Cache Resources Research Articles

An on-demand collaborative edge caching strategy for edge–fog–cloud environment

In this paper, we tackle the critical challenges of content edge caching, such as limited storage capacity, content popularity prediction, dynamic user demand, and user privacy, issues that most existing studies only address partially. We present an innovative Genetic Algorithm-based On-demand Collaborative Edge Caching mechanism (GAOCEC), which introduces a multi-tiered caching architecture integrating cloud, fog, and edge computing. To enhance caching efficiency and minimize system cost, a novel on-demand caching quota mechanism is proposed that dynamically allocates cache resources to edge servers. To strengthen user privacy protection during content popularity prediction, a CNN-BiLSTM-based Federated Learning algorithm (CBFL) is presented that ensures high prediction accuracy without the need to upload local data to the cloud. We also refine the genetic algorithm for content placement by fine-tuning various parameter sets to identify the optimal balance between latency reduction and caching cost. Our experimental results validate the effectiveness of our approach, demonstrating increased cache hit rates, decreased content response times, and an overall improvement in system efficiency. This work provides a comprehensive, adaptive, and privacy-preserving solution for the edge–fog–cloud environment.

Cluster-based cooperative fog caching for scalable coded videos of multiple content providers

Efficient content caching in video streaming services is important for improving user experience as well as reducing network bandwidth consumption. Fog caching combined with scalable video coding (SVC) has the potential to significantly improve caching efficiency. However, challenges such as the limited storage capacity of fog nodes and determining the optimal number of SVC layers must be overcome for their effective adoption. This becomes more complicated with multiple content providers requiring shared cache resources. To the best of our knowledge, no existing research has simultaneously tackled all these aspects. In this paper, we present Cluster-based Cooperative Fog Caching (CCo-Fog), a holistic caching strategy that enables multiple content providers to share the scarce storage of fog nodes in a multi-tier fog network to judiciously cache SVC videos in a cooperative manner. In particular, CCo-Fog consists of a cluster-based storage partitioning method and tier-wise cooperative content placement policies. The partitioning method distributes the storage of each fog node to multiple content providers for users clustered based on their population density and their proximity to fog nodes. The content placement policies determine the optimal number of SVC layers of each video for different tiers of the fog network by solving a latency-aware content placement optimization problem. Our evaluations on a real-world dataset and various configurations demonstrate the efficacy of CCo-Fog, showing a reduction in latency by about 60% and an increase in fog hit ratio by about 20% on average, compared to state-of-the-art caching strategies.

RMBCC: A Replica Migration-Based Cooperative Caching Scheme for Information-Centric Networks

How to maximize the advantages of in-network caching under limited cache space has always been a key issue in information-centric networking (ICN). Replica placement strategies aim to fully utilize cache resources by optimizing the location and quantity distribution of replicas in the network, thereby improving the performance of the cache system. However, existing research primarily focuses on optimizing the placement of replicas along the content delivery path, which cannot avoid the inherent drawback of not being able to leverage off-path cache resources. The proposals for off-path caching cannot effectively solve this problem as they introduce excessive complexity and cooperation costs. In this paper, we address the trade-off between cache resource utilization and cooperation costs by introducing a mechanism complementary to replica placement. Instead of redesigning a new caching strategy from scratch, we propose a proactive cooperative caching mechanism (called RMBCC) that involves an independent replica migration process, through which we proactively relocate replicas evicted from the local cache to neighboring nodes with sufficient cache resources. The cooperation costs are effectively controlled through migration replica filtering, migration distance limitation, as well as hop-by-hop migration request propagation. Extensive simulation experiments show that RMBCC can be efficiently integrated with different on-path caching strategies. Compared with representative caching schemes, RMBCC achieves significant improvements in evaluation metrics such as cache hit ratio and content retrieval time, while only introducing negligible cooperation overhead.

Real-time FPGA Implementation of an Optic Flow Algorithm Suitable for Background Oriented Schlieren Technique

Abstract To meet the demands of real-time displacement detection in embedded systems, a real-time optical flow calculation method suitable for background oriented schlieren (BOS) technology was transplanted and implemented on an Field Programmable Gate Array(FPGA) system. Firstly, through the analysis of different optical flow operation characteristics and the comparison of processing results in MATLAB with actual experimental data, an optical flow algorithm suitable for real-time processing of background shadow images was selected. Subsequently, by using hardware pipelining and multimodule parallel processing, the single-layer L&K optical flow algorithm was transplanted onto the FPGA and algorithm optimization was performed to achieve real-time optical flow calculation based on adjacent frame images. The key optimization techniques in this paper mainly include cleverly designing Double-Data-Rate Three Synchronous Dynamic Random Access Memory (DDR3) memory partitioning read and write logic to avoid image read and write conflicts; ensuring the timing alignment and data synchronization of consecutive frames by using burst read arbitration modules and two independent First In First Out (FIFO) buffers; and in optical flow calculation, constructing a 3×3 window using a shifting FIFO method to effectively save cache resources and improve computational speed. Experimental results show that the system can achieve real-time processing speeds of not less than 60fps for 1024×768 resolution images, meeting the requirements of practical applications.

Mobility-aware proactive video caching based on asynchronous federated learning in mobile edge computing systems

With the rapid advance of the Internet of Things (IoT) and the surging user-centric smart devices, video services such as short videos, real-time video streaming, and Virtual Reality (VR) gaming are emerging. Traditional cloud-centric caching is no longer able to satisfy the low-latency needs of mobile user groups. Predicting the most popular content in advance and caching it on edge devices such as small base stations (SBS) is a promising solution to alleviate network congestion and supply better Quality of Service (QoS) for mobile users. However, frequently changing popular content, limited edge caching resources, privacy of user data, and user mobility pose significant challenges to edge video caching. To tackle these challenges, we propose an efficient asynchronous federated learning (AFL)-based mobility-aware proactive video caching scheme (AF-MPVC), which significantly improves the overall network performance while preventing the risk of user data leakage. First, we model the proactive video caching problem in mobile edge computing systems and consider the impact of user mobility on AFL, representing the problem as an autoencoder (AE) model loss minimization problem. Then, a proactive video caching algorithm is proposed based on a filtering model, which uses the hidden features obtained from the trained AE model to compute the user similarity to find the popular videos that are most likely to be accessed by users and combine them with the most frequently requested videos. Meanwhile, we take into account the short duration of high mobility users staying on the current SBS and cache some videos to neighboring edge servers to have more redundant space to store more less popular videos. Finally, experiments on three real-world datasets demonstrate that the presented caching scheme outperforms other baseline caching schemes with respect to cache hit rate and latency reduction.

Dynamic task offloading and service caching based on game theory in vehicular edge computing networks

Vehicular edge computing (VEC) enables task offloading from vehicle to edge servers deployed on Road Side Units (RSUs) and to vehicle with idle resources, which is considered as a promising vehicle network architecture. However, efficient task offloading and task resource cache management in the VEC network is challenging. In this work, we formulate a joint task offloading and service caching problem (JTOSCP) for VEC with edge-vehicle cooperation, aiming to minimize the total task delay-energy value and cost value of all vehicles while guaranteeing task processing delay tolerance. The formulated JTOSCP is proven as NP-hard problem. Due to the complexity of JTOSCP, we propose a cooperative task service caching and task offloading algorithm named CO-MATCH. In this algorithm, we design the dynamic programming-based service caching (DPSC) Algorithm to incentivize edge services and volunteer vehicles to cache, which takes into account both service caching preferences and social similarity. The Many-to-One Matching Game (MOMG) Algorithm is proposed to stimulate edge device games and achieve the optimal task offloading. Then, the CO-MATCH method is proven to be stable. Simulation results demonstrate that our proposed approach can significantly improve the quality of service of user vehicles compared to the other methods.

Joint optimization for computation offloading and 3C resource allocations over wireless-powered and NOMA-enabled multi-access MEC

Multi-access mobile edge computing (MEC) enables mobile users (MUs) to offload tasks to proximal multiple MEC servers for fast task processing. Since MUs generally have stringent delay requirements and limited energy and MEC servers have finite communication, computation, and caching (3C) resources, the joint co-design and optimization over computation offloading and 3C resource allocation for wireless-powered multi-access MEC systems have been highly demanded, while having not been well studied yet. We consider a wireless-powered multi-access MEC, where multiple energy harvesting (EH) based MUs and multiple base stations (BSs) each equipped with an MEC server coexist. Each MU first harvests energy from nearby MEC servers, and then offloads its sub-tasks to multiple MEC servers concurrently based on non-orthogonal multiple access (NOMA), so as to reduce data offloading delay. We first formulate a delay minimization problem, by jointly optimizing MUs’ computation offloading, MEC servers’ computation and caching resources allocation, and system communication resources allocation. Then we propose an alternating direction multiplier method (ADMM) based distributed scheme to decompose the formulated optimization problem into several sub-problems, and use the block coordinate descending (BCD) method and the successive convex approximation (SCA) method to transform all sub-problems each corresponding to one MEC server to convex subproblems. Finally, we validate and evaluate our proposed scheme through numerical analyses, which show that our proposed distributed scheme can greatly reduce the min–max delay of all MUs by allowing each MU’s concurrent data offloading to multiple MEC servers using NOMA and by jointly optimizing computation offloading and 3C resource allocations. Also, the delay imposed by our proposed scheme is much smaller than that imposed by the Interior Point method, which shows the effectiveness of our proposed scheme.

Optimized Two-Tier Caching with Hybrid Millimeter-Wave and Microwave Communications for 6G Networks

Data caching is a promising technique to alleviate the data traffic burden from the backhaul and minimize data access delay. However, the cache capacity constraint poses a significant challenge to obtaining content through the cache resource that degrades the caching performance. In this paper, we propose a novel two-tier caching mechanism for data caching on mobile user equipment (UE) and the small base station (SBS) level in ultra-dense 6G heterogeneous networks for reducing data access failure via cache resources. The two-tier caching enables users to retrieve their desired content from cache resources through device-to-device (D2D) communications from neighboring users or the serving SBS. The cache-enabled UE exploits millimeter-wave (mmWave)-based D2D communications, utilizing line-of-sight (LoS) links for high-speed data transmission to content-demanding mobile UE within a limited connection time. In the event of D2D communication failures, a dual-mode hybrid system, combining mmWave and microwave μWave technologies, is utilized to ensure effective data transmission between the SBS and UE to fulfill users’ data demands. In the proposed framework. the data transmission speed is optimized through mmWave signals in line-of-sight (LoS) conditions. In non-LoS scenarios, the system switches to μWave mode for obstacle-penetrating signal transmission. Subsequently, we propose a reinforcement learning (RL) approach to optimize cache decisions through the approximation of the Q action-value function. The proposed technique undergoes iterative learning, adapting to dynamic network conditions to enhance the content placement policy and minimize delay. Extensive simulations demonstrate the efficiency of our proposed approach in significantly reducing network delay compared with benchmark schemes.

Resource allocation strategy based on service function Chaining in Multi-Access edge computing network

The Multi-access Edge Computing (MEC) network enhances the processing capability of Internet of Things (IoT) terminals by deploying 3C resources (computing resources, cache resources, and communication resources) at the IoT edge side, effectively improving the quality of service (QoS) of IoT applications. The majority of current MEC network research focuses on task offloading and energy management, with few studies looking at resource allocation from a service provisioning perspective. In this paper, we focus on the resource allocation problem when processing multiple tasks requested by IoT terminal devices in the MEC network. Firstly, we transform various tasks requested by IoT end devices into an SFC-based resource allocation problem. Then, we model the problem as a graph theory-based virtualized network function (VNF) node deployment cost optimization problem. Furthermore, we propose two resource allocation and deployment strategies by jointly considering the computing cost and security cost in VNF node deployment. Finally, extensive simulation experiments show that the proposed method can significantly improve the success rate of SFC task processing by more than 30% and network resource utilization by more than 20% when compared to other deployment methods for processing multiple SFC tasks with dynamic requests.

The satellite network cache placement strategy based on content popularity and node collaboration.

Proposed is a Satellite network cache placement strategy (PNCCP) based on popularity and node cooperation to address the issue of significant delays in end-to-end connectivity due to instability among satellites. Initially, the strategy employs spectral clustering algorithm to partition the satellite network's topology, limiting the retrieval scope of content and reducing unnecessary propagation delays. Within each partition, a cache collaboration open mechanism among satellites is devised to share cache resources, utilizing the proximity of neighboring nodes to share popular content and cache space. Furthermore, the data naming network (NDN) cache model is enhanced and integrated with the open mechanism, with an update mechanism designed to address the invalidation caused by the dynamic nature of satellite networks. Finally, aiming to minimize users' average retrieval delay, the artificial bee colony algorithm is employed to solve the optimal cache placement problem. Simulation results demonstrate that compared to three contrasting cache strategies, the proposed strategy reduces user content retrieval delays, improves cache hit rates, and holds an advantage in reducing request hop counts.

Cache allocation policy based on user preference using reinforcement learning in mobile edge computing

SummaryIn mobile edge computing (MEC), due to the limited computing resources and power of mobile augmented reality (MAR) devices, cache identification results which can reduce power consumption and executing time of mobile devices are the solution to process MAR tasks. In this paper, we study an allocation cache problem in MAR systems. The allocation cache problem is formulated as maximizing the cache utility of cache hit ratio and user preference factor. To solve this problem, a cache resource allocation and cache space adjustment policy for edge computing systems is proposed. We also propose an improved double deep Q‐network (DDQN) algorithm to learn this policy. Simulation results show that the policy greatly improves the cache hit ratio compared with the traditional caching policy.

LAG-based schedulability analysis for preemptive global EDF scheduling with dynamic cache allocation

In recent years, the maturation of modern multicore processor technology and its increasing adoption in critical industrial domains have posed significant challenges for real-time systems, primarily due to contention for shared cache resources and the resulting uncertainty. To address this issue, contemporary processors employ cache partitioning techniques, enhancing temporal predictability by isolating cache access among processor cores. However, this isolation technique may lead to real-time tasks missing their deadlines due to an insufficient number of cache partitions. Consequently, this paper investigates the schedulability of preemptive global Earliest Deadline First (EDF) real-time scheduling algorithms that support dynamic cache allocation. We propose an innovative LAG-based schedulability analysis method for these algorithms and present a utilization-based schedulability condition that reduces analysis time complexity while improving analysis accuracy. Building upon this foundation, and incorporating task characteristics into our analysis, this paper further introduces an optimization technique aimed at minimizing the pessimism inherent in the schedulability test. Lastly, the performance and efficiency of the proposed schedulability determination method are validated through simulation experiments with randomly generated tasks.

TVM: A Tile-based Video Management Framework

With the exponential growth of video data, there is a pressing need for efficient video analysis technology. Modern query frameworks aim to accelerate queries by reducing the frequency of calls to expensive deep neural networks, which often overlook the overhead associated with video decoding and retrieval. Furthermore, video storage frameworks optimize video retrieval through video partition or caching, often relying on prior information about the query workload. To further accelerate queries, this study introduces a novel tile-based video management framework, called TVM, which leverages the semantic information embedded in videos, without being dependent on specific query workloads. By constructing a tile-based semantic index for newly ingested videos, TVM effectively reduces the size of decoded and processed video data. To achieve this, TVM introduces an optimal index construction algorithm that utilizes cost function and pseudo-labels. Additionally, the framework proposes a query-driven tile parallel decoding algorithm and resource caching algorithms, which further expedite the retrieval of video frames. Experimental results demonstrate that TVM can significantly enhance the throughput of various query tasks, achieving a notable speedup of more than 5.6×.

A novel coalition game based distributed cooperative content caching in mobile edge networks

In mobile edge networks with mobile vehicles, road-side units (RSUs) can be used as edge nodes to cache some popular contents for moving vehicles in advance, and therefore improving the transmission efficiency and quality of user experience during vehicle-to-infrastructure (V2I) content communication. Transferring large files to a fast-moving car is a challenging task because of the limited communication time between the vehicle and the RSU. In this paper, we propose a cooperative content placement model for RSUs, in which multiple RSUs can jointly complete the cache and transmission of large files, so that the vehicle users can obtain the target content in the process of high-speed movement. To form cooperative coalitions between RSUs and place as much content as possible, a novel coalition game based distributed cooperative content caching scheme, namely CGD3C, is proposed, in which an RSU cooperative clustering algorithm and a resource balancing based coalition formation algorithm is proposed to merge and split the coalitions, so as to obtain a stable coalition structure. Finally, a cooperative content caching determination algorithm is proposed to determine the content placement with objective of maximizing the current interests. Our simulation results show that our proposed CGD3C can achieve stable and effective content delivery for high-speed moving vehicles, form an optimal RSU coalition structure independently, as well as improve the profit by 15%, the caching resource utilization by 24%, and the request hit rate by 12% on average in comparison with existing schemes.

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%.

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.

A Contract-Theory-Based Incentive Mechanism for UAV-Enabled VR-Based Services in 5G and Beyond

The proliferation of novel infotainment services such as Virtual Reality(VR)-based services has fundamentally changed the existing mobile networks. These bandwidth-hungry services expanded at a tremendously rapid pace, thus, generating a burden of data traffic in the mobile networks. To cope with this issue, one can use Multi-access Edge Computing (MEC) to bring the resource to the edge. By doing so, we can release the burden of the core network by taking the communication, computation, and caching resources nearby the end-users (UEs). Nevertheless, due to the vast adoption of VR-enabled devices, MEC resources might be insufficient in peak times or dense settings. To overcome these challenges, we propose a system model where the service provider (SP) might rent Unmanned Area Vehicles (UAVs) from UAV service providers (USPs) to serve as micro-based stations (UBSs) that expand the service area and improve the spectrum efficiency. In which, UAV can pre-cached certain sets of VR-based contents and serve UEs via air-to-ground (A2G) communication. Furthermore, future intelligent devices are capable of 5G and B5G communication interfaces, and thus, they can communicate with UAVs via A2G links. By doing so, we can significantly reduce a considerable amount of data traffic in mobile networks. In order to successfully enable such kinds of services, an attractive incentive mechanism is required. Therefore, we propose a contract theory-based incentive mechanism for UAV-assisted MEC in VR-based infotainment services, in which the MEC offers an amount reward to a UAV for serving as a UBS in a specific location for certain time slots. We then derive an optimal contract-based scheme with individual rationality and incentive compatibility conditions. The numerical findings show that our proposed approach outperforms the Linear Pricing (LP) technique and is close to the optimal solution in terms of social welfare. Additionally, our proposed scheme significantly enhanced the fairness of utility for UAVs in asymmetric information problems.

Resource Allocation Strategy Based On Tripartite Graph in Vehicular Social Networks

Owing to the limited spectrum of vehicular social networks and the fragmented distribution of caching resources, problems such as low vehicle data transmission rate, poor service quality, and low service content delivery efficiency of streaming media are encountered. To solve these problems, we propose a resource allocation strategy based on tripartite graph in vehicular social networks (RATG). This strategy establishes a mobile vehicular social network model based on vehicle mobility and social similarity. To maximize the content delivery efficiency and transmission rate, one-to-one stable matching based on mobile social connections and one-to-one stable matching based on channel state are established. The proposed strategy incorporates a heuristic optimization algorithm based on the maximum spanning tree, such that the matching results of the two tripartite graphs can reach the approximate global optimal simultaneously and further improve the service quality of vehicles. The simulation results show that the content delivery efficiency of the RATG strategy is 6.05%, 5.00%, and 10.64% higher than those of the randomized resources allocation strategy, local search strategy, and local ratio strategy, respectively, and the average transmission rate is at least 2.7% higher for the RATG strategy, relative to those of the other strategies.

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.

Blockchain enabled task offloading based on edge cooperation in the digital twin vehicular edge network

The rapid development of the Internet of Vehicles (IoV) along with the emergence of intelligent applications have put forward higher requirements for massive task offloading. Even though Mobile Edge Computing (MEC) can diminish network transmission delay and ease network congestion, the constrained heterogeneous resources of a single edge server and the highly dynamic topology of vehicular edge networks may compromise the efficiency of task offloading, including latency and energy consumption. Vehicular edge networks are also vulnerable to malicious outside attacks. In this paper, we propose a new blockchain-enabled digital twin vehicular edge network (DTVEN) where digital twin (DT) is exploited to monitor network communication, computation, and caching (3C) resources management in real time to provide rich data for offloading decision-making, and blockchain is utilized to secure fair and decentralized offloading transactions among DTs. To ensure 3C resources sharing across edge servers, we design a DT-assisted edge cooperation scheme, which makes full use of edge resources in vehicular networks. Furthermore, a DT-based smart contract is built to achieve a quick and effective consensus process. Then, we apply a task offloading algorithm based on an improved cuckoo algorithm (ICA) and a resource allocation scheme based on greedy strategy to minimize network cost by comprehensively taking into account latency and energy consumption. Numerical results demonstrate that our proposed scheme outperforms the existing schemes in terms of network cost.