Optimal Caching Research Articles

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.

Caching Placement Optimization in UAV-Assisted Cellular Networks: A Deep Reinforcement Learning-Based Framework

Capable of delivering contents offloaded from the base station (BS) to users, unmanned aerial vehicle (UAV) has emerged as a crucial leverage to compensate for terrestrial BSs-based communication. However, the limited storage capacity of the UAV brings challenges to providing low-latency services for users. In this paper, we investigate the caching placement of the UAV for enhancing the timeliness of services. To overcome the unknown content popularity, proximal policy optimization (PPO) is adopted in the proposed algorithm. To be specific, we first propose a modified timeliness model, named effective age of information (EAoI), to comprehensively evaluate the timeliness of services. Then, we employ PPO to build a deep reinforcement learning framework for finding the optimal caching strategy adaptively. Extensive simulation results are provided to verify the superiority of the proposed scheme, in comparison with the conventional schemes.

Joint Optimization of Computation, Communication and Caching in D2D-Assisted Caching-Enhanced MEC System

In the era of intelligent applications, Mobile Edge Computing (MEC) is emerging as a promising technology that provides abundant resources for mobile devices. However, establishing a direct connection to the MEC server is not always feasible for certain devices. This paper introduces a novel Device-to-Device (D2D)-assisted system to address this challenge. The system leverages idle helper devices to execute and offload tasks to the MEC server, thereby enhancing resource utilization and reducing offload time. To further minimize offloading time for latency-sensitive tasks, this paper incorporates edge caching. The problem is formulated by jointly optimizing computation, communication and caching, and a novel Joint Multiple Decision Optimization Algorithm (JMDOA) is proposed to solve the minimum-energy-consumption problem. Specifically, the JMDOA algorithm decomposes the integer-mixed non-convex optimization problem into two subproblems based on distinct properties of discrete variables. These subproblems are solved separately and optimized iteratively, ensuring convergence to a suboptimal solution. Simulations demonstrate the effectiveness and superiority of JMDOA, exhibiting lower energy consumption and reduced time compared to other baseline algorithms, approaching the optimum. This work contributes to the field by presenting a novel approach to optimizing resource allocation in MEC systems, with potential implications for the future development of intelligent applications.

Vehicle clustering based edge caching scheme in internet of vehicles

AbstractWith the development of mobile edge computing technology, edge caching provides a feasible solution for low‐latency and high‐reliability data forwarding in the Internet of Vehicles (IoV). Due to the limited number and storage capacity of the edge servers, and the limited communication range of a single vehicle, the delay and connection requirements are hard to be satisfied. A vehicle clustering based edge caching scheme is proposed in this paper, aiming at minimizing the content acquisition delay of the requesting vehicles (RVs) within the communication range of the edge servers. After getting the optimal vehicle association results, the minimization of content acquisition delay is transformed into a knapsack problem, which is then solved by a dynamic programming algorithm to obtain the optimal cache decisions. Simulation results show that the proposed scheme reduces task acquisition delay by at least about 100 s and increases the cache hit rate by about 50%, compared with the random cache scheme.

Cache-Aided MEC for IoT: Resource Allocation Using Deep Graph Reinforcement Learning

With the growing demand for latency-sensitive and compute-intensive services in the Internet of Things (IoT), multi-access edge computing (MEC)-enabled IoT is envisioned as a promising technique that allows network nodes to have computing and caching capabilities. In this paper, we propose a cache-aided MEC (CA-MEC) offloading framework for joint optimization of communication, computing, and caching (3C) resources in the MEC-enabled IoT. Our goal is to optimize the offloading decision and resource allocation strategy to minimize the system latency subject to dynamic cache capacities and computing resource constraints. We first formulate this optimization problem as a multi-agent decision problem, a partially observable Markov decision process (POMDP). Then, the deep graph convolution reinforcement learning (DGRL) method is applied to motivate the agents to learn optimal strategies cooperatively in a highly dynamic environment. Simulations show that our method is highly effective for computation offloading and resource allocation and performs superior results in a large-scale network.

Cache-Enabled UAV Emergency Communication Networks: Performance Analysis With Stochastic Geometry

The unmanned aerial vehicles (UAVs) are able to assist damaged cellular networks as aerial base stations (ABSs) due to their flexibility and affordability. This paper investigates the cache-enabled UAV emergency communication networks in the post-disaster area using stochastic geometry approaches. We propose a cache-enabled UAV emergency communication network framework in which a limited number of ABSs are deployed overlaid the partially destructed cellular network. The ABSs are equipped with cache units that adopt probabilistic caching strategy. We design a content-centric association strategy within the specified circular cache area by comprehensively considering the cache status and channel conditions. Based on the framework, we derive the exact expressions of the offloading probability and average rate under the general channel model taking line-of-sight (LOS)/ non-line-of-sight (NLoS) path loss and Nakagami-m fading into account. Simulation results demonstrate that there exist optimal values for ABSs number, ABSs altitude, and cache serving radius to maximize the offloading probability and average rate. Moreover, increasing ABSs number and the cache serving radius in the case that ABSs are deployed at the relatively low altitude is beneficial to improve the system performance. A large number of ABSs helps to reduce the optimal cache serving radius for maximizing the system performance.

Joint Content Caching and Recommendation in Opportunistic Mobile Networks Through Deep Reinforcement Learning and Broad Learning

Edge caching has been a research hotspot of the Mobile Edge Computing (MEC) in recent years, which is an effective way to ease the burden of traffic in cellular networks. It places contents to the edge of the networks and assists contents transmission via Device-to-Device (D2D) links. Traditional caching strategies strictly depend on the personal preferences of users, and they are scarcely possible to reduce the transmission cost while ensuring the high effectiveness. Fortunately, recent studies have found that the combination of caching and recommendation can effectively improve the efficiency of edge caching and reduce the transmission cost. In this paper, we jointly consider the content caching and recommendation through Opportunistic Mobile Networks (OMNs) to reduce the cost of Content Service Center (CSC). In order to obtain the optimal caching and recommendation solutions with sparse rating matrix, we propose a Joint Content Caching and Recommender System (JCCRS). In JCCRS, a Broad Incremental Learning based Collaborative Filtering algorithm, named BILCF, is first proposed to predict the missing ratings. Afterwards, we quantify the relationship between each pair of Mobile Users (MUs) according to their mobility, similarity and preference. The content caching and recommendation problem is then modeled as a Non-Linear Integer Programming (NLIP) problem and we prove that it belongs to NP-hard. To solve this problem, a Deep Deterministic Policy Gradient (DDPG) based Content Caching and Recommendation method, named DCRM, is further proposed to obtain the approximate optimal solutions. Extensive experiments on both a realistic dataset and a synthetic dataset validated by the realistic data show that the proposed algorithms outperform other baseline methods under different scenarios.

Proactive Content Caching Scheme in Urban Vehicular Networks

Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities and extreme complexity of business scenarios pose an enormous challenge to content caching and distribution in vehicular networks. To tackle this problem, this paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework. Firstly, we profoundly analyze the spatio-temporal correlation between the historical vehicle trajectory of user requests and construct the system model to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Meanwhile, we probe into privacy protection strategies to realize privacy-preserved prediction model. Furthermore, based on trajectory and popular content prediction results, content caching strategy is studied, and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Finally, simulations are provided to verify the superiority of our proposed scheme and algorithms. It shows that the proposed algorithms effectively improve the performance of the considered system in terms of hit ratio and average delay, and narrow the gap to the optimal caching scheme comparing with the traditional schemes.

Device–Edge Collaborative Differentiated Data Caching Strategy Toward AIoT

Caching AIoT data at the edge can reduce the load on cloud centers while providing real-time services for AIoT users. Existing static caching strategies based on popularity prediction fail to meet users’ demands for time-varying entity data, while dynamic caching strategies focus only on evaluating the time-varying state characteristics of entity data, but ignore the differences in popularity among entities, resulting in poor service experience. To this end, a device-edge collaborative differentiated data caching strategy considering static entity popularity as well as dynamic system state is proposed. First, a hot entity recognition method centered on user interests is designed to achieve individual preference estimation by mining users’ long short-term interests, and then achieve group interest prediction based on social computing. Based on this, a dynamic caching optimization method is designed, which considers the timeliness of entity data and communication cost of system to design the objective function of optimal cache decision and then solve it based on reinforcement learning. Simulation results demonstrate that the proposed caching strategy achieves better performance than other benchmark strategies in terms of cache hit rate and search cost.

U-slot Loaded Half-Circled Microstrip Patch Antenna Analysis Using XGBOOST Machine Learning Algorithm

Half-circled U-slot loaded antenna is studied using the HFSS and Machine Learning (ML) algorithm. The proposed work is for predicting the resonance frequency of the U-slot loaded antenna by providing the dimensions of the antennas.The proposed antenna is designed for Wi-MAX application with operating frequency of 3.4 GHz.The HFSS tool is being used for designing and analyzing fractal antennas and generating the training data. Parametric analysis of the designed U-slot-loaded half-circled antenna is developed by altering the half-circle radius, length of the U-slot and width. The data set is then given to the XGBoost ML algorithm for training the model. The XGBoost contains remarkably high processing speed and contains features like parallelization, cache optimization, and out-of-core computation which makes the perfect algorithm for predicting the resonance frequencies.U-slot loaded half-circled antenna offers a substantial size reduction, a wide impedance bandwidth, and a uniform radiation pattern on all sides.

Energy Efficient Joint Computation Offloading and Service Caching for Mobile Edge Computing: A Deep Reinforcement Learning Approach

Mobile Edge Computing (MEC) meets the delay requirements of emerging applications and reduces energy consumption by pushing cloud functions to the edge of the networks. Service caching is to cache application services and related databases at Edge Servers (ESs) in advance, and then ESs can process the relevant computation tasks. Due to the limited resources in the ESs, how to determine an effective service caching strategy is very crucial. In addition, the heterogeneity of ESs makes it impossible to make full use of the computing and caching resources without considering the collaboration among ESs. This paper considers a joint optimization of computation offloading, service caching, and resource allocation in a collaborative MEC system with multi-users, and formulates the problem as Mixed-Integer Non-Linear Programming (MINLP) which aims at minimizing the long-term energy consumption of the system. To solve the optimization problem, a Deep Deterministic Policy Gradient (DDPG) based algorithm is proposed for determining the strategies of computation offloading, service caching, and resource allocation. Simulation results demonstrate that the proposed DDPG based algorithm can reduce the long-term energy consumption of the system greatly, and can outperform some other benchmark algorithms under different scenarios.

Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees

Representation learning over dynamic graphs is critical for many real-world applications such as social network services and recommender systems. Temporal graph neural networks (T-GNNs) are powerful representation learning methods and have achieved remarkable effectiveness on continuous-time dynamic graphs. However, T-GNNs still suffer from high time complexity, which increases linearly with the number of timestamps and grows exponentially with the model depth, causing them not scalable to large dynamic graphs. To address the limitations, we propose Orca, a novel framework that accelerates T-GNN training by non-trivially caching and reusing intermediate embeddings. We design an optimal cache replacement algorithm, named MRU, under a practical cache limit. MRU not only improves the efficiency of training T-GNNs by maximizing the number of cache hits but also reduces the approximation errors by avoiding keeping and reusing extremely stale embeddings. Meanwhile, we develop profound theoretical analyses of the approximation error introduced by our reuse schemes and offer rigorous convergence guarantees. Extensive experiments have validated that Orca can obtain two orders of magnitude speedup over the state-of-the-art baselines while achieving higher precision on large dynamic graphs.

Toward Efficient Homomorphic Encryption for Outsourced Databases through Parallel Caching

Many applications deployed to public clouds are concerned about the confidentiality of their outsourced data, such as financial services and electronic patient records. A plausible solution to this problem is homomorphic encryption (HE), which supports certain algebraic operations directly over the ciphertexts. The downside of HE schemes is their significant, if not prohibitive, performance overhead for data-intensive workloads that are very common for outsourced databases, or database-as-a-serve in cloud computing. The objective of this work is to mitigate the performance overhead incurred by the HE module in outsourced databases. To that end, this paper proposes a radix-based parallel caching optimization for accelerating the performance of homomorphic encryption (HE) of outsourced databases in cloud computing. The key insight of the proposed optimization is caching selected radix-ciphertexts in parallel without violating existing security guarantees of the primitive/base HE scheme. We design the radix HE algorithm and apply it to both batch- and incremental-HE schemes; we demonstrate the security of those radix-based HE schemes by showing that the problem of breaking them can be reduced to the problem of breaking their base HE schemes that are known IND-CPA (i.e. Indistinguishability under Chosen-Plaintext Attack). We implement the radix-based schemes as middleware of a 10-node Cassandra cluster on CloudLab; experiments on six workloads show that the proposed caching can boost state-of-the-art HE schemes, such as Paillier and Symmetria, by up to five orders of magnitude.

Joint optimization of recommendation and caching based on user preference prediction

AbstractThe development of the Internet of things brings exponential growth of wireless traffic, which puts great pressure on the backhaul link. The proactive caching of some contents in the edge device of mobile network can effectively reduce the repeated transmission of the same contents and relieve the burden of the backhaul link. Moreover, the introduction of recommendation mechanisms can reshape user's request and improve cache hit ratio. However, the optimization of recommendation and caching decisions is highly dependent on the users’ preference information for files. Here, a joint optimization algorithm of recommendation and caching based on users’ preference prediction with multiple base stations cooperative caching is proposed. To improve the caching efficiency, the Deep Crossing model is adopted to predict users’ preferences. Under the constraints of cache capacity, recommendation quantity and bandwidth, an optimization problem to minimize the total transmission delay of the system is formulated. Then, the NP‐hardness of the proposed optimization problem is proved and it is decoupled it into three sub‐problems, namely recommendation, user access and caching optimization sub‐problems. Simulation results show that the proposed algorithm can effectively reduce the total transmission delay of the system.

Joint Edge Computing and Caching Based on D3QN for the Internet of Vehicles

With the Internet of Vehicles (IOV), a lot of self-driving vehicles (SDVs) need to handle a variety of tasks but have very seriously limited computing and storage resources, meaning they cannot complete intensive tasks timely. In this paper, a joint edge computing and caching based on a Dueling Double Deep Q Network (D3QN) is proposed to solve the problem of the multi-task joint edge calculation and caching process. Firstly, the processes of offloading tasks and caching them to the base station are modeled as optimization problems to maximize system revenues, which are limited by system latency and energy consumption as well as cache space for computing task constraints. Moreover, we also take into account the negative impact of the number of unfinished tasks in relation to the optimization problem—the higher the number of unfinished tasks, the lower the system revenue. Secondly, we use the D3QN algorithm together with the cache models to solve the formulated NP-hard problem and select the optimal caching and offloading action by adopting an e-greedy strategy. Moreover, two cache models are proposed in this paper to cache tasks, namely the active cache, based on the popularity of the task, and passive cache, based on the D3QN algorithm. Additionally, tasks which deal with cache space are updated by computing the expulsion value based on type of popularity. Finally, simulation results show that the proposed algorithm has good performance in terms of the latency and energy consumption of the system and that it improves utilization of cache space and reduces the probability of unfinished tasks. Compared to the Deep Q Network with caching policy, with the Double Deep Q Network with caching policy and Dueling Deep Q Network with caching policy, the system revenue of the proposed algorithm is improved by 65%, 35% and 66%, respectively. The scenario of the IOV proposed in this article can be expanded to larger-scale IOV systems by increasing the number of SDVs and base stations, and the content caching and download functions of the Internet of Things can also be achieved through collaboration between multiple base stations. However, only the cache model is focused on in this article, and the design of the replacement model is not good enough, resulting in a low utilization of cache resources. In future work, we will analyze how to make joint decisions based on multi-agent collaboration for caching, offloading and replacement in IOV scenarios with multiple heterogeneous services to support different Vehicle-to-Everything services.

Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network

With the continuous development of intelligent vehicles, people’s demand for services has also rapidly increased, leading to a sharp increase in wireless network traffic. Edge caching, due to its location advantage, can provide more efficient transmission services and become an effective method to solve the above problems. However, the current mainstream caching solutions only consider content popularity to formulate caching strategies, which can easily lead to cache redundancy between edge nodes and lead to low caching efficiency. To solve these problems, we propose a hybrid content value collaborative caching strategy based on temporal convolutional network (called THCS), which achieves mutual collaboration between different edge nodes under limited cache resources, thereby optimizing cache content and reducing content delivery latency. Specifically, the strategy first obtains accurate content popularity through temporal convolutional network (TCN), then comprehensively considers various factors to measure the hybrid content value (HCV) of cached content, and finally uses a dynamic programming algorithm to maximize the overall HCV and make optimal cache decisions. We have obtained the following conclusion through simulation experiments: compared with the benchmark scheme, THCS has improved the cache hit rate by 12.3% and reduced the content transmission delay by 16.7%.

Cache-MAB: A reinforcement learning-based hybrid caching scheme in named data networks

The presence of content stores in routers enables in-network caching in Named Data Networks (NDN) to improve customer experience, especially when content distribution is considered. Typically, homogeneous caching is assumed in the network where each router operates its cache according to the installed policy. Numerous in-network caching policies that vary in admission logic and caching attributes have been urged. Hence, the policies vary in performance (efficiency) according to unbounded network factors such as caching capacity, users’ request pattern, content popularity distribution, and application type (amongst other contexts). A single scheme may not effectively realize all the network characteristics and vandalize the efficient use of precious cache spaces. Therefore, a new strategy capable of dynamically selecting the optimal caching policy under varying network contexts is inevitable. In this direction, we propose a reinforcement learning (RL)-based hybrid caching strategy, namely Cache-MAB, where the routers work in a distributed manner and learn to pick the most suitable policy for caching a content. The set of policies is formed by picking the policies that assume similar caching attributes for caching decisions. The proposed strategy decouples the caching policy selection from the admission logic used by the selected policy and models the suitable policy selection as a Multi-armed Bandit (MAB) problem. The hybrid strategy equips each router with a diverse set of caching policies and an RL agent that utilizes a reinforcement algorithm to solve the MAB problem and, hence, select the optimal policy in different cases. The optimal policy is the one that maximizes the local performance, for instance, the cache hit rate. Simulation results confirm that the hybrid strategy effectively achieves the optimal policy’s performance, having a performance gap of less than 1%, by adapting to different network scenarios.

When content-centric networking meets multi-criteria group decision-making: Optimal cache placement policy achieved by MARCOS with q-rung orthopair fuzzy set pair analysis

Assessing the cache placement policy (CPP) is a crucial step in the deployment process of content-centric networking (CCN), which is still an open question. In this paper, assessing the CPP is formulated to be a multi-criteria group decision-making (MCGDM) issue since it involves the consideration of multiple experts, and a new aggregated MCGDM algorithm is presented for dealing with this issue. On that account, an assessment criteria system is to formulate to portray these experts’ considerations for assessing CPPs, and then the concepts of q-rung orthopair fuzzy set (q-ROFS) and set pair analysis (SPA) are presented to directly and indirectly define the group preference information of CPPs with respect to the criteria, respectively. Later, some revised aggregation operators are employed for aggregating the group preference information. Then, a new integrated objective criteria weights (IOCW) method based on score value and combined criteria weights based on non-linear comprehensive method are given for determining the important degrees of criteria. Based on IOCW method, non-linear comprehensive method, score function, distance measure, and aggregation operators, a new integrated q-rung orthopair fuzzy MCGDM model based on MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) method is developed to rank CPPs. Finally, an example is given to illustrate the evaluation process of CPP, and the advantages of developed method are verified by comparative analysis.

Quality-Aware Joint Caching, Computing and Communication Optimization for Video Delivery in Vehicular Networks

As being clear that the joint optimization of caching, computing, and communication (3 C) can significantly improve vehicular network performance, this paper investigates the quality-aware 3 C optimization for video delivery in vehicular networks. By incorporating the quality-awareness into 3 C models, we formulate both the quality-aware joint 3 C optimization problem and the quality-aware joint computing and communication (2 C) optimization problem with predetermined caching. By observing that the proposed 2 C and 3 C problems can be interpreted as submodular maximization problems with constraints, we propose a solution methodology using the submodularity and knapsack interpretation. Based on the proposed methodology, we then propose efficient solution approaches assuming batch input and streaming input, respectively. Complexity of the proposed solution approaches is analyzed. In addition, extensive simulations are conducted to evaluate the proposed solution approaches. Results show that our proposed approaches can outperform the reference schemes and improve the video delivery of vehicular networks.

Multi-Rate Probabilistic Caching Optimized Video Offloading in Dense D2D Networks

In this paper, we propose a multi-rate caching (MRC) scheme for video offloading in dense device-to-device (D2D) networks. The MRC scheme utilises homogeneous Poisson Point Process model, D2D underlay transmission mode, and features of videos encoded into multiple rates (or versions), to derive the cache and spectrum sharing hit probabilities and the cache-based achievable rate. This way, an MRC optimisation problem is formulated for optimal caching probability and encoding rate selection of each video. The MRC optimisation problem further considers the playback resolution and storage resource constraints of the mobile devices to serve the mobile users more efficiently. As a result, the MRC solution gains the highest video offloading playback quality while conserving the bandwidth and caching storage resources of dense D2D networks.