Caching Scheme Research Articles

Improved Multiresolution Neural Network for Mobility-Aware Security and Content Caching for Internet of Vehicles

With the emerging communication and computation technologies, the Internet of Vehicles (IoV) has become a new paradigm that enables vehicles to communicate with Road Side Units (RSUs) as well as with other vehicles to collect or exchange information. However, the inherent characteristics of IoV, such as the high mobility of the vehicle and the limited storage capacity of the edge nodes, cause numerous difficulties in developing a caching scheme. Therefore, in this paper, a novel improved Multinomial Recurrent Neural Network (MRNN) classifier and 2CK-ECC algorithm are proposed to predict the vehicular mobility, security, and content caching for the IoV. Initially, the vehicles are checked for registration. Afterwards, vehicle login and vehicle authentication take place. Then, mobility prediction is carried out for the authenticated vehicles using the MRNN Classifier. After that, vehicle clustering is done via the novel Variance Balanced Iterative Reducing and Clustering Using Hierarchies (VBIRCH) technique. Next, the Relay Vehicles (RVs) are selected using the novel Harris Self Avoiding Hawks Optimization (HSAHO). Further, the data contents are divided into chunks and saved in the cache memory. From the cache memory, data is transferred to the RSU. Finally, a novel Caesar Combined Key-based Elliptic Curve Cryptography (2CK-ECC) algorithm is investigated for secure data transmission. The experimental outcomes demonstrate that the proposed technique outperforms the existing base line approaches.

Cluster Content Caching: A Deep Reinforcement Learning Approach to Improve Energy Efficiency in Cell-Free Massive Multiple-Input Multiple-Output Networks.

With the explosive growth of micro-video applications, the transmission burden of fronthaul and backhaul links is increasing, and meanwhile, a lot of energy consumption is also generated. For reducing energy consumption and transmission delay burden, we propose a cell-free massive multiple-input multiple-output (CF-mMIMO) system in which the cache on the access point (AP) is used to reduce the load on the link. In this paper, a total energy efficiency (EE) model of a cache-assisted CF-mMIMO system is established. When optimizing EE, forming the co-operation cluster is critical. Therefore, we propose an energy-efficient joint design of content caching, AP clustering, and low-resolution digital-to-analog converter (DAC) in a cache-assisted CF-mMIMO network based on deep reinforcement learning. This scheme can effectively cache content in APs and select the appropriate DAC resolution. Then, taking into account the channel state information and user equipment (UE)'s content request preference, a deep deterministic policy gradient algorithm is used to jointly optimize the cache strategy, AP clustering, and DAC resolution decisions. Simulation results show that the energy efficiency of the proposed scheme is 4% higher than that of other schemes without the resolution optimization and is much higher than that of the only AP clustering without the joint design of content caching and channel quality.

VMIFresh: Efficient and fresh caches for virtual machine introspection

Virtual machine introspection (VMI) is the process of extracting knowledge about the inner state of a virtual machine from the outside. Traditional passive introspection mechanisms have proved themselves ineffective in many application domains due to their low performance. As a remedy for this issue, caching at the level of the introspection application was introduced. However, this sacrificed the freshness of VMI and led to an inconsistent outside view.In this work, we propose a multi-purpose hybrid caching scheme with freshness and consistency guarantees that is interleaved with the guest's MMU. This scheme can easily be integrated into existing applications and frameworks such as libvmi and Volatility 3. We demonstrate its feasibility by developing a prototype for such applications. Furthermore, the experimental evaluation of our approach suggests that it even significantly exceeds the performance of previous inconsistent caches.

Collaborative caching relay algorithm based on recursive deep reinforcement learning in mobile vehicle edge network

With the rapid development of Internet of vehicles (IoV) and the continuous emergence of vehicle information applications, the demand for content in vehicle networking is growing at an alarming speed. Mobile vehicular edge caching is regarded as a promising technology in improving Quality of Service (QoS) and reducing latency. Many caching algorithms have been proposed, which usually place contents in the Road Side Units (RSUs) to provide services to users near them. However, due to the high-speed movement of vehicles and limitations of RSU coverage, caching interrupts often occur frequently, leading to a deterioration in service quality. To deal with this problem, we make full use of Vehicle-to-Vehicle (V2V) collaboration to construct a caching system which does not require RSU support, and propose a Recursive Deep Reinforcement Learning based Collaborative Caching Relay strategy (RDRL-CR). On purpose to minimize the service delay under capacity constraints, the caching problem is formulated as an integer linear programming problem, and caching decisions are achieved through partially observable Markov Decision Process (MDP). Specifically, this strategy utilizes a Graph Neural Network (GNN) to predict vehicle trajectories, and then selects vehicles that can serve as caching nodes by calculating link stability metrics between vehicles. The Long Short Term Memory (LSTM) network is embedded into a deep deterministic strategy gradient algorithm to achieve the final caching decision. Compared with existing caching strategies, the proposed caching strategy in this paper improves the caching hit rate by about 25% and reduces content access latency by about 20%.

Multi-Agent Caching Strategy for Spatial-Temporal Popularity in IoV

Motivated by connected vehicles, the internet of vehicles (IoV) has a prosperous development, thus a variety of IoV applications have emerged, which causes the dramatic growth of data traffic and content request, thus affecting the quality of service (QoS) of IoV. Edge caching can effectively improve the QoS by caching the contents in the road side unit (RSU) near the requester. Moreover, most existing IoV caching strategies only consider that the content popularity has temporal correlation or assume that the content popularity is given, which will impact the accuracy of content popularity. In this paper, we propose a spatial-temporal correlation approach to predict the content popularity, which can improve the accuracy of the content popularity by the spatial-temporal correlation analyzing of the RSU historical content requests. Then, we introduce a multi-agent reinforcement learning (MARL) caching strategy based on the result of prediction, where each RSU agent individually chooses the action with the constraint of caching resources and updates the action to maximize the cumulative reward. Simulation results demonstrate that the proposed caching strategy has better performance. Take the Zipf parameter is 0.8 as an example, compared with the probability caching scheme (PCS), the proposed caching strategy improves the caching hit ratio by 15% and reduces the delay by 34%.

Extended Placement Delivery Arrays for Multi-Antenna Coded Caching Scheme

Extended Placement Delivery Arrays for Multi-Antenna Coded Caching Scheme

Multi-Agent Deep Reinforcement Learning for content caching within the Internet of Vehicles

In the Internet of Vehicles (IoV), multiple applications and multimedia services are deployed to enhance the quality of traveling experienced by passengers. These software components ingest heterogeneous multimedia content (e.g., streams, safety information, etc.) acquired through the Internet. However, the high mobility constraints and time-variant dynamics of the IoV may delay the acquisition process. Caching lowers acquisition delays of the content by storing requested content in the vicinity of its consumers.We propose a Multi-Agent Deep Reinforcement Learning (MADRL) based framework to design an optimal caching strategy for IoVs. The framework objective is to incentivize sharing caching resources while minimizing errors in estimating content needs. Extensive simulation results with different system parameters demonstrate the efficiency of the proposed solution.

Stateless Q-learning algorithm for service caching in resource constrained edge environment

In resource constrained edge environment, multiple service providers can compete to rent the limited resources to cache their service instances on edge servers close to end users, thereby significantly reducing the service delay and improving quality of service (QoS). However, service providers renting the resources of different edge servers to deploy their service instances can incur different resource usage costs and service delay. To make full use of the limited resources of the edge servers to further reduce resource usage costs, multiple service providers on an edge server can form a coalition and share the limited resource of an edge server. In this paper, we investigate the service caching problem of multiple service providers in resource constrained edge environment, and propose an independent learners-based services caching scheme (ILSCS) which adopts a stateless Q-learning to learn an optimal service caching scheme. To verify the effectiveness of ILSCS scheme, we implement COALITION, RANDOM, MDU, and MCS four baseline algorithms, and compare the total collaboration cost and service latency of ILSCS scheme with these of these four baseline algorithms under different experimental parameter settings. The extensive experimental results show that the ILSCS scheme can achieve lower total collaboration cost and service latency.

NDN-Based Coded Caching Strategy for Satellite Networks

To solve the transmission correlation issue arising from traditional named data networking (NDN) caching during segmenting contents, where users must obtain data blocks related to the requested file blocks to recover the source data, in this paper, we propose an NDN-based coded caching strategy for low Earth orbit (LEO) satellite networks, using coding operations to remove the transmission correlation of data. To achieve efficient content distribution, the satellite node’s coded package is strategically placed with the objectives of minimizing the backhaul link load and content acquisition latency. The optimization problem is solved by using a multi-colony ant colony algorithm, enabling fast content retrieval. We designed a cluster cooperative distribution mechanism to simplify the satellite network’s management and reduce the load of satellite links for content distribution when covered by multiple satellite nodes. Finally, we compare the multi-objective coded caching (CCMO) introduced in this paper with the most popular (MP) strategy and the random-based coded caching (CCR) strategy; the average cache hit ratio, backhaul link load, and content acquisition delay are compared and analyzed. The results show that CCMO performs better.

A review of modern caching strategies in named data network: overview, classification, and research directions

A review of modern caching strategies in named data network: overview, classification, and research directions

Mobility-aware caching strategy in an SDN-based cooperative caching network

The upsurge of mobile multimedia traffic puts a heavy burden on the cellular network, and wireless caching has emerged as a powerful technique to overcome the backhaul bottleneck and alleviate the network burden. However, most previous works ignored user mobility, thus not reaping the caching gain from user mobility and having limited practical applications. In this paper, a mobility-aware caching strategy for the software-defined network (SDN)-based network is studied. Firstly, since typical mobile user (MU) has multiple opportunities to connect with the nearby MUs and Small-base stations (SBSs), the contact times between MUs as well as between MU and SBSs are derived as the Poisson distribution and Gamma distribution. Secondly, we propose a two-tier cooperative caching strategy, where SBSs cache the rateless Fountain code encoded video blocks probabilistically and nonrepeatedly while MUs just store the whole encoded video received last time. The corresponding four-stage transmission process is analyzed, where the key intermediate step is the derivation of the service and the failing probabilities of each transmission manner. Finally, we derive the successful offloading rate and average data offloading ratio (ADOR) as performance metrics. A system optimization problem based on ADOR is formulated, and two solutions are proposed, namely, derivative-based solution (DB-Solution) and long-tail distribution approximation (LTD-Approximation). Simulation results demonstrate that the effectiveness of LTD-Approximation is similar to the DB-Solution, and the proposed caching strategy can achieve quasi-optimal performance compared with other contrast schemes.

Ebublio: Edge-Assisted Multiuser 360° Video Streaming

As one of the most important manifestations of virtual reality (VR), 3600 panoramic videos in recent years have experienced booming development due to the desire for immersive and interactive experiences. Compared to traditional videos, 3600 videos are featured with uncertain user field of view (FoV), more sensitive delay tolerance, and much higher bandwidth requirement, bringing unprecedented challenges to 3600 video streaming. Meanwhile, the development of 5G and mobile edge computing starts to pave the way for high-bandwidth low-latency video streaming. Some preliminary works focus on either individual FoV prediction or multi-user QoE oriented cache strategy design, while how to design a holistic solution toward optimizing the overall user QoE with considerations over fairness and long-term system cost remains a non-trivial problem. In this paper, we propose Ebublio, a novel intelligent edge caching framework to address the aforementioned challenges in 3600 video streaming. Ebublio consists of a collaborative FoV prediction (CFP) module and a long-term tile caching optimization (LTO) module to jointly optimize the long-term user QoE and system cost. The former module integrates the features of video content, user trajectory, and other users records for combined prediction. The latter one employs the Lyapunov framework and a subgradient optimization approach towards the optimal caching replacement policy. Our trace-driven evaluation demonstrates the superiority of our framework, with about 42% improvement in FoV prediction, and 36% improvement in QoE at similar traffic consumption.

COCAM: a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning in multi-clouds environment

The evolution of the Internet of Things technology (IoT) has boosted the drastic increase in network traffic demand. Caching and multicasting in the multi-clouds scenario are effective approaches to alleviate the backhaul burden of networks and reduce service latency. However, existing works do not jointly exploit the advantages of these two approaches. In this paper, we propose COCAM, a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning to minimize the transmission number in the multi-clouds scenario with limited storage capacity in each edge cloud. Specifically, by integrating a cooperative transmission model with the caching model, we provide a concrete formulation of the joint problem. Then, we cast this decision-making problem as a multi-agent extension of the Markov decision process and propose a multi-agent actor-critic algorithm in which each agent learns a local caching strategy and further encompasses the observations of neighboring agents as constituents of the overall state. Finally, to validate the COCAM algorithm, we conduct extensive experiments on a real-world dataset. The results show that our proposed algorithm outperforms other baseline algorithms in terms of the number of video transmissions.Graphical

Social-Aware Decentralized Cooperative Caching for Internet of Vehicles

As the industry related to intelligent vehicles becomes increasingly mature, emerging in-vehicle applications and services are mushrooming. They have strict requirements on service response delay and network bandwidth. In the face of dynamic environment in the Internet of Vehicles (IoV), how to design an adaptive edge caching strategy becomes a challenge. To cope with this challenge, some researchers have introduced optimization methods based on learning algorithms in cooperative caching. However, general learning algorithms tend to waste bandwidth and computing resources on repetitive task. To this end, we make full use of the idle resources in the road to build a cooperative caching system and propose a Social-Aware Decentralized Cooperative caching (SADC) for IoV. This strategy uses the federated learning framework to train the collaborative caching algorithm based on Deep Reinforcement Learning (DRL). Among them, the Road Side Unit (RSU) is responsible for the training and updating of the global model, and vehicles use local data to provide local updates to the RSU, which then averages the updates provided by all vehicles to improve the shared model. In addition, we use the social network of vehicle users to obtain vehicle contact rates in different areas. The SADC strategy can reduce the content transmission latency and response time, thereby improving the experience quality of vehicle users. Compared with traditional caching strategies, this strategy reduces the average content access delay by about 20%. We also demonstrate the effectiveness of this strategy using an extensive set of experiments.

In-Network Caching for ICN-Based IoT (ICN-IoT): A Comprehensive Survey

The Internet of Things (IoT) has already emerged as one of the most popular directions in today’s information and communication technology (ICT) domain. With its advancement over different application areas, such as smart home, smart healthcare, industry 4.0, etc., a huge amount of data has been generated by billions of IoT devices, which aggravates the shortcomings of IP-based networks, such as limited expressiveness of IP addressing, inefficient support for mobility and in-network caching. Building IoT on top of information-centric networking (ICN) is believed to be a promising solution to tackle the above challenge, especially the in-network caching of ICN can significantly benefit IoT in terms of reducing data and saving IoT devices’ energy. However, caching IoT data is more challenging than caching traditional Internet content, e.g., video, because IoT data are usually valid within a certain period of time, and IoT devices are typically constrained with battery. Hence, in this survey, we first review the current implementation proposals of ICN-IoT. Next, we present the conventional caching decision policies and replacement policies which could be adopted to mitigate the aforementioned challenges, e.g., reducing IoT traffic, saving energy, reducing data retrieval latency. Further, since leveraging machine learning (ML) techniques have the potential to further improve the caching efficiency by dealing with uncertainties, e.g., predicting unknown information, adaptively interacting with the environment, we also demonstrate the recently proposed ML-based caching schemes for ICN-IoT. In addition, we outline the open research issues and point out the future opportunities of caching in ICN-IoT.

UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks

A UAV-assisted caching strategy considering content cache pricing in vehicular networks is proposed to address the problem of high communication load and high backhaul link overhead in vehicular networks. Consider a traffic scenario consisting of a content provider (CP), a network operator (NO), and multiple mobile users, where the NO has a set of cache-enabled roadside units (RSUs) and an unmanned aerial vehicle (UAV). The CP leases some popular contents to the NO for its benefit and the NO places this leased content in its RSU’s local cache to save expensive backhaul transmission overhead and latency. However, both NO and CP are selfish and their interests conflict with each other because they have opposing expectations for content pricing. In order to take into account the interests of both, this paper defines the utilities of CP and MNO and uses the Stackelberg game framework to model the competition between the two entities, where CP acts as a leader and sets the rental price of the content and NO acts as a follower responding to CP’s actions. An iteration-based dynamic programming algorithm is also designed to find the Stackelberg equilibrium. Meanwhile, a caching-capable UAV is introduced into the vehicular network and, based on this, a Dijkstra-based path planning algorithm is designed to further increase the total utility of NO by optimizing the trajectory of the UAV. The simulation results show that the strategy in this paper can reasonably allocate the benefits of CP and NO, reduce the average request delay, and increase the utility of NO; for example, we reduced the request latency for vehicle users by 27% and increased the total utility of NO by 13%.

Digital-Twin-Enabled On-Demand Content Delivery in HetVNets

The heterogeneous vehicular networks (HetVNets) can accelerate the deployment of Internet of vehicles (IoV) and enrich the content distribution methods. However, the diverse requirements of vehicular users (VUs), the limited cache resources of roadside units (RUs), and the frequent interactions between VUs and RUs pose great challenges to efficiently distribute contents. To address these challenges, we propose an on-demand content delivery scheme in digital twin enabled HetVNets (DT-HetVNets). Specifically, we first design an on-demand content delivery architecture in DT-HetVNets which uses DT communication mode to simplify the frequent interactions between VUs and RUs. With this architecture, by jointly considering the popularity of each content and the relevance between different contents, the personal content requirement of each DT of VU (DT-VU) can be perceived and the VUs within the coverage of the same RU can collaboratively request contents in groups. Then, we formulate the interaction between each group and the DT of the RU (DT-RU) as a double auction game to determine the transaction price of the perceived content, where the request information of the contents which are accepted by the groups can be shared between different DT-RUs based on the path of each group, enabling collaborative content recommendation between the RUs. After that, by jointly considering the contents recommended by different DT-RUs and the content popularity, the content caching model of each DT-RU is formulated as a knapsack problem, where a collaborative content caching algorithm is designed to obtain the optimal caching strategy with the target of making full use of the limited cache resources. Compared with the conventional schemes, the simulation results show that our scheme can not only bring the highest utility to the RUs, but also lead to the highest hit ratio and the lowest delay.

Joint Unmanned Aerial Vehicle Location and Beamforming and Caching Optimization for Cache-Enabled Multi-Unmanned-Aerial-Vehicle Networks

Due to the advantages such as high flexibility, low cost and easy implementation offered by unmanned aerial vehicles (UAVs), a UAV-assisted network is regard as an appealing solution to a seamless coverage, high disaster-tolerant and on-demand wireless system. In this paper, we focus on the downlink transmission in a cache-enabled UAV-assisted wireless communication network, where UAVs cache popular content from a macro base station in advance and cooperatively transfer the content to users. We aim to minimize the average transmission latency of the system and to formulate an optimization problem that jointly optimizes the UAV location, beamforming and caching strategy. However, the formulated problem is very challenging because of its non-convexity and the highly coupled optimization variables. To solve this resulting problem efficiently, we decompose it into two subproblems, namely UAV location and beamforming optimization, and UAV caching strategy optimization. The first subproblem is an NP-hard joint optimization problem, while the second one is a linear programing problem. By adopting the first-order Taylor expansion, we propose a convex optimization algorithm based on the difference-of-convex (DC) method. Specifically, we bring out a method to apply linear approximation in the DC-based algorithm, which is particularly suitable to the problems involving complicated summations. The numerical results demonstrate that the proposed DC-based iterative optimization algorithm can efficiently reduce the average transmission latency of the system.

Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing

With the convergence of the Internet of Things, 5G, and artificial intelligence, limited network bandwidth and bursts of incoming service requests seem to be the most important factors affecting user experience. Therefore, caching technology was introduced. In this paper, a caching placement optimization strategy based on comprehensive utility (CPOSCU) in edge computing is proposed. Firstly, the strategy involves quantifying the placement factors of data blocks, which include the popularity of data blocks, the remaining validity ratio of data blocks, and the substitution rate of servers. By analyzing the characteristics of cache objects and servers, these placement factors are modeled to determine the cache value of data blocks. Then, the optimization problem for cache placement is quantified comprehensively based on the cache value of data blocks, data block retrieval costs, data block placement costs, and replacement costs. Finally, to break out of the partial optimal solution for cache placement, a penalty strategy is introduced, and an improved tabu search algorithm is used to find the best edge server placement for cached objects. Experimental results demonstrate that the proposed caching strategy enhances the cache service rate, reduces user request latency and system overhead, and enhances the user experience.

SoftCaching: A framework for caching node selection and routing in Software-Defined Information Centric Internet of Things

A considerable demand for Internet of Things (IoT) services is causing exponential growth in Internet traffic. Software-Defined Networking (SDN) and Information-Centric Networking (ICN) are complementary technologies for IoT services provisioning that reduce network traffic and service provisioning delays by caching content at intermediate nodes in the network. Existing research on Software-Defined Information-Centric Networking (SDICN) for IoT service provisioning suffers from two major challenges: (a) optimal caching node selection and (b) optimal path computation from content consumers to caching nodes based on end-to-end delay and link utilization constraints. We propose a SoftCaching framework that optimizes caching node selection for content publishers and computes the optimal routing path to caching nodes for content consumers. It computes optimal locations for caching nodes and finds optimal routing paths for IoT services to access cached content. SoftCaching analyzes network traffic on all nodes in the network and collects traffic metrics. It estimates the best possible caching nodes by using Waypoint Enforcement (WPE) and solving the traffic matrix using Singular Value Decomposition (SVD) and QR Factorization. WPE and SVD-based QR factorization provide optimal locations to cache the content for IoT services. Subsequently, SoftCaching uses a constraint-based shortest path algorithm to compute the optimal routing path from content consumers to caching nodes. We make sure that the caching node and the routing path selection follow delay and link utilization constraints. We perform extensive experiments for evaluation, and the results show that SoftCaching outperforms state-of-the-art caching schemes in terms of delay, link load, hop count, path stretch, and energy consumption metrics.