Cache Placement Research Articles

Cache Placement Optimization in Mobile Edge Computing Networks With Unaware Environment—An Extended Multi-Armed Bandit Approach

Caching high-frequency reuse contents at the edge servers in the mobile edge computing (MEC) network omits the part of backhaul transmission and further releases the pressure of data traffic. However, how to efficiently decide the caching contents for edge servers is still an open problem, which refers to the cache capacity of edge servers, the popularity of each content, and the wireless channel quality during transmission. In this paper, we discuss the influence of unknown user density and popularity of content on the cache placement solution at the edge server. Specifically, towards the implementation of the cache placement solution in the practical network, there are two problems needing to be solved. First, the estimation of unknown users’ preference needs a huge amount of records of users’ previous requests. Second, the overlapping serving regions among edge servers cause the wrong estimation of users’ preference, which hinders the individual decision of caching placement. To address the first issue, we propose a learning-based solution to adaptively optimize the cache placement policy without any previous knowledge of the user density and the popularity of the contents. We develop the extended multi-armed bandit (Extended MAB), which combines the generalized global bandit (GGB) and Standard Multi-armed bandit (MAB), to iteratively estimate both a global parameter, i.e., the user density, and individual parameters, i.e., the popularity of each content. For the second problem, a multi-agent Extended MAB based solution is presented to avoid the mis-estimation of parameters and achieve the decentralized cache placement policy. The proposed solution determines the primary time slot and secondary time slot for each edge server. The edge servers estimate expected satisfied user number of caching a content with the overlap information and determine the cache placement solution. The proposed strategies are proven to achieve the bounded regret according to the mathematical analysis. Extensive simulations verify the optimality of the proposed strategies when comparing with baselines.

Stochastic Game Based Cooperative Alternating Q-Learning Caching in Dynamic D2D Networks

Edge caching has become an effective solution to cope with the challenges brought by the massive content delivery in cellular networks. In device-to-device (D2D) enabled caching cellular networks with time-varying content popularity distribution and user terminal (UT) location, we model these dynamic networks as a stochastic game to design a cooperative cache placement policy. The cache placement reward of each UT is defined as the caching incentive minus the transmission power cost for content caching and sharing. We consider the long-term cache placement reward of all UTs in this stochastic game. In an effort to solve the stochastic game problem, we propose a multi-agent cooperative alternating Q-learning (CAQL) based cache placement algorithm. The caching control unit is defined to execute the proposed CAQL, in which, the cache placement policy of each UT is alternatively updated according to the stable policy of other UTs during the learning process, until the stable cache placement policy of all the UTs in the cell is obtained. We discuss the convergence and complexity of CAQL, which obtains the stable cache placement policy with low space complexity. Simulation results show that the proposed algorithm can effectively reduce the backhaul load and the average content access delay in dynamic networks.

Association and Caching in Relay-Assisted mmWave Networks: A Stochastic Geometry Perspective

Limited backhaul bandwidth and blockage effects are two main factors limiting the practical deployment of millimeter wave (mmWave) networks. To tackle these issues, we study the feasibility of relaying as well as caching in mmWave networks. A user association and relaying (UAR) criterion dependent on both caching status and maximum biased received power is proposed by considering the spatial correlation caused by the coexistence of base stations (BSs) and relay nodes (RNs). Using stochastic geometry tools, we decouple the UAR and caching placement issues by analyzing the relationship between UAR probabilities and caching placement probabilities. We then optimize the formulated caching placement problem based on polyblock outer approximation by exploiting the monotonic property in the general case and utilizing convex optimization in the noise-limited case. Accordingly, we propose a BS and RN selection algorithm where caching status at BSs and maximum biased received power are jointly considered. Experimental results demonstrate a significant enhancement of backhaul offloading using the proposed algorithms, and show that deploying more RNs and increasing cache size in mmWave networks is a more cost-effective alternative than increasing BS density to achieve similar backhaul offloading performance.

A caching strategy based on many-to-many matching game in D2D networks

Wireless edge caching has been proposed to reduce data traffic congestion in backhaul links, and it is being envisioned as one of the key components of next-generation wireless networks. This paper focuses on the influences of different caching strategies in Device-to-Device (D2D) networks. We model the D2D User Equipments (DUEs) as the Gauss determinantal point process considering the repulsion between DUEs, as well as the caching replacement process as a many-to-many matching game. By analyzing existing caching placement strategies, a new caching strategy is proposed, which represents the preference list of DUEs as the ratio of content popularity to cached probability. There are two distinct features in the proposed caching strategy. (1) It can cache other contents besides high popularity contents. (2) It can improve the cache hit ratio and reduce the latency compared with three caching placement strategies: Least Recently Used (LRU), Equal Probability Random Cache (EPRC), and the Most Popular Content Cache (MPC). Meanwhile, we analyze the effect of caching on the system performance in terms of different content popularity factors and cache capacity. Simulation results show that our proposed caching strategy is superior to the three other comparison strategies and can significantly improve the cache hit ratio and reduce the latency.

A cache placement algorithm based on comprehensive utility in big data multi-access edge computing

A cache placement algorithm based on comprehensive utility in big data multi-access edge computing

Exploiting Coding and Recommendation to Improve Cache Efficiency of Reliability-Aware Wireless Edge Caching Networks

In this paper, a full-stack perspective that involves content coding, caching and recommendation is presented for wireless edge caching networks. Therein, content coding and recommendations are utilized to ensure the reliability of the network and to boost the gain of edge caching, respectively. Specifically, with a well-designed coding pattern, the concurrently occurred data corruptions of multiple access points (APs) can be successfully recovered. Whilst, the successful content retrieval is warranted with highly selective retrieval sets. Further, by offering personalized recommendations, the cache hitting at network edges can be promoted. On these grounds, a cache hit ratio maximization problem for reliability-aware wireless edge caching system is formulated, considering the successful content retrieval and repair, total repair cost requirements, cache capacity budget of each AP, and recommendation quality and quantity per user. To solve this intractable problem, a framework with two steps is developed, which leads to construction of a suitable coding schema with the minimum repair cost per content to satisfy the retrieval and repair criteria, thus achieving a stabilized optimal solution regarding the joint cache placement and recommendation decisions from a game-theoretical viewpoint. Finally, extensive numerical simulations are performed to confirm the superior performance of our devised algorithms compared with benchmark baselines.

Blockchain-based Data Trading in Edge-cloud Computing Environment

With the continuous growth in the amount of data generated in the edge-cloud environment, security risks in traditional centralized data management platforms have been concerned. Blockchain technology can be applied to guarantee safety and information transparency in data caching and trading processes. Therefore, a blockchain-based secure cost-aware data caching scheme is proposed to optimize the placement and prevent the tampering of cache data. In this scheme, under the constraints of transmission cost, edge cache size, a quantum particle swarm optimization (QPSO) algorithm is used to solve the data cache placement problem with the greatest content caching gain. A blockchain-based secure decentralized data trading model is proposed to solve the trust problem among the buyers, sellers, and agent nodes and increase incentives for users to trade data. A double auction mechanism is used to maximize social welfare. The experimental results reveal that the proposed data caching and trading scheme can reduce the data transmission cost, improve the cache hit ratio, and maximize social welfare.

Flow control oriented forwarding and caching in cache-enabled networks

The cache-enabled network architecture is very promising to improve the efficiency of content distribution and reduce the network congestion. In this paper, we propose a maximizing weighted throughput (MWT) algorithm for joint request forwarding, cache placement and flow control to dynamically optimize network performance. Specifically, a dual queue system that includes requests and data is established to retrieve the global content demands and traffic congestion information. In order to improve throughput performance as well as stabilize the queue, we formulate the flow-level throughput and design the request-level control policy by optimizing the throughput function and Lyapunov drift. The request forwarding policy adaptively allocates request forwarding rates for every link according to the differences among adjacent request queue backlogs. A novel cache priority function and a threshold-based request-dropping policy are used in the caching policy and flow control respectively to alleviate network overload. In addition, we prove the MWT algorithm achieves throughput near-optimal performance, and testify the dual queue system is stable by deducing the upper bound of the all queue backlogs. The experimental results verify the MWT algorithm stability and demonstrate the superiority of the MWT algorithm, compared to the state-of-the-art caching algorithms which are combined with back-pressure algorithm.

Congestion avoidance by dynamically cache placement method in named data networking

Congestion avoidance by dynamically cache placement method in named data networking

History-Aware Online Cache Placement in Fog-Assisted IoT Systems: An Integration of Learning and Control

In fog-assisted Internet-of-Things systems, it is a common practice to cache popular content at the network edge to achieve high quality of service. Due to uncertainties, in practice, such as unknown file popularities, the cache placement scheme design is still an open problem with unresolved challenges: 1) how to maintain time-averaged storage costs under budgets; 2) how to incorporate online learning to aid cache placement to minimize performance loss [also known as (a.k.a.) regret]; and 3) how to exploit offline historical information to further reduce regret. In this article, we formulate the cache placement problem with unknown file popularities as a constrained combinatorial multiarmed bandit problem. To solve the problem, we employ virtual queue techniques to manage time-averaged storage cost constraints, and adopt history-aware bandit learning methods to integrate offline historical information into the online learning procedure to handle the exploration–exploitation tradeoff. With an effective combination of online control and history-aware online learning, we devise a cache placement scheme with history-aware bandit learning called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPHBL</i> . Our theoretical analysis and simulations show that CPHBL achieves a sublinear time-averaged regret bound. Moreover, the simulation results verify CPHBL’s advantage over the deep reinforcement learning-based approach.

On the Fundamental Limits of Cache-Aided Multiuser Private Information Retrieval

We consider the problem of cache-aided Multiuser Private Information Retrieval (MuPIR) which is an extension of the single-user cache-aided PIR problem to the case of multiple users. In MuPIR, each of the $K_{\rm u}$ cache-equipped users wishes to privately retrieve a message out of $K$ messages from $N$ databases each having access to the entire message library. The privacy constraint requires that any individual database learns nothing about the demands of all users. The users are connected to each database via an error-free shared-link. In this paper, we aim to characterize the optimal trade-off between users' memory and communication load for such systems. Based on the proposed novel approach of \emph{cache-aided interference alignment (CIA)}, first, for the MuPIR problem with $K=2$ messages, $K_{\rm u}=2$ users and $N\ge 2$ databases, we propose achievable retrieval schemes for both uncoded and general cache placement. The CIA approach is optimal when the cache placement is uncoded. For general cache placement, the CIA approach is optimal when $N=2$ and $3$ verified by the computer-aided approach. Second, when $K,K_{\rm u}$ and $N$ are general, we propose a new \emph{product design} (PD) which incorporates the PIR code into the linear caching code. The product design is shown to be order optimal within a multiplicative factor of 8 and is exactly optimal when the user cache memory size is large.

Collaborative Data Caching and Computation Offloading for Multi-Service Mobile Edge Computing

Mobile edge computing (MEC) can use wireless access network (RAN) to provide the services required by user's information technology (IT) and cloud computing functions nearby, which can create a high-performance and low latency service environment. Performing task offloading and data caching at access points (APs) in a cooperative manner can reduce the heavy backhaul load and the retransmission of content downloading. However, in edge networks (ENs), how to maximize storage utilization while reducing service latency and energy consumption is still a key issue, because the heterogeneity of ENs and the uneven distribution of users make it difficult to determine which MEC server and what data should be cached. In this paper, we study a two-tier MEC system, which enables data caching and computing offloading policy to minimize the network cost at the user equipment (UE) side, while satisfying the constraints of task offloading deadline, the cache capacity at APs and the computing capability of MEC servers. The optimization problem is formulated as a mixed integer nonlinear program (MINLP) problem. In order to solve the problem, we transform it into an equivalent task offloading convex optimization problem by fixing an optimization variable. Furthermore, we solve a cache placement problem by dynamic programming (DP) algorithm. Then we propose a distributed collaborative data caching and computing offloading (CDCCO) iterative algorithm. Simulation results demonstrate that our proposed CDCCO algorithm can significantly reduce the network cost and achieve better performance than other existing schemes.

Mitigating Congestion with Explicit Cache Placement Notification for Adaptive Video Streaming over ICN

Mitigating Congestion with Explicit Cache Placement Notification for Adaptive Video Streaming over ICN

Cooperative Caching in HetNets With Mutual Information Accumulation

Caching is a promising technique in the next generation cellular network, reducing backhaul traffic while improving network efficiency. In this letter, we introduce a cache-enabled small base station cooperative transmission scheme while considering the macro base station (MBS) silence and mutual information accumulation (MIA). A tractable analytical framework for characterizing the successful transmission probability (STP) of the introduced scheme is described. The STP is maximized by optimizing the caching placement policy based on a constrained cross entropy optimization algorithm. Simulation results demonstrate that the proposed SBS cooperative transmission with MBS silence and MIA achieves higher STP than other compared schemes.

Intelligent Resource Management at the Edge for Ubiquitous IoT: An SDN-Based Federated Learning Approach

The ubiquitous nature of Internet of Things (IoT) devices has posited many challenges that need innovative solutions in the 5G era. Software defined networks (SDNs) are becoming indispensable in managing several aspects of next-generation IoT networking that arise from the need to control highly heterogeneous, geographically dispersed, mobile IoT devices. One such aspect is cache management at the edge. Recently, multiple forms of edge resources, including mobile device clouds and micro-edge data centers have emerged to provide scalable cache placement locations that reduce the costs for the mobile network operator (MNO). As all of these service locations are registered with the MNO (or links established after registration with the 5G base station, BS), content should be placed according to the user's demand and the cost the user is willing to pay to receive the desired level of QoS. To this end, it is important to understand the future popularity of the content for its optimal placement considering the highly dynamic user mobility. In this article, we address two key aspects of a mobile IoT network: security and seamless connectivity for data delivery. We rely on the federated learning (FL) architecture, which enables harnessing data and computational capabilities at end-user devices to train machine learning models. We study FL concepts in the domain of edge computing for IoT use cases, such as caching. We draw conclusions from various state-of-the-art models and posit several challenges that can be overcome via a novel proposed control algorithm.

Joint Interference Alignment and Probabilistic Caching in MIMO Small-Cell Networks

Cache-enabled small base stations (SBS) are capable of relieving the heavy burden of the backhaul link and reducing the transmission latency. The hit probability depends on the coverage probability and caching placement probabilities. However, the interference in the small-cell networks may significantly degrade the coverage probability. In this paper, for MIMO small-cell networks consisting of SBS and users, where both of them are equipped with multiple antennas, a joint interference alignment (IA) and probabilistic caching (JIA-ProbC) scheme is proposed. Using tools from stochastic geometry, the <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> -th order Voronoi cells are constructed to form clusters, where <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> SBSs cooperatively serve users within each of the <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> -th order Voronoi cells. Then, the IA scheme for MIMO interference channel (IC) is employed to cancel the intra-cluster interference within each <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> -th order Voronoi cell. By exploiting the advantage of multiples antennas at users, the IA scheme can simultaneously support more users interference-free than both the zero forcing (ZF) based interference cancellation scheme for MISO systems and SISO systems without interference management, as more interference can be canceled. Furthermore, the coverage probability is analytically approximated by the a closed-form expression. Moreover, the optimal caching placement probability is analytically derived. Numerical Simulation results show that the proposed JIA-ProbC can significantly outperform the existing joint ZF and probabilistic caching (JZF-ProbC) scheme for MISO systems and SISO probabilistic caching (SISO-ProbC) scheme as well as the joint IA and most popular caching (JIA-MPC) caching scheme.

Geocaching in Texas state parks: a technology readiness analysis

德州州立公园地理藏宝：一项科技准备度的分析研究研究目的本研究旨在运用科技三角模型以及科技准备度指数（TRI）以定性研究为方法来探索地理藏宝这一现象。研究设计/方法/途径本研究为探索公园, 地理藏宝以及访客的三角结构关系提出了理论框架。研究对德克萨斯州立公园行政人员进行了12项半结构化访谈。主题分析发进行了访谈数据分析。研究结果研究发现了三种形式的互动：公园访客, 访客科技装备, 和公园的科技装备。尽管地理藏宝活动提高了公园访客的体验, 本研究通过公园人员的视角也发现访客的热情有所降低。研究结果发现德州州立公园在科技准备度意义来说仍处于“回避”阶段, 基于较低的积极度和创新意识, 以及对地理藏宝活动表现较高的不适以及不安。研究原创性/价值本研究为已有的文献增添了有关州立公园地理藏宝的新发现。本研究是首次运用科技三角模型来研究公园, 地理藏宝和访客之间的三边关系。本论文也创新性的在定性研究中运用了TRI对州立公园科技准备度进行研究。

Joint Successful Transmission Probability, Delay, and Energy Efficiency Caching Optimization in Fog Radio Access Network

The fog radio access network (F-RAN) is considered an efficient architecture for caching technology as it can support both edge and centralized caching due to the backhauling of the fog access points (F-APs). Successful transmission probability (STP), delay, and energy efficiency (EE) are key performance metrics for F-RAN. Therefore, this paper proposes a proactive cache placement scheme that jointly optimizes STP, delay, and EE in wireless backhauled cache-enabled F-RAN. First, expressions of the association probability, STP, average delay, and EE are derived using stochastic geometry tools. Then, the optimization problem is formulated to obtain the optimal cache placement that maximizes the weighted sum of STP, EE, and negative delay. To solve the optimization problem, this paper proposes the normalized cuckoo search algorithm (NCSA), which is a novel modified version of the cuckoo search algorithm (CSA). In NCSA, after generating the solutions randomly via Lévy flight and random walk, a simple bound is applied, and then the solutions are normalized to assure their feasibility. The numerical results show that the proposed joint cache placement scheme can effectively achieve significant performance improvement by up to 15% higher STP, 45% lower delay, and 350% higher EE over the well-known benchmark caching schemes.

A Comprehensive Review on Edge Caching from the Perspective of Total Process: Placement, Policy and Delivery

With the explosive growth of smart devices and mobile applications, mobile core networks face the challenge of exponential growth in traffic and computing demand. Edge caching is one of the most promising solutions to the problem. The main purpose of edge caching is to place popular content that users need at the edge of the network, borrow free space to reduce user waiting time, and lighten the network load by reducing the amount of duplicate data. Due to the promising advantages of edge caching, there have been many efforts motivated by this topic. In this paper, we have done an extensive survey on the existing work from our own perspectives. Distinguished from the existing review articles, our work not only investigates the latest articles in this area, but more importantly, covers all the researches of the total process of edge caching from caching placement optimization, policy design, to the content delivery process. In particular, we discuss the benefits of caching placement optimization from the perspective of different stakeholders, detail the delivery process, and conduct in-depth discussions from the five phases, i.e., requested content analysis, user model analysis, content retrieval, delivery, and update. Finally, we put forward several challenges and potential future directions, and hope to bring some ideas for the follow-up researches in this area.

Joint optimization of cache placement and request routing in unreliable networks

Edge caching is a prevailing media delivery technology where data is hosted at the edge nodes with computing and storage capability in close proximity to users, in order to expand the backhaul network capacity and enhance users' quality of experience (QoE). The existing work in this area often neglects the fact that large-scale distributed cache networks are not particularly reliable and many edge nodes are prone to failure. In this paper we investigate and develop a novel, cooperative caching mechanism for content placement and request routing. We aim to minimize the content access delay and achieve the optimization in polynomial time, taking into account failures in an unreliable network environment with limited edge storage and bandwidth. We introduce two optimization algorithms: 1) a primal-dual algorithm that is based on the Lagrangian dual decomposition and subgradient method, and 2) a greedy-based approximation algorithm with a proven approximation ratio. Numerical results show that the proposed algorithms outperform other comparative approaches in synthetic and real network environments, and the approximation algorithm is particularly suitable for networking scenarios with sparse node connectivity and resources in short supply.