Content Placement Problem Research Articles

Improving Caching Efficiency in Content-Aware C-RAN-Based Cooperative Beamforming: A Joint Design Approach

This work studies the joint problem of content placement, remote radio head (RRH) clustering and beamformer design, in a cache-enabled cloud-radio access network (C-RAN). In the considered system, downlink users are cooperatively served by multiple RRHs, in turn connected to a centralized baseband unit (BBU) pool via fronthaul links. Each RRH is equipped with a local cache from which it can directly acquire the requested user contents, without utilizing the fronthaul links. We aim to jointly optimize the aforementioned three aspects, in order to strike a balance between fronthaul traffic reduction and transmission power minimization. To this end, we propose to employ the ratio between these two important system utilities as the objective function, referred to as caching efficiency. Two joint design algorithms are presented to address the resulting nonconvex optimization problem, which features coupling constraints and mixed-integer variables, namely: the penalty concave-convex procedure (P-CCCP) and penalty dual decomposition (PDD) based algorithms. Furthermore, since content placement is usually updated over a larger timescale, we propose a two-timescale joint design algorithm, where the P-CCCP and PDD-based algorithms can be employed for efficient initialization as well as for establishing performance limits. Simulation results validate the efficiency of the proposed algorithms.

Smart collaborative video caching for energy efficiency in cognitive Content Centric Networks

Due to the prosperity of online media, caching video content has become an important measure to improve the user experience and alleviate the link burden in wireless caching networks. However, facing to perceptual requirements of video users, Scalable Video Coding (SVC) technique has been cumulatively utilized in wireless caching networks, which can guarantee acceptable video quality. In this paper, we propose a smart collaborative video caching policy to boost energy efficiency (EE) in Cognitive Content Centric networking (C-CCN), where the spectrum sharing characteristic of cognitive ratio (CR) is integrated into CCN. With consideration of the imperfect channel-state information (CSI), the collision interference exists between the primary users (PUs) and secondary base stations (SBs). Inspired by this, we derive the expressions of successful content delivery rate (SCDR) and total power consumption for the proposed policy. By using these expressions, the optimal EE model under the caching size constraint is formulated as an optimal content placement problem, which can be solved by the standard gradient projection method. Numerical and simulation results show the efficacy and superiority of the proposed policy compared with some traditional caching schemes.

Machine-Learning Approach for User Association and Content Placement in Fog Radio Access Networks

The joint user association and cache placement problem is challenging in fog radio access networks (F-RANs) due to its difficulty to present the optimal solution with low complexity. Motivated by the recent development of artificial intelligence, we divide the original optimization problem into two subproblems. In particular, the user association problem is solved by a reinforcement-learning-based algorithm in which the enhanced fog access point content placement profiles and the fronthaul constraint are considered. On the other hand, since the popularity profile of the contents is hard to acquire in practice, a stacked autoencoder-based scheme is presented to predict the content popularity, which considers both the local and global user request status within a specified time interval. Based on the popularity prediction, the edge content placement problem is solved by a deep-reinforcement-learning-based algorithm, aiming at maximizing the F-RAN network payoff. Moreover, the complicated interactions and the cyclic dependency among the short time-scale user association and the long time-scale content popularity prediction and placement problems are studied by applying the Stackelberg game theory. The simulation validates the accuracy of the analytical results and proves that the proposal can further improve the performance of F-RANs.

Content placement in 5G‐enabled edge/core data center networks resilient to link cut attacks

AbstractHigh throughput, resilience, and low latency requirements drive the development of 5G‐enabled content delivery networks (CDNs) which combine core data centers (cDCs) with edge data centers (eDCs) that cache the most popular content closer to the end users for traffic load and latency reduction. Deployed over the existing optical network infrastructure, CDNs are vulnerable to link cut attacks aimed at disrupting the overlay services. Planning a CDN to balance the stringent service requirements and increase resilience to attacks in a cost‐efficient way entails solving the content placement problem (CPP) across the cDCs and eDCs. This article proposes a framework for finding Pareto‐optimal solutions with minimal user‐to‐content distance and maximal robustness to targeted link cuts, under a defined budget. We formulate two optimization problems as integer linear programming (ILP) models. The first, denoted as K‐best CPP with minimal distance (K‐CPP‐minD), identifies the eDC/cDC placement solutions with minimal user‐to‐content distance. The second performs critical link set detection to evaluate the resilience of the K‐CPP‐minD solutions to targeted fiber cuts. Extensive simulations verify that the eDC/cDC selection obtained by our models improves network resilience to link cut attacks without adversely affecting the user‐to‐content distances or the core network traffic mitigation benefits.

Cache hit ratio maximization in device-to-device communications overlaying cellular networks

This paper investigates the content placement problem to maximize the cache hit ratio in device-to-device (D2D) communications overlaying cellular networks. We consider offloading contents by users themselves, D2D communications and multicast, and we analyze the relationship between these offloading methods and the cache hit ratio. Based on this relationship, we formulate the content placement optimization as a cache hit ratio maximization problem, and propose a heuristic algorithm to solve it. Numerical results demonstrate that the proposed scheme can outperform existing schemes in terms of the cache hit ratio.

Multidimensional Cooperative Caching in CoMP-Integrated Ultra-Dense Cellular Networks

Small base stations (BSs) equipped with caching units are potential to provide improved quality of service (QoS) for multimedia service in ultra-dense cellular networks (UDCNs). In addition, the Coordinated MultiPoint (CoMP) transmission method, allowing multiple BSs to jointly serve users, is proposed to increase the throughput of cell-edge mobile terminals (MTs). Yet, the combination of content caching and CoMP in UDCNs is still not well explored for future networks. In this paper, we focus on the application of caching in CoMP-integrated UDCNs, where the cache-enabled BSs can collaboratively serve each MT using either joint transmission or single transmission. We propose a multidimensional cooperative caching (MDCC) scheme, supporting storage-dimension and transmission-dimension cooperations for the content placement. In particular, we analyze the delivery delay based on request patterns, transmission method, and the proposed cooperation strategy. Then the content placement problem is formulated as a problem of minimizing the overall expected delay. The problem is a mixed binary integer linear programming (BILP) problem, which is NP-hard. Therefore, we address the problem with approximation and substitution, and design a genetic algorithm (GA) based method to solve it. Simulation results demonstrate that the proposed MDCC scheme contributes performance gain in terms of content delivery delay in both cell-core and cell-edge areas.

Content Placement and User Association for Delay Minimization in Small Cell Networks

In small cell networks, content placement can reduce the content download delay at backhaul links while imposing challenges on user association problems. Specifically, a user may be associated with a small base station (SBS) that has the desired contents but is far away, increasing the delay over radio access links. To this end, we investigate the joint content placement and user association problem to reduce the average download delay over backhaul and radio access links for content delivery. Considering both dynamic user population and natural tandem systems, we model content delivery in each SBS as a tandem queue and thus derive the average sojourn time (the average time contents stay in the tandem queue), i.e., the average download delay. On this basis, we formulate a delay minimization problem, encompassing user characteristics (traffic arrival rates, requested content lengths and content preferences) and SBS constraints (backhaul capacity and storage size). Then, this problem is decomposed into a user association subproblem and a content placement subproblem by exploiting the biconvexity. Finally, a distributed content placement and user association scheme is proposed by exploring the optimal match with the first derivative length method. Extensive simulation results reveal that the delay under the proposed content placement policy may be reduced as user characteristics become heterogeneous, which is opposite to the case under user association policies.

Multi-Location-Aware Joint Optimization of Content Caching and Delivery for Backhaul-Constrained UDN.

Mobile edge caching is regarded as a promising way to reduce the backhaul load of the base stations (BSs). However, the capacity of BSs’ cache tends to be small, while mobile users’ content preferences are diverse. Furthermore, both the locations of users and user-BS association are uncertain in wireless networks. All of these pose great challenges on the content caching and content delivery. This paper studies the joint optimization of the content placement and content delivery schemes in the cache-enabled ultra-dense small-cell network (UDN) with constrained-backhaul link. Considering the differences in decision time-scales, the content placement and content delivery are investigated separately, but their interplay is taken into consideration. Firstly, a content placement problem is formulated, where the uncertainty of user-BS association is considered. Specifically, different from the existing works, the specific multi-location request pattern is considered that users tend to send content requests from more than one but limited locations during one day. Secondly, a user-BS association and wireless resources allocation problem is formulated, with the objective of maximizing users’ data rates under the backhaul bandwidth constraint. Due to the non-convex nature of these two problems, the problem transformation and variables relaxation are adopted, which convert the original problems into more tractable forms. Then, based on the convex optimization methods, a content placement algorithm, and a cache-aware user association and resources allocation algorithm are proposed, respectively. Finally, simulation results are given, which validate that the proposed algorithms have obvious performance advantages in terms of the network utility, the hit ratio of the cache, and the quality of service guarantee, and are suitable for the cache-enabled UDN with constrained-backhaul link.

Edge Caching and Computation Offloading for Fog-Enabled Radio Access Network

Cooperation between the fog and cloud in fog-enabled radio access network could enormously improve the computation offloading services both in terms of latency and energy consumption, while edge caching allowing for low-latency transmission of the cached files, is essential for cooperation. It is necessary to select the best method that minimizes the energy consumption with latency tolerance guaranteed, to offload the task. In this paper, we study the problem of content placement for edge caching and the cooperative computation offloading between the fog and the cloud by optimizing the cooperative offloading decisions and caching strategies. The problem is formulated to minimize the total energy consumption of all the UEs by optimizing the transmit power of the user equipments (UEs), caching strategies and the cooperation factor (the ratio of the fog execution of a file to the whole file), which is a non-convex programming problem. An approximate solution is obtained by using successive convex approximation strategy and implicit enumeration method. Numerical results show that our proposed algorithm can achieve better performance compared with other schemes, e.g., cloud computing and cache most popular strategies.

Hybrid collaborative caching in mobile edge networks: An analytical approach

With caching at the base stations (BSs) in a cooperative manner, mobile edge caching (MEC) can alleviate the heavy backhaul burden and reduce the duplicated transmissions of content downloads, which has recently been considered a promising solution to cope with the exponentially increasing data traffic. However, how to maximize the storage utilization while reduce service latency and improve energy savings is still a critical issue in the large-scale mobile edge networks (MENs), since the growth of MENs in size as well as uneven users’ distribution make it difficult to determine which MEC should cache which content. To address this problem, we propose a hybrid collaborative caching (Hy-CoCa) design that jointly leverages local independent, intra-group collaborative and intra-network collaborative caching manners. MECs are clustered into disjoint groups, and then each MEC’s storage is partitioned into local, intra-group and intra-network portions. Local storage is reserved for storing the most popular contents to each MEC locally, so that users can directly fetch them from their associated MEC; Intra-group storage of different MECs inside the same group are regarded as an entity, which is used for collaboratively storing the middle popular contents, so as to reduce the probability for requesting contents from distant MECs; Intra-network storage of all MECs are leveraged for collaboratively storing less popular contents to different MECs in the entire MENs, as a means to improve the overall content diversity. Specifically, we first develop the Hy-CoCa’s framework to support users’ requests locally and conduct the construction of logical groups, considering users’ distribution and MECs’ proximity. Moreover, under the storage and popularity constraints, we formulate the storage allocation optimization problem to minimize average service latency and derive the optimal storage allocation. Furthermore, given an optimal storage allocation, we also formulate the request-aware content placement problem into an integer linear programming problem to maximize the overall energy savings. We prove the submodularity property of the objective function and propose a greedy algorithm with linear computational complexity, which can achieve (1−1/e)-optimality. Simulation results with real-world YouTube trace data demonstrate that our caching strategy can achieve 6% to 28% latency reduction and 9% to 75% energy savings improvement compared with other existing caching strategies.

Energy‐efficient hierarchical cooperative caching optimisation for 5G networks

The caching in fifth generation (5G) networks has been considered as a promising technique to reduce the duplicate traffic transmission and improve the users' quality of experience (QoE). Although many works have been done for caching in 5G networks, most of them focus on the content caching policy design to optimise the users' QoE, the issue to realise the energy efficiency of the whole network is not fully considered. Therefore, in this study, by introducing the hierarchical cooperative caching property, i.e. the core gateway and the base stations can cooperative cache the content, the authors study the problem of the hierarchical cooperative caching policy to realise the energy efficiency. They then formulate the hierarchical cooperative content placement problem as an integer programming problem to minimise the total energy consumption. Also, to reduce the computation complexity, the optimisation algorithm based on the idea of quantum-inspired evolutionary is proposed, which has the fast convergence and approximate to the optimal solution. Finally, extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.

Joint Content Placement and Storage Allocation in C-RANs for IoT Sensing Service

The Internet of Things (IoT) sensing service allows systems and users to monitor environment states by transmitting the content sensed by a variety of sensors. Owing to billions of sensors and devices deployed in the IoT system, a huge amount of data (big data) are generated, thus injecting tremendous traffic into the network. Cloud radio access network (C-RAN) is a promising wireless network architecture to accommodate the fast growing IoT traffic and improve the performance of IoT services. Caching in C-RAN, which brings content to the edges, not only alleviates the network traffic, thus improving the end-to-end user quality of service (QoS), but also avoids activating the sensors too frequently, thus reducing their energy consumption. The content placement problem determines what and where to cache in C-RAN. However, the caching performance is highly related to the caching storages. The storage allocation problem determines the storage capacities of network entities. In this paper, we jointly optimize the storage allocation problem and content placement problem in a hierarchical cache-enabled C-RAN architecture for IoT sensing service. We formulate the joint problem as an integer linear programming (ILP) model with the objective to minimize the total network traffic cost. The storage allocation problem and content placement problem are constrained by caching storage budgets and cache capacities, respectively. Two heuristic algorithms are proposed in order to reduce the computational complexity of ILP. Extensive simulations have been conducted to demonstrate that the performances of our proposed algorithms approximate the optimal solutions.

User-Centric Delay-Aware Joint Caching and User Association Optimization in Cache-Enabled Wireless Networks

The mobile edge caching is a promising way to reduce the user-perceived delay and improve the transmission data rates for the wireless networks. However, the cache capacities of base stations (BSs) tend to be limited and users’ interests for the contents are diverse, which makes the content placement decision critical for the network performance optimization. Besides, due to the flexible user-BS association, it is more complicated to optimize the content delivery and placement which are coupled with each other. This paper investigates the content placement and content delivery strategies in the cache-enabled wireless networks. In particular, the effective capacity , which can characterize the end-to-end user-perceived delay and data rates simultaneously, is introduced as the user’s utility metric. As the content caching and content delivery operate in different time-scales, they are investigated separately. For the content caching, a content placement problem is formulated, where both users’ different active levels and diverse content preferences are considered. Due to the NP-hard nature, the problem is decomposed into two sub-problems, and an iterative association-aware content placement algorithm is proposed. For the content delivery, the user-BS association problem is formulated, and a cache-aware user-BS association algorithm is designed. The performance of the proposed algorithms is evaluated based on the simulations. The numerical results show that the proposed algorithms have a better capability to cope with users’ diverse active levels, and achieve a better performance in terms of effective capacity and fairness level, compared with the existing algorithms.

Autonomous Cache Resource Slicing and Content Placement at Virtualized Mobile Edge Network

Nowadays, the unprecedented growth of mobile traffic is only rivaled by the demands for a better quality of service and faster data delivery. Caching the content closer to the end users is one solution to cope with these demands, but when service providers share a common physical infrastructure, managing the limited cache resources becomes more challenging. With this increased complexity, the deep reinforcement learning (DRL) presents itself as an optimal solution. In this work, we propose a dynamic DRL-based management framework for virtual cache slicing and customized content placement. The virtualization problem is formulated as a Markov decision process, and it utilizes a deep Q-network to autonomously make network-wide slicing decisions and optimize the cache resource allocation. These global decisions are then mapped to the small cell stations with a dynamic resource provisioning algorithm. Lastly, a customized content placement algorithm is used to find the optimal content placement policy for each service provider. The content placement problem is first formulated as a convex optimization problem and is then solved with a distributed alternating direction method of multipliers (ADMM). Finally, the simulation results are presented to show the effectiveness of the proposed solution.

Joint Cache Partitioning, Content Placement, and User Association for D2D-Enabled Heterogeneous Cellular Networks

The rapid growth of traffic demands has posed the challenges to both radio access networks (RANs) and backhaul links. While the device-to-device (D2D)-enabled heterogeneous cellular networks (HCNs) are expected to offer diverse radio access capabilities and to improve the transmission performance of user equipments (UEs) significantly, the backhaul links may still experience challenges in offering quality-of-service guaranteed services to UEs. To tackle these problems, caching technology, i.e., caching user contents at the infrastructures of RANs, is proposed as an effective approach. In this paper, we consider the joint cache partitioning, content placement, and user association problem in the D2D-enabled HCNs and propose a two-step algorithm. Aiming to improve the utilization of cache space at small base stations, we propose a bankruptcy game-based cache partitioning algorithm to obtain the optimal cache space allocation strategy, based on which we then propose a joint content placement and user association algorithm to achieve the minimum service delay of all the content request users. As the formulated optimization problem is a mixed integer nonlinear optimization problem which cannot be solved conveniently, we apply the McCormick envelopes and the Lagrangian partial relaxation method to decompose the optimization problem into three subproblems which can be iteratively solved by means of the modified Kuhn–Munkres algorithm and the unidimensional knapsack algorithm. Simulation results validate the effectiveness of our proposed scheme.

Content Fragmentation: A Redundancy Scheme to Save Energy in Cloud Networks

Due to rapid growth of content-based cloud services (e.g., video streaming), energy usage of cloud infrastructures, consisting of distributed data centers interconnected by a high-bandwidth long-distance network, keeps increasing. This energy usage could further increase due to requested content redundancy to ensure content-service resiliency. Typical content redundancy schemes are based on content replication (CR), i.e., each content is replicated in at least one secondary dc location, reachable in case of failure affecting the primary location, which induces at least 100% increase in storage energy consumption. To reduce storage energy overhead of CR, we investigate a new redundancy scheme, called content fragmentation (CF). CF exploits Reed–Solomon erasure code to fragment and encode content into blocks with less storage overhead (thus less storage energy usage) to guarantee content-service resiliency. But CF requires additional energy for content reconstruction and data transport in the core network. To determine which scheme is more energy efficient, we formulate, for both, the content-placement problem using mixed integer liner program (MILP), with the objective to minimize energy consumption. Also, due to MILP’s poor scalability, we propose a meta-heuristic content placement and routing assignment algorithm for more efficient solutions. We observe the impact on energy usage of three main metrics, i.e., number of content requests (popularity), resiliency, and latency. Results from a realistic case study suggest that CF always consumes less energy than CR given the same resiliency requirement, and CF is particularly energy-efficient when stored contents are less popular and latency-tolerant.

Hierarchical collaborative caching in 5G networks

Caching in mobile networks can reduce the redundant data transmission and cope with the challenge of the explosive growth of mobile data traffic. It has been considered as a promising technology in 5G networks and has been attracting a lot of attention in recent years. Although many existing works have addressed the content placement problem or the cache optimisation problem, most of them do not consider the issue of hierarchical collaborative caching. Collaborative caching can further alleviate the traffic pressure and reduce the user-perceived latency by reducing duplicate content transmission. Therefore, in this study, the authors consider a hierarchical collaborative caching framework with the cache deployment at the distributed gateway and mobile edge computing servers, and then design a novel caching strategy based on this framework. They formulate the hierarchical collaborative content placement problem as an optimisation problem to maximise the latency saving under the constraint of limited cache capacity. Since finding the optimal solution is an NP-hard problem, they propose a genetic placement algorithm to find the near-optimal solution to reduce the computation complexity. Numerical experiment results show that the proposed algorithms can significantly improve the performance compared with the reference algorithms.

Modeling and improving the throughput of vehicular networks using cache enabled RSUs

Newly emerged applications in vehicular networks demand high throughput to transfer large amount of data through both Vehicle-to-Vehicle and Vehicle-to-Infrastructure links. One solution which recently draws researchers attention to itself for improving the throughput in this type of network is to deploy some Road-Side-Units (RSUs) in the streets with storage capability and store the data closer to the end users. Consequently, vehicles are able to download their inquired contents from these local RSUs instead of the base station. This will decrease the network traffic of the base station and also the average delay each vehicle has to wait to receive his requested files. The main issue to implement this distributed approach in this type of environment compared to other types of networks is that the fast moving vehicles make the topology of the network highly dynamic. Also due to limited storage capacity of the caches in the RSUs, we should decide on how to distribute the contents in the RSUs to maximize the number of locally satisfied vehicles. In this paper, we address the cache content placement problem in vehicular networks and model it using a game theoretic approach. We show that the proposed game model is a special case of generalized covering games. Considering the hit ratio of the caches as the performance metric in our model, we propose a method to distributively optimize this metric using the RSU’s local information. In addition, we propose a combinatorial approach to find efficient file placements in the RSUs using Markov approximation. Empirical evaluations on realistic trace-based simulations show an improvement of 7.5% in the average hit ratio of the proposed method compared to other well-known cache content placement approaches. Newly emerged applications in vehicular networks demand high throughput to transfer large amount of data through both Vehicle-to-Vehicle and Vehicle-to-Infrastructure links. To improve the network throughput, we deploy some Road-Side-Units (RSUs) in the streets with storage capability and store the data closer to the end users. Consequently, vehicles are able to download their inquired contents from these local RSUs instead of the base station. The main issue to implement this distributed approach is that the fast moving vehicles make the topology of the network highly dynamic. Also due to limited storage capacity of the caches in the RSUs, we should decide on how to distribute the contents in the RSUs to maximize the number of locally satisfied vehicles. In this paper, we address the cache content placement problem in vehicular networks and model it using game theoretic approach and Combinatorial approach. We show that the proposed game model is a special case of generalized covering games. Considering the hit ratio of the caches as the performance metric in our model, we propose a method to distributively optimize this metric using the RSU’s local information. In addition, we propose a Combinatorial approach to find efficient file placements in the RSUs using Markov approximation. Empirical evaluations on realistic trace-based simulations show an improvement of 7.5% in the average hit ratio of the proposed method compared to other well-known cache content placement approaches.

Lightpath routing and spectrum allocation over elastic optical networks in content provisioning with cloud migration

In this paper, the problem of content provisioning over elastic optical network (EON) with possible cloud migration is addressed. We perform the analysis that encompasses the problem of content placement into cloud data centers together with lightpath routing and spectrum assignment in EON. We propose a novel mixed integer linear programming model to solve the considered optimization problem. Multiple attribute decision-making methods are used to determine the relevance of each criterion in the objective function. The developed model achieves appropriate placement of content groups into cloud data centers and determines the lightpaths depending on a given bandwidth demand for content provisioning, thus optimizing cloud migration, spectrum utilization and lightpath length, simultaneously. The model is evaluated in two realistic optical network scenarios: the medium-sized INT9 network and the Euro28 network topology. Extensive optimization experiments are performed and the analysis of the network resources utilization is provided.

Proactive content caching by exploiting transfer learning for mobile edge computing

SummaryTo address the vast multimedia traffic volume and requirements of user quality of experience in the next‐generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named learning‐based cooperative caching (LECC) strategy based on mobile edge computing architecture to reduce transmission cost while improving user quality of experience for future mobile networks. In LECC, we exploit a transfer learning‐based approach for estimating content popularity and then formulate the proactive caching optimization model. As the optimization problem is NP‐hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate and decrease content delivery latency and transmission cost in comparison with known existing caching strategies.