to find a wireless coded caching policy to minimize the total discounted network cost, which involves both transmission delay and cache replacement cost, using tools from deep learning. The problem is known to be challenging due to the unknown, time-variant content popularity as well as the continuous, high-dimensional action space. We first propose a clustering based long short-term memory (C-LTSM) approach to predict the number of content requests using historical request information. This approach exploits the correlation of the historical request information between different files through clustering. Based on the predicted results, we then propose a supervised deep deterministic policy gradient (SDDPG) approach. This approach, on one hand, can learn the caching policy in continuous action space by using the actor-critic architecture. On the other hand, it accelerates the learning process by pre-training the actor network based on the solution of an approximate problem that minimizes the per-slot cost. Real-world trace-based numerical results show that the proposed prediction and caching policy using deep learning outperform the considered existing methods.

Fair caching networks

We consider caching networks in which the routing cost for serving a content request can be reduced by caching the requested content item in cache nodes closer to the users. We refer to the cost reduction enabled by caching as the caching gain, and the product of the caching gain of a content request and its request rate as caching gain rate. We aim to study fair content allocation strategies through a utility-driven framework, where each request achieves a utility of its caching gain rate, and consider a family of α-fair utility functions to capture different degrees of fairness. The resulting problem is an NP-hard problem with a non-decreasing submodular objective function. 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.

An Incrementally Deployable IP-Compatible-Information-Centric Networking Hierarchical Cache System

The major advantage of information-centric networking (ICN) lies in in-network caching. Ubiquitous cache nodes reduce the user’s download latency of content and the drain of network bandwidth, which enables efficient content distribution. Due to the huge cost of updating an entire network infrastructure, it is realistic for ICN to be integrated into an IP network, which poses new challenges to design a cache system and corresponding content router. In this paper, we firstly observed that the behavior pattern of data requests based on a name resolution system (NRS) makes an ICN cache system implicitly form a hierarchical and nested structure. We propose a complete design and an analytical model to characterize an uncooperative hierarchical ICN caching system compatible with IP. Secondly, to facilitate the incremental deployment of an ICN cache system in an IP network, we designed and implemented a cache-supported router with multi-terabyte cache capabilities. Finally, the simulation and measurement results show the accuracy of proposed analytical model, the significant gains on hit ratio, and the access latency of the hierarchical ICN cache system compared with a flat cache system based on naming routing, as well as the high performance of the implemented ICN router.

Toward Energy-Aware Caching for Intelligent Connected Vehicles

With the widespread application of infotainment services in intelligent connected vehicles (ICVs), network traffic has grown exponentially, bringing huge burden and energy consumption to the ICV network. Edge caching, which enables edges [e.g., vehicles or roadside units (RSUs)] with cache storages, is a promising technology to alleviate this problem. In this article, in terms of the hybrid communication mode of vehicle to vehicle (V2V) and vehicle to RSU (V2R), an energy-aware caching scheme for infotainment services is proposed. Considering the geographical distribution of vehicles and RSUs as well as the size of transmission content, the energy consumption model in the ICV network is formulated to implement the optimal selection of cache nodes. Then, the selection of the cache node in the ICV network is transformed into the optimal stopping problem and solved by the optimal stopping theory. Finally, we propose a new algorithm for optimal energy-efficiency cache node selection (OEECS). The simulation results show that the proposed OEECS can obtain higher energy saving and lower average access latency than other baseline schemes.

Mobility-Aware Femtocaching Algorithm in D2D Networks Based on Handover

Recent developments and advancements in wireless networks have brought about new challenges including the backhaul bottleneck and content download latency. The paper focuses on an enterprise wireless network consisting of various candidate caching nodes to serve users' requests such as Femto Access Points (FAPs) and Mobile Users (MUs), supported by Device-to-Device (D2D) communications. Due to the fact that user's mobility is one of the inherent features of massive wireless networks, where FAPs operate in an open access mode, the small transmission range of FAPs leads to triggering frequent handovers. Taking the above challenges into account, we propose a novel Mobility-Aware Femtocaching scheme based on Handover (MAFH). To implement a real wireless network, a random walk model as the mobility pattern of users is utilized within the proposed scheme. In this regard, the best caching node is selected by considering the Received Signal Strength Indicator (RSSI) value and the velocity of users as decision criteria to extend the time interval between two consecutive handovers and increase the battery life of devices. The effectiveness of the proposed MAFH algorithm is evaluated in terms of the cache hit ratio, transferred byte volume, connecting time, content delivery time, the number of handovers, and the energy consumption of clients. Based on the simulation results, the proposed MAFH framework illustrates significant performance improvements in companion to its counterparts across all the aforementioned aspects.

An algorithm based on markov chain to improve edge cache hit ratio for blockchain-enabled IoT

Reasonable allocation of storage and computing resources is the basis of building big data system. With the development of IoT (Internet of Things), more data will be brought. A three-layer architecture includes smart devices layer, edge cloud layer and blockchain-based distributed cloud layer. Blockchain is used in IoT for building a distributed decentralize P2P architecture to deal with the secure issue while edge computing deals with increasing volume of data. Edge caching is one of the important application scenarios. In order to allocate edge cache resources reasonably, to improve the quality of service and to reduce the waste of bandwidth resources, this paper proposes a content selection algorithm of edge cache nodes. The algorithm adopts markov chain model, improves the utilization of cache space and reduces the content transmission delay. The hierarchical caching strategy is adopted and the secondary cache stores slides of contents to expand the coverage of cached content and to reduce user waiting time. Regional node cooperation is adopted to expand the cache space and to support the regional preference of cache content. Compared with the classical substitution algorithm, simulation results show that the algorithm in this paper has higher cache hit ratio and higher space utilization.

Joint Distributed Link Scheduling and Power Allocation for Content Delivery in Wireless Caching Networks

In wireless caching networks, the design of the content delivery method must consider random user requests, caching states, network topology, and interference management. In this article, we establish a general framework for content delivery in wireless caching networks without stringent assumptions that restrict the network structure and interference model. Based on the framework, we propose a dynamic and distributed link scheduling and power allocation scheme for content delivery that is assisted by belief-propagation (BP) algorithms. The proposed scheme achieves three critical purposes of wireless caching networks: 1) limiting the delay of user request satisfactions, 2) maintaining the power efficiency of caching nodes, and 3) managing interference among users. In addition, we address the intrinsic problem of the BP algorithm in our network model, proposing a matching algorithm for one-to-one link scheduling. Simulation results show that the proposed scheme provides almost the same delay performance as the optimal scheme found through an exhaustive search at the expense of a little additional power consumption and does not require a clustering method and orthogonal resources in a large-scale D2D network.

Content-Centric Heterogeneous Fog Networks Relying on Energy Efficiency Optimization

Next-generation wireless communications are expected to provide reliable high-rate connectivity. The recent concept of fog-enabled architecture comes to rescue by invoking a substantial amount of data storage close to end-user devices for meeting these challenges. Thus, caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. Therefore, in this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs in conjunction with a fixed node association strategy, where the FNs communicate either with other FNs by node-to-node (N2N) communications or with the FAPs in their proximity. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem under caching memory constraints. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm for maximizing the EE. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements.