Content Caching Research Articles

Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

Energy-Efficient Green Information Centric Networking for Future Wireless Communications

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content.

Optimizing proactive content caching with mobility aware deep reinforcement & asynchronous federate learning in VEC

Edge caching in the Internet of Vehicles (IoV) can reduce backhaul strain and content access delay. However, due to the constant changes in vehicle requests, offloading applications to edge servers is crucial for efficiently anticipating and caching popular content. Additionally, conventional data-sharing techniques are inadequate for this task due to their inability to preserve the privacy of vehicular users (VU). To overcome these issues, we propose a cooperative proactive content caching system incorporating Asynchronous federated learning and Deep reinforcement learning named PCAD that leverages the strengths of Dueling Deep Q-Networks and Prioritized Experience Replay in vehicular edge computing. PCAD lowers the latency of content access by prefetching contents that are popular beforehand caching them on edge nodes and cutting the waiting time for every vehicle to complete training as well as uploading local models before updating the global model. Additionally, we investigate intelligent caching decisions based on content prediction. Comprehensive experimental evaluations indicate that our proposed approach significantly outperforms existing benchmark caching techniques. More specifically, our suggested approach works better than DDQN, c-ϵ-greedy, and PCAD without DRL methods and the cache hit rate improves by approximately 4.25%, 11.23%, and 25.82%, respectively, as the cache capacity hits 400 MB.

Content delivery network solutions for the CMS experiment: The evolution towards HL-LHC

The Large Hadron Collider at CERN in Geneva is poised for a transformative upgrade, preparing to enhance both its accelerator and particle detectors. This strategic initiative is driven by the tenfold increase in proton-proton collisions anticipated for the forthcoming high-luminosity phase scheduled to start by 2029. The vital role played by the underlying computational infrastructure, the World-Wide LHC Computing Grid, in processing the data generated during these collisions underlines the need for its expansion and adaptation to meet the demands of the new accelerator phase. The provision of these computational resources by the worldwide community remains essential, all within a constant budgetary framework. While technological advancements offer some relief for the expected increase, numerous research and development projects are underway. Their aim is to bring future resources to manageable levels and provide cost-effective solutions to effectively handle the expanding volume of generated data. In the quest for optimized data access and resource utilization, the LHC community is actively investigating Content Delivery Network (CDN) techniques. These techniques serve as a mechanism for the cost-effective deployment of lightweight storage systems that support both, traditional and opportunistic compute resources. Furthermore, they aim to enhance the performance of executing tasks by facilitating the efficient reading of input data via caching content near the end user. A comprehensive study is presented to assess the benefits of implementing data cache solutions for the Compact Muon Solenoid (CMS) experiment. This in-depth examination serves as a use-case study specifically conducted for the Spanish compute facilities, playing a crucial role in supporting CMS activities. Data access patterns and popularity studies suggest that user analysis tasks benefit the most from CDN techniques. Consequently, a data cache has been introduced in the region to acquire a deeper understanding of these effects. In this paper, the details of the implementation of a data cache system in the PIC Tier-1 compute facility are presented. It includes insights into the developed monitoring tools and discusses the positive impact on CPU usage for analysis tasks executed in the region. The study is augmented by simulations of data caches, with the objective of discerning the most optimal requirements in both size and network connectivity for a data cache serving the Spanish region. Additionally, the study delves into the cost benefits associated with deploying such a solution in a production environment. Furthermore, it investigates the potential impact of incorporating this solution into other regions of the CMS computing infrastructure.

Effects of mound building and caching by steppe mouse (Mus spicilegus Petényi) on the vegetation in agroecosystems

Several rodent species are considered ecosystem engineers. They exert profound changes in agroecosystems by disturbing the soil during their activities. The steppe mouse (Mus spicilegus) inhabits various agroecosystems and constructs conspicuous mounds for overwintering using piled up plant material and soil. These mounds are widespread in many agroecosystems and may significantly affect the vegetation. In our study, we evaluated the effect of mound building activity of the steppe mouse on the vegetation of agroecosystems in Hungary. We sampled the cache content of 90 mounds in total located in old fields, alfalfa fields, and annual crop fields, and surveyed the aboveground vegetation of the mounds and their surrounding undisturbed matrix in 39 paired plots. Mice cached large amounts of seeds belonging mostly to weeds. In total we found 50,413 germinable seeds of 30 species in the cache content samples. However, the mound vegetation and the cache shared only a few species, suggesting that seeds cached by the mice do not contribute to the regenerating vegetation on the mounds. Soil disturbance by mice created distinct vegetation patches with species composition and structure different from the neighbouring undisturbed matrix. Early secondary successional vegetation patches on mounds introduced small-scale heterogeneity into the homogenous agricultural landscape, increased plant diversity and provided distinct flower resources for pollinators. The detected differences in the aboveground vegetation between the mound and the matrix in the studied habitats suggest that the steppe mouse acts as a facultative engineer species in agroecosystems.

Learning-based cooperative content caching and sharing for multi-layer vehicular networks

Caching and sharing the content files is critical and fundamental for various future vehicular applications. However, how to satisfy the content demands in a timely manner with limited storage is an open issue owing to the high mobility of vehicles and the unpredictable distribution of dynamic requests. To better serve the requests from the vehicles, a cache-enabled multi-layer architecture, consisting of a Micro Base Station (MBS) and several Small Base Stations (SBSs), is proposed in this paper. Considering that vehicles usually travel through the coverage of multiple SBSs in a short time period, the cooperative caching and sharing strategy is introduced, which can provide comprehensive and stable cache services to vehicles. In addition, since the content popularity profile is unknown, we model the content caching problems in a Multi-Armed Bandit (MAB) perspective to minimize the total delay while gradually estimating the popularity of content files. The reinforcement learning-based algorithms with a novel Q-value updating module are employed to update the caching files in different timescales for MBS and SBSs, respectively. Simulation results show the proposed algorithm outperforms benchmark algorithms with static or varying content popularity. In the high-speed environment, the cooperation between SBSs effectively improves the cache hit rate and further improves service performance.

A vehicular edge computing content caching solution based on content prediction and D4PG

A vehicular edge computing content caching solution based on content prediction and D4PG

Optimization of IoT perceived content caching in F-RANs: Minimum retrieval delay and resource extension with performance sensitivity

In the Internet of Things (IoT) perceived applications of monitoring the states of the environment, a feasible technology is to use fog radio access networks (F-RANs) to alleviate the problems of long response time and cloud server bottlenecks in cloud computing. In response to the above problems, this work investigates the problem of minimizing the retrieval delay of IoT contents in F-RANs under the constraints of system resources. The problem is formulated as an integer linear programming (ILP) model. Then, a polynomial-time method with linear programming (LP) relaxation and rounding is proposed to approximate the optimal solution of the problem. Through proof, the method can obtain a feasible solution with a bounded approximation ratio in polynomial time. The conducted simulations validate that the obtained feasible solution is very close to the optimal one. On the other hand, when the system resources are not enough to meet the continuous growth of content retrieval and need to be expanded, this work further establishes an association relation between cached contents and system resources. Based on the above relation, the second method of expanding system resources with performance sensitivity is proposed to provide the service provider with an effective and economical expansion of system resources. It utilizes a predefined system parameter in balancing the trade-off between the approximation ratio to the optimal solution of the problem and the extended system resources. The solution obtained by the second method is also proved to have a bounded approximation ratio.

DRL-based Content Caching Strategy With Efficient User Preference Predictions in UAV-assisted VEC

DRL-based Content Caching Strategy With Efficient User Preference Predictions in UAV-assisted VEC

Towards Analysing Cache-Related Preemption Delay in Non-Inclusive Cache Hierarchies

The impact of preemptions has to be considered when determining the schedulability of a task set in a preemptively scheduled system. In particular, the contents of caches can be disturbed by a preemption, thus creating context-switching costs. These context-switching costs occur when a preempted task needs to reload data from memory after a preemption. The additional delay created by this effect is termed cache-related preemption delay (CRPD). The analysis of CRPD has been extensively studied for single-level caches in the past. However, for two-level caches, the analysis of CRPD is still an emerging area of research. In contrast to a single-level cache, which is only affected by direct preemption effects, the second-level cache in a two-level hierarchy can be subject to indirect interference after a preemption. Accesses that could be served from the L1 cache in the absence of preemptions, may be forwarded to the L2 cache, as the relevant data was evicted by a preemption. These accesses create the indirect interference in the L2 cache and can cause further evictions. Recently, a CRPD analysis for two-level non-inclusive cache hierarchies was proposed. In this article, we show that this state-of-the-art analysis is unsafe as it potentially underestimates the CRPD. Furthermore, we show that the analysis is pessimistic and can overestimate the indirect preemption effects. To address these issues, we propose a novel analysis approach for the CRPD in a two-level non-inclusive cache hierarchy. We prove the correctness of the presented approach based on the set of feasible program execution traces. We implemented the presented approach in a worst-case execution time (WCET) analysis tool and compared the performance to existing analysis methods. Our evaluation shows that the presented analysis increases task set schedulability by up to 14 percentage points compared with the state-of-the-art analysis.

Capacity and delay performance analysis for large-scale UAV-enabled wireless networks

In this paper, we analyze the capacity and delay performance of a large-scale Unmanned Aerial Vehicle (UAV)-enabled wireless network, in which untethered and tethered UAVs deployed with content files move along with mobile Ground Users (GUs) to satisfy their coverage and content delivery requests. We consider the case where the untethered UAVs are of limited storage, while the tethered UAVs serve as the cloud when the GUs cannot obtain the required files from the untethered UAVs. We adopt the Ornstein-Uhlenbeck (OU) process to capture the mobility pattern of the UAVs moving along the GUs and derive the compact expressions of the coverage probability and capacity of our considered network. Using tools from martingale theory, we model the traffic at UAVs as a two-tier queueing system. Based on this modeling, we further derive the analytical expressions of the network backlog and delay bounds. Through numerical results, we verify the correctness of our analysis and demonstrate how the capacity and delay performance can be significantly improved by optimizing the percentage of the untethered UAVs with cached contents.

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.

Vehicular edge cloud computing content caching optimization solution based on content prediction and deep reinforcement learning

In conventional studies on vehicular edge computing, researchers frequently overlook the high-speed mobility of vehicles and the dynamic nature of the vehicular edge environment. Moreover, when employing deep reinforcement learning to address vehicular edge challenges, insufficient attention is given to the potential issue of the algorithm converging to a local optimal solution. This paper presents a content caching solution tailored for vehicular edge cloud computing, integrating content prediction and deep reinforcement learning techniques. Given the swift mobility of vehicles and the ever-changing nature of the vehicular edge environment, the study proposes a content prediction model based on Informer. Leveraging the Informer prediction model, the system anticipates the vehicular edge environment dynamics, thereby informing the caching of vehicle task content. Acknowledging the diverse time scales involved in policy decisions such as content updating, vehicle scheduling, and bandwidth allocation, the paper advocates a dual time-scale Markov modeling approach. Moreover, to address the local optimality issue inherent in the A3C algorithm, an enhanced A3C algorithm is introduced, incorporating an ɛ-greedy strategy to promote exploration. Recognizing the potential limitations posed by a fixed exploration rate ɛ, a dynamic baseline mechanism is proposed for updating ɛ dynamically. Experimental findings demonstrate that compared to alternative content caching approaches, the proposed vehicle edge computing content caching solution substantially mitigates content access costs. To support research in this area, we have publicly released the source code and pre-trained models at https://github.com/JYAyyyyyy/Informer.git.

Cooperative edge-caching based transmission with minimum effective delay in heterogeneous cellular networks

In heterogeneous cellular networks (HCNs), neighboring users often request similar contents asynchronously. Based on the content popularity, base stations (BSs) can download and cache contents when the network is idle, and transmit them locally when the network is busy, which can effectively reduce the backhaul burden and the transmission delay. We consider a two-tier HCN, where macro base stations (MBSs) and small base stations (SBSs) can cooperatively and probabilistically cache contents. Each user is associated to the BS with the maximum average received signal power in any tier. With the cooperative content transfer between MBS tier and SBS tier, users can adaptively obtain contents from BSs or remote content servers. We properly model both wired and wireless delays when a user requests an arbitrary content, and propose the concept of effective delay. Content caching probabilities are optimized using the Marine Predators Algorithm via minimizing the average effective delay. Numerical results show that our proposed cooperative caching scheme achieves much shorter delays than the benchmark caching schemes.

LPCE-Based Replacement Scheme for Enhancing Caching Performance in Named Data Networking

The substantial surge in users has adversely impacted the performance of the present IP-based Internet. Named data networking (NDN) emerges as a future alternative, given its distributed content caching system, where data can be cached in multiple routers and retrieved from the closest one instead of the original producer, enhancing content availability, reducing latency, and minimizing data loss. This paper introduces the less popular content eviction (LPCE) policy, a novel cache replacement scheme designed to enhance the caching performance of the conventional LFU (least frequently used) policy in NDN routers, thereby improving overall network efficiency. The proposed method subsumes LFU and FIFO (first in first out) policies and employs an additional list controlled by the LRU (least recently used) policy. Utilizing the ccnSim simulator, we conduct a comparison of LPCE’s performance with that of the LFU technique and other competing caching techniques, considering variations in several simulation parameters. Experimental results reveal that the proposed LPCE algorithm excels over others across a majority of performance metrics, such as cache hit ratio, content delivery delay, upstream hop count, network traffic, and producers’ load. Besides, the findings indicate that LPCE outperforms LFU, with an increase in cache hit ratio ranging from 1.32% to 5.75%.

Performance analysis of CSI independent cache-aided NOMA ordering

This paper introduces a Non-Orthogonal Multiple Access (NOMA) ordering technique, called the region-based ordering method, which operates independently of channel state information (CSI) for vehicular-to-infrastructure (V2I) networks. Caching has been Integrated with NOMA to significantly enhance outage probability, ergodic sum rate, and latency performance compared to traditional NOMA systems. The performance of the proposed method is evaluated under various cache content availability scenarios and compared to dynamic ordered NOMA, which relies on instantaneous CSI for user ordering. A closed-form analytical expression for outage probability is derived for a Rayleigh fading channel and validated through Monte Carlo simulations. The analysis demonstrates that the proposed method is as effective as the dynamic ordering method. Additionally, cache-aided NOMA significantly improves outage performance and reduces delivery delay compared to conventional NOMA.

Trailblazer: A proactive caching, blockchain, minimum content retrieval latency, and energy cost-aware resource and job orchestration scheme for publisher-subscriber based 6G ICN services

With the rise of information-centric networks (ICN), the user can access the caching content from nearby caching nodes rather than the remote content server through in-network caching facilities. The existing articles did not look into minimum caching content retrieval latency, user energy consumption, user financial cost, and maximum service provider profit-aware proper resource orchestration and job scheduling policies all at once, taking into account multiple publishers and subscriber-based caching jobs, blockchain, user-owned and service provider cache, pre-caching, neighbor collaboration, and available resources. To suppress these challenges, this article offers a proactive pre-caching, collaboration, blockchain, minimum latency-aware resource, and job scheduling policy for 6G ICN services that takes into account publishers’ and subscribers’ caching job requests, user-owned and service provider cache, available resources, and deadline. The experimental results visualized that at least 16% content retrieval delay, 66% energy, and 22% service provider profit gain are attained in the proposed scheme over the existing techniques.

Optimal Push and Pull-Based Edge Caching for Dynamic Content

Optimal Push and Pull-Based Edge Caching for Dynamic Content

An Approach to Secure Data Sharing in the Internet of Things Using Blockchain- Based Proxy Re-Encryption

The prevalence of data sharing has increased with the rise in popularity of cloud computing and the Internet of Things (IoT). However, ensuring data security is difficult because of the possible negative consequences of illegal data usage. This study presents a method for ensuring the secure transmission of data in cloud environments: proxy re-encryption. The identity-based encryption method enables data owners to transfer encrypted data to the cloud. Subsequently, a proxy re-encryption framework enables authorized individuals to access the data. To surpass the limitations of IoT device resources, we utilize an edge device as a proxy server to transfer computationally intensive tasks to a separate device. By utilizing the advantages of information-centric networking, we can optimize network capacity and enhance service quality by efficiently distributing cached content across the proxy. This method decreases bottlenecks in centralized systems while providing precise control over data access. The proposed work demonstrates the effectiveness of the strategy in ensuring data security, confidentiality, and integrity through thorough security research and plan review.

Task Partition-Based Computation Offloading and Content Caching for Cloud–Edge Cooperation Networks

With the increasing complexity of applications, many delay-sensitive and compute-intensive services have posed significant challenges to mobile devices. Addressing how to efficiently allocate heterogeneous network resources to meet the computing and delay requirements of terminal services is a pressing issue. In this paper, a new cooperative twin delayed deep deterministic policy gradient and deep-Q network (TD3-DQN) algorithm is introduced to minimize system latency by optimizing computational offloading and caching placement asynchronously. Specifically, the task-partitioning technique divides computing tasks into multiple subtasks, reducing the response latency. A DQN intelligent algorithm is presented to optimize the offloading path to edge servers by perceiving network resource status. Furthermore, a TD3 approach is designed to optimize the cached content in the edge servers, ensuring dynamic popularity content requirements are met without excessive offload decisions. The simulation results demonstrate that the proposed model achieves lower latency and quicker convergence in asymmetrical cloud–edge collaborative networks compared to other benchmark algorithms.