Content Caching Research Articles

Adaptive Federated Deep Reinforcement Learning for Proactive Content Caching in Edge Computing

With the aggravation of data explosion and backhaul loads on 5 G edge network, it is difficult for traditional centralized cloud to meet the low latency requirements for content access. The federated learning ( <u>F</u> L)-based <u>p</u> roactive content <u>c</u> aching (FPC) can alleviate the matter by placing content in local cache to achieve fast and repetitive data access while protecting the users’ privacy. However, due to the non-independent and identically distributed (Non-IID) data across the clients and limited edge resources, it is unrealistic for FL to aggregate all participated devices in parallel for model update and adopt the fixed iteration frequency in local training process. To address this issue, we propose a distributed resources-efficient FPC policy to improve the content caching efficiency and reduce the resources consumption. Through theoretical analysis, we first formulate the FPC problem into a stacked autoencoders (SAE) model loss minimization problem while satisfying resources constraint. We then propose an adaptive FPC (AFPC) algorithm combined deep reinforcement learning (DRL) consisting of two mechanisms of client selection and local iterations number decision. Next, we show that when training data are Non-IID, aggregating the model parameters of all participated devices may be not an optimal strategy to improve the FL-based content caching efficiency, and it is more meaningful to adopt adaptive local iteration frequency when resources are limited. Finally, experimental results in three real datasets demonstrate that AFPC can effectively improve cache efficiency up to 38.4 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 6.84 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , and save resources up to 47.4 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 35.6 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , respectively, compared with traditional multi-armed bandit (MAB)-based and FL-based algorithms.

Content Collaborative Caching Strategy in the Edge Maintenance of Communication Network: A Joint Download Delay and Energy Consumption Method

With the development of Big Data technology and Internet, the surge of data in the network will cause network congestion and untimely task processing. Additionally, caching content in the core network may cause redundant access of content and backhaul bottlenecks. Due to the increasing requirements of users for task processing efficiency, the centralized maintenance system based on traditional cloud computing cannot meet the current computing requirements. In view of these problems, we propose a content collaborative caching mechanism based on joint decision of download delay and energy consumption. By integrating network coding and content caching technology, the work content maintained in the communication network is deployed near the edge of the network in the form of coding to reduce the redundant transmission of content and acquisition time of content. This article establishes a user QoE satisfaction model, which consists of two indexes that measure time delay and energy consumption. This article proposes a <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -hybrid Q-learning algorithm to optimize the placement of cache files, and made the cache action selection based on the combination of improved heuristic greedy algorithm and simulated annealing algorithm. The experimental results show that the proposed cache strategy can reduce the delay of users downloading content and the energy consumption of content cache, so as to improve the quality of field maintenance work in communication network.

Optimal and Suboptimal Dynamic Cache Update Algorithms for Wireless Cellular Networks

In this letter, we study the dynamic cache update problem for wireless content caching networks. The cost minimization perspective is considered, wherein the system cost contains three components, including the cache cost, the retrieval cost, and the update cost. Particularly, the update cost lies at the root of the coded caching strategy. To address the non-convex optimization problem, we propose both suboptimal and optimal algorithms. More specifically, we prove that with previous caching state, the cost minimization problem for the current time interval is a 0–1 Knapsack problem, which can be optimally solved by a dynamic programming with pseudo polynomial time complexity. On this basis, the original optimization problem is solved by an iterative manner, resulting in an suboptimal solution. Thereafter, we design the optimal dynamic caching policy, which is derived from the recursive algorithm. Simulation result shows that the suboptimal solution achieves near-optimal performance. It also demonstrates the superiority of our developed strategies compared to various baselines in terms of cost saving. Moreover, counterintuitive insight is judiciously acquired, such as that the high cache capacity does not always reveal positive effect on system’s performance when update cost is taken into consideration.

On Secure Uplink Transmission in Hybrid RF-FSO Cooperative Satellite-Aerial-Terrestrial Networks

This work investigates the secrecy outage performance of the uplink transmission of a radio-frequency (RF)-free-space optical (FSO) hybrid cooperative satellite-aerial-terrestrial network (SATN). Specifically, in the considered cooperative SATN, a terrestrial source (S) transmits its information to a satellite receiver (D) via the help of a cache-enabled aerial relay (R) terminal with the most popular content caching scheme, while a group of eavesdropping aerial terminals (Eves) trying to overhear the transmitted confidential information. Moreover, RF and FSO transmissions are employed over S-R and R-D links, respectively. Considering the randomness of R, D, and Eves, and employing a stochastic geometry framework, the secrecy outage performance of the cooperative uplink transmission in the considered SATN is investigated and a closed-form analytical expression for the end-to-end secrecy outage probability is derived. Finally, Monte-Carlo simulations are shown to verify the accuracy of our analysis.

Attack Resilience of Cache Replacement Policies: A Study Based on TTL Approximation

Caches are pervasively used in communication networks to speed up content access by reusing previous communications, where various replacement policies are used to manage the cached contents. The replacement policy of a cache plays a key role in its performance, and is thus extensively engineered to achieve a high hit ratio in benign environments. However, some studies showed that a policy with a higher hit ratio in benign environments may be more vulnerable to cache pollution attacks that intentionally send requests for unpopular contents. To understand the cache performance under such attacks, we analyze a suite of representative replacement policies under the framework of TTL approximation in how well they preserve the hit ratios for legitimate users, while incorporating the delay for the cache to obtain a missing content. We further develop a scheme to adapt the cache replacement policy based on the perceived level of attack. Our analysis and validation on real traces show that although no single policy is resilient to all the attack strategies, suitably adapting the replacement policy can notably improve the attack resilience of the cache. Motivated by these results, we implement selected policies as well as policy adaptation in an open-source SDN switch to manage flow rule replacement, which is shown to notably improve its resilience to pollution attacks.

Dynamic Caching for Files With Rapidly-Varying Features and Content

Proactive caching shows great potential to minimize peak download rates by caching popular data, in advance, at the edge. Fast-changing file features, such as fast-changing file popularities and fast-changing file contents (data freshness), represent a challenge for proactive caching if cache content update is much slower, which decreases the efficiency and usability of caching. We present a dynamic caching scheme that updates local user caches and optimizes the use of caching resources. The developed scheme index-code the updates with the delivery messages. The developed scheme is presented for a network with one cache-enabled server, that has a pool of files, communicating with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> cache-enabled receivers with requests limited to the server’s file pool. The developed scheme assumes partial knowledge of features variation. Asynchronous file delivery is assumed as a result of non-flexible receivers’ request timing. We show that the file delivery messages can be used to proactively and constantly update the receivers’ finite caches by index-coding the update messages with delivery messages at no additional rate-cost. We also show that this mechanism reduces the downloaded traffic and can be used to reduce other QoS metrics.

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content's early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.

Intelligent Caching for Mobile Video Streaming in Vehicular Networks with Deep Reinforcement Learning

Caching-enabled multi-access edge computing (MEC) has attracted wide attention to support future intelligent vehicular networks, especially for delivering high-definition videos in the internet of vehicles with limited backhaul capacity. However, factors such as the constrained storage capacity of MEC servers and the mobility of vehicles pose challenges to caching reliability, particularly for supporting multiple bitrate video streaming caching while achieving considerable quality of experience (QoE). Motivated by the above challenges, in this paper, we propose an intelligent caching strategy that takes into account vehicle mobility, time-varying content popularity, and backhaul capability to improve the QoE of vehicle users effectively. First, based on the mobile video mean opinion score (MV-MOS), we designed an average download percentage (ADP) weighted QoE evaluation model. Then, the video content caching problem is formulated as a Markov decision process (MDP) to maximize the ADP weighted MV-MOS. Owing to the prior knowledge of video content popularity and channel state information that may not be available at the road side unit in practical scenarios, we propose a deep reinforcement learning (DRL)-based caching strategy to solve the problem while achieving a maximum ADP weighted MV-MOS. To accelerate its convergence speed, we further integrate the prioritized experience replay, dueling, and double deep Q-network technologies, which improve the performance of DRL algorithm. Numerical results demonstrate that the proposed DRL-based caching strategy significantly improves QoE, and achieves better video delivery reliability compared to existing non-learning approaches.

A survey of mobility-aware Multi-access Edge Computing: Challenges, use cases and future directions

Many mobile and pervasive applications avail cloud services to reduce overheads in on-device computation. The performance of these services depends on the available bandwidth of the underlying network, the physical proximity of the cloud server and the end devices, the volume of data, the computational capacity of the server, and, importantly, the mobility of the devices hosting the applications. Edge computing promises to provide better performance by bringing services (e.g., a video streaming service) from the cloud to servers near the user. It also enables partial or full offloading of the computation (tasks) and storage functionalities from the User Equipment (UE) to the edge of the network. This saves power and benefits from relatively more powerful devices at the edge. Multi-access Edge Computing (MEC), which supports wireless and wired access technologies, has gained significant research interest. When UEs move, services must continue to operate, tasks may need to be offloaded again, and states related to tasks and services may need to be migrated. In this paper, we focus on four functional components (task/service offloading, resource allocation, content/task caching, and service/task migration) of MEC. We survey the challenges to these and their solutions in the context of UE mobility. Mobility creates challenges during offloading resource-intensive tasks as the user may move while the task is being offloaded. Some of the other challenges are how to jointly allocate computing and communication resources, minimize service down time during migration, and share the backhaul network if the same MEC host must continue to be used. Some key research areas include intelligent task offloading and service migration algorithms, exploiting group mobility to improve task migration time, studying the interplay of MEC parameters such as capabilities of the target MEC host, etc. In addition, predicting the mobile trajectory through intelligent methods and implementations with datasets from real-world scenarios are required.We compare this paper on 11 parameters (service migration, task offloading, resource allocation, content caching, mobility, use cases, architecture, computing paradigm, mobility model, system model, virtualization/Software Defined Networks) with 31 other survey papers from 2018 to April 2022 in MEC and related domains. We discuss the Edge Computing paradigm, the system architecture and model descriptions, and use cases. We briefly explain the relevant challenges and future directions in emerging domains, such as the Internet of drones and Digital twins. We also discuss future research directions in task/service migration, offloading, resource management, distributed computing, reliability, and Quality of Service, all related to mobility in MEC.

Mode Selection of Content Delivery at Edge Nodes Based on Learning

Pre-caching at edge nodes may improve resource efficiency for future content-centric cellular networks by bringing contents closer to users. However, which mode (muticast or unicast) should be selected for the efficient delivery of those cached contents is still not well addressed, especially at the situation that some key parameters such as content popularity and transmission environments are unknown. To solve this problem, the criterion of delivery mode selection is studied based on the learning policy of multi-armed bandit. According to the criterion, a mode selection algorithm is proposed. In this algorithm, edge nodes can choose the better delivery modes in the current slot only dependent on existing observations in the previous slot. Performance evaluation results validate our analyses and proposals on the mode selection.

Cooperative Content Precaching Scheme Based on the Mobility Information of Vehicles in Intermittently Connected Vehicular Networks

Intermittently connected vehicular networks (ICVNs) consist of vehicles moving on roads and stationary roadside units (RSUs) deployed along roads. In ICVNs, the long distances between RSUs and the large volume of vehicular content lead to long download delays to vehicles and high traffic overhead on backhaul links. Fortunately, the improved content storage size and the enhanced vehicular mobility prediction afford opportunities to ameliorate these problems by proactively caching (i.e., precaching) content. However, existing precaching schemes exploits RSUs and vehicles individually for content precaching, even though the cooperative precaching between them can reduce download delays and backhaul link traffic. Thus, this paper proposes a cooperative content precaching scheme that exploits the precaching ability of both vehicles and RSUs to enhance the performance of content downloads in ICVNs. Based on the trajectory and velocity information of vehicles, we first select the optimal relaying vehicle and the next RSUs to cache the requested content proactively and provide it to the requester vehicle optimally. Next, we calculate the optimal content precaching amount for each of the relaying vehicle and the downloading RSUs by using a mathematical model that exploits both the dwell time in an RSU and the contact time between vehicles. To compensate for the error of the mobility prediction in determining both the dwell time and the contact time, our scheme adds a guardband to the optimal content precaching amount by considering the expected reduced delay. Finally, we evaluate the proposed scheme in various simulation environments to prove the achievement of efficient content download performance by comparing with the existing schemes.

Research on application of dynamic adaptive cache strategy and artificial intelligence technology in power engineering data processing

In order to improve the effect of power engineering data processing, this paper combines dynamic adaptive caching strategy and artificial intelligence technology to study power engineering data processing. This paper proposes a mechanism for the survival time of cached content. The survival time is set according to the frequency of content requests and the distance of the content node, so that the cached content of downstream nodes close to the user has a longer time. At the same time, after the content in the upstream node expires, it can free up cache space for the rollback operation. Finally, this paper tests the system model of this paper through experimental research. The research results show that the power engineering data processing system based on dynamic adaptive caching strategy and artificial intelligence technology proposed in this paper has certain effects.

Index Coding Algorithms: Cooperative Caching and Delivery for F-RANs

In a Fog Radio Access Network (F-RAN), fog access points (F-APs) are equipped with caches that can store popular files during off-peak hours. Besides, they are densely deployed to have overlapping radio coverage so that requested files can be delivered cooperatively using beamforming. The bottleneck of the network is typically in the bandwidth-limited wireless fronthaul, which connects a cloud server to the F-APs. This work studies index coding design for cooperative caching and delivery in F-RAN to minimize fronthaul traffic and transmit energy. Index coding algorithms are designed considering the cached content at the F-APs and the possibility of beamforming in the access network under coded and uncoded caching schemes. An optimal polynomial-time index coding algorithm for uncoded and repetition caching and an efficient heuristic for Maximum Distance Separable (MDS) coded caching are designed, and their superior performance is verified by simulations. The study is further extended to consider the tradeoff between the traffic load of the fronthaul link and the transmit energy consumed in the access network. At the expense of more fronthaul traffic, beamforming opportunities can be increased, significantly reducing energy consumption. Algorithms to achieve the tradeoff are crafted, and simulation results show that uncoded caching well balances the tradeoff.

Maximizing the Capacity of Edge-Caching Networks With User-Content Evolution Relationship

System capacity is an important metric for evaluating the performance of edge-caching networks. In most previous studies, the caching content was assumed to remain unchanged in a base station. Hence, the requested probability for each content was treated as stable; In fact, both the number of content and users can change overtime, which impacts the system performance. This article firstly constructs a new user-content evolution model to infer such performance changes, and analyzes the number of links between users and content (i.e., degree). Then, the requested probability of the content and the optimal copy number are calculated by using the content degree. Next, the minimum average transmission distance of the content and system capacity under the cache scheme are given. Finally, a user-content time-varying system is simulated. The results show that the proposed model can characterize the evolution relationship between users and content and achieve better performance in terms of system capacity.

A hybrid caching strategy for information-centric satellite networks based on node classification and popular content awareness

Low Earth Orbit (LEO) satellite networks are inexpensive to deploy and have wide coverage, and are widely available for differentiated content distribution services. With the development of on-board storage and computing capabilities, in-network caching technology in information centric networking (ICN) has proven to be an effective way to increase the throughput and content distribution efficiency of satellite networks. However, traditional caching and distribution schemes, which do not take into account the high-speed motion of satellite nodes and dynamic changes in topology, are not applicable to satellite networks. To address these issues, a hybrid caching strategy for satellite networks based on node classification and popular content awareness (NCPCA) is proposed. Firstly, the time slot partitioning method based on interlayer similarity is proposed to transform the dynamically changing process into a set of time slots with stable topology. Next, the satellite nodes are dynamically divided into two categories by fully considering the changes in connection relationships and interaction order during the spatial–temporal evolution of the satellite nodes. Nodes with satellite topological and functional characteristics are screened out as core nodes with TOPSIS algorithm, and the remaining nodes are regarded as edge nodes. Finally, a probabilistic caching scheme based on content popularity is adopted with core nodes as caching nodes to ensure caching performance and promote the diversity of cached content. Simulation results show that this strategy can effectively improve cache hit rate, reduce user request delay and content fetching hops, and promote the stable operation of satellite networks compared with other caching strategies.

A Cross-Layer Optimization Framework for Index-Coded NOMA in Cache-Aided F-RANs

This paper studies cached-aided multicast transmissions in fronthaul fog radio access networks (F-RANs). While index coding and cached-aided non-orthogonal multiple access (NOMA) are techniques commonly used for utilizing cache contents to save transmit energy, there is a lack of general framework to integrate them. This work proposes index-coded NOMA and dynamic coded-NOMA to investigate energy performance of the integration of index coding and NOMA under whole-file and subfile caching, respectively. Besides, dynamic cache space allocation is applied to both caching schemes, which allocates cache sizes to the fog access points (F-APs) according to their large-scale channel conditions. For index-coded NOMA, the general grouping problem is proved to be NP-hard and optimal solutions for some special cases are given. Furthermore, efficient heuristic grouping algorithms are proposed. For dynamic coded-NOMA, we obtain the closed-form minimum transmit energy. The numerical results validate the good performance of our proposed algorithms. Index-coded NOMA and dynamic coded-NOMA have comparable performance and both of them save much energy than the existing schemes. When there are 12 F-APs under small-cache scenarios, index-coded NOMA saves energy by 70.3% compared to traditional NOMA.

A DRL-Driven Intelligent Optimization Strategy for Resource Allocation in Cloud-Edge-End Cooperation Environments

Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions.

SACC: A Size Adaptive Content Caching Algorithm in Fog/Edge Computing Using Deep Reinforcement Learning

Edge/Fog caching is promising to mitigate the data traffic problem in both traditional wireline/wireless networks and the 5G network. Recently, deep reinforcement learning (DRL) has been adopted to provide a more powerful content caching policy. The current DRL-based scheme considers the requests for the same size and updates the caching for each request. However, the real-world data delivery systems usually refresh the content cache periodically, with different sizes of requests. To satisfy the real-world requirements, this study proposes a novel size adaptive content caching algorithm using DRL, termed SACC. SACC models the requests with random sizes and updates the cache after a batch of requests. Technically, SACC utilizes the Actor-Critic framework, which is able to process large discrete action space. SACC comprehensively considers the short-, medium- and long-term requests as the state to train the actor network. The reward is modeled as the cache hit rate. Once an action is selected from the policy network, it is expended to its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> nearest neighbors. The critic network finds the action with the best reward from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> actions. The performance of the proposed SACC is evaluated through computer simulation. The experimental results showed that SACC could train the network much more efficiently and improve the cache hit rate by as much as 4% when comparing to the state-of-art DRL-based scheme.

Energy-efficient proactive caching with multipath routing

The ever-continuing explosive growth of on-demand content distribution has imposed great pressure on mobile/wireless network infrastructures. To ease congestion in the network and to increase perceived user experience, caching of popular content closer to the end-users can play a significant role and as such this issue has received significant attention over the last few years. Additionally, energy saving is treated as a fundamental requirement in the design of next-generation mobile networks. However, there has been little attention to the overlapping area between energy saving and network caching especially when considering multipath routing. To this end, this paper proposes an energy-efficient caching with multipath routing support. The proposed scheme provides a joint anchoring of popular content into a set of potential caching nodes with optimized multipath support whilst ensuring a balance between transmission and caching energy cost. The proposed model also considers different content delivery modes, such as multicast and unicast . Two separated Integer-Linear Programming (ILP) models are formulated for each delivery mode. To tackle the curse of dimensionality we then provide a greedy simulated annealing algorithm , which not only reduces the time complexity but also provides a competitive performance. A wide set of numerical investigations reveal that the proposed scheme reduces the energy consumption up to 75% compared with other widely used caching approaches under the premise of network resource limitation. Sensitivity analysis to different parameters is also meticulously discussed in this paper.

Uncoded Caching and Cross-Level Coded Delivery for Non-Uniform File Popularity

Proactive content caching at user devices and coded delivery is studied for a non-uniform file popularity distribution. A novel centralized uncoded caching and coded delivery scheme, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cross-level coded delivery (CLCD)</i> , is proposed, which can be applied to large file libraries under non-uniform demands. In the CLCD scheme, the same sub-packetization is used for all the files in the library in order to prevent additional zero-padding in the delivery phase, and unlike the existing schemes in the literature, users requesting files from different popularity groups can still be served by the same multicast message in order to reduce the delivery rate. Simulation results indicate more than 10% reduction in the average delivery rate for typical Zipf distribution parameter values.