Content Popularity Distribution Research Articles

Cache content placement in the presence of fictitious requests in mmWave 5G IAB networks

Caching popular content at the edge of wireless networks leads to backhaul congestion mitigation. To come up with an effective caching policy, content popularity distribution should be taken into account, which is not accurately known in most practical scenarios. Moreover, the mobile users’ (MU) request pattern may not always follow a well-defined distribution since some malicious MUs may deliberately issue their requests incompatible with the content popularity statistics. In this paper, we consider the problem of cache content placement in a 5G mmWave small cell network that relies on integrated access and backhaul (IAB) technology for pushing contents to MUs. We assume that the IAB node is equipped with a cache and has no prior knowledge about the content popularity profiles; instead, it only relies on the observation of the instantaneous demands to shape its caching policy. Also, malicious MUs may exist whose goals are to increase cache miss by issuing fictitious requests. The IAB node decides on which contents to cache and for how long, given that frequently replacing contents incurs administrative costs. We model the content placement problem as an ”adversarial combinatorial multi-armed bandit process with switching costs (ACMAB-SC)” and present an online learning algorithm for shaping the caching policy. We conduct extensive simulation experiments to evaluate the convergence property and assess the performance of our algorithm in terms of backhaul congestion, delay, and cache hit ratio. We also compare against two baseline online learning schemes, including a CMAB-based approach and a generic caching policy based on the ”Follow the Perturbed Leader (FPL)” algorithm.

Energy efficiency of cache collaboration in core content distribution networks

With escalating demands for high-definition video, cache collaboration allows neighbor nodes to share locally stored content in order to reduce download traffic. High energy consumption associated with content delivery remains a concern for Content Distribution Networks (CDNs). Therefore, this paper proposes cluster-based collaborative caching in a core network employing IP over WDM. The aim is to allow sets of core caches to fully share content while minimizing power. A Mixed Integer Linear Programming (MILP) model is used to form energy-efficient cache clusters. The energy consumption of the network is evaluated under different cluster sizes to find the optimum size that minimizes energy. To evaluate the influence of content popularity distribution, a heavy-tailed Zipf distribution and an Equal popularity distribution are evaluated. In addition, the work investigates the influence of downlink traffic behavior and power consumption parameters on optimum cluster sizes. Attained results reveal that maximum savings in energy consumption introduced by cluster-based collaborative caching are up to 34.3% and 21.8% under the Zipf and Equal distribution, respectively. Cache collaboration is not recommended when all core nodes contain fully replicated content servers. Results also show that power consumption parameters do not influence cluster formation. It is recommended keeping cache collaboration in the core network simple, so as to reduce intra-cluster communication.

Delay-Optimized Edge Caching in Integrated Satellite-Terrestrial Networks With Diverse Content Popularity Distribution and User Access Modes

Delay-Optimized Edge Caching in Integrated Satellite-Terrestrial Networks With Diverse Content Popularity Distribution and User Access Modes

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.

An accurate retransmission timeout estimator for content-centric networking based on the Jacobson algorithm

Accurately estimating of Retransmission TimeOut (RTO) in Content-Centric Networking (CCN) is crucial for efficient rate control in end nodes and effective interface ranking in intermediate routers. Toward this end, the Jacobson algorithm, which is an Exponentially Weighted Moving Average (EWMA) on the Round Trip Time (RTT) of previous packets, is a promising scheme. Assigning the lower bound to RTO, determining how an EWMA rapidly adapts to changes, and setting the multiplier of variance RTT have the most impact on the accuracy of this estimator for which several evaluations have been performed to set them in Transmission Control Protocol/Internet Protocol (TCP/IP) networks. However, the performance of this estimator in CCN has not been explored yet, despite CCN having a significant architectural difference with TCP/IP networks. In this study, two new metrics for assessing the performance of RTO estimators in CCN are defined and the performance of the Jacobson algorithm in CCN is evaluated. This evaluation is performed by varying the minimum RTO, EWMA parameters, and multiplier of variance RTT against different content popularity distribution gains. The obtained results are used to reconsider the Jacobson algorithm for accurately estimating RTO in CCN. Comparing the performance of the reconsidered Jacobson estimator with the existing solutions shows that it can estimate RTO simply and more accurately without any additional information or computation overhead.

Content Placement Considering the Temporal and Spatial Attributes of Content Popularity in Cache-Enabled UAV Networks

Unmanned aerial vehicles (UAVs) can provide low-cost connectivity to wireless devices in areas with no infrastructure coverage. In particular, the cache-enabled UAV network can meet the needs of Ground Terminals (GTs) by virtue of its content cache advantages, and reduces the use of backhaul links. However, since the temporal and spatial distribution of content popularity in the cache-enabled UAV network is dynamic, guaranteeing the utilization of UAV cache resources is challenging. Correspondingly, this paper presents a mathematical model for the temporal and spatial distribution characteristics of content popularity of the enabled UAV network, the remaining cache changes, and the user download experience effect, to propose an optimized content placement method in an enabled UAV network. Lastly, the acquired numerical results validate the effectiveness of our method in the cache resource allocation of UAV.

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.

Content caching for shared medium networks under heterogeneous users’ behaviors

Content caching is a widely studied technique aimed to reduce the network load imposed by data transmission during peak time while ensuring users’ quality of experience. It has been shown that when there is a common link between caches and the server, delivering contents via the coded caching scheme can significantly improve performance over conventional caching. However, finding the optimal content placement is a challenge in the case of heterogeneous users’ behaviors. In this paper we consider heterogeneous number of demands and non-uniform content popularity distribution in the case of homogeneous and heterogeneous user-preferences. We propose a hybrid coded–uncoded caching scheme to trade-off between popularity and diversity. We derive explicit closed-form expressions of the server load for the proposed hybrid scheme and formulate the corresponding optimization problem. Results show that the proposed hybrid caching scheme can reduce the server load significantly and outperforms the baseline pure coded and pure uncoded schemes and previous works in the literature for both homogeneous and heterogeneous user preferences.

A Novel Cooperative Cache Policy for Wireless Networks

Mobile edge caching is an emerging approach to manage high mobile data traffic in fifth-generation wireless networks that reduces content access latency and offloading data traffic of backhaul links. This paper proposes a novel cooperative caching policy based on long short-term memory (LSTM) neural networks considering the characteristics between the features of the heterogeneous layers and the user moving speed. Specifically, LSTM is applied to predict content popularity. Size-weighted content popularity is utilised to balance the impact of the predicted content popularity and content size. We also consider the moving speeds of mobile users and introduce a two-level caching architecture consisting of several small base stations (SBSs) and macro base stations (MBSs). To avoid content requests of fast-moving users affecting the content popularity distribution of the SBS since fast-moving users frequently handover among SBSs, fast-moving users are served by MBSs no matter which SBS they are in. SBSs serve low-speed users, and SBSs in the same cluster can communicate with one another. The simulation results show that compared to common cache methods, for example, the least frequently used and least recently used methods, our proposed policy is at least 8.9% lower and 6.8% higher in terms of the average content access latency and offloading ratio, respectively.

Content delivery enhancement in Vehicular Social Network with better routing and caching mechanism

Vehicular Social Network (VSN) emerges as one meaningful scenario of Mobile Social Network (MSN) and aims at addressing the traffic congestion and security problems in smart cities. However, the current IP based network connection cannot efficiently solve the high mobility and dynamically changing topology issues caused by VSN, which results in long routing latency and low cache hit ratio. In this regard, this work intends to optimize the routing latency and the cache hit ratio by solving the content delivery problem in VSN, during which the concepts of fog/edge computing and Information-Centric Networking (ICN) are introduced, since they can provide a new perspective of network connection in VSN by concentrating on contents instead of IP addresses. Firstly, the ICN routing table structure is expanded for the purpose of providing key routing basis for both interest and data packets. Secondly, the vehicle social information is fully explored from the points of relationship strength and interest degree to offer reliable data foundation for community detection and packet forwarding. Thirdly, the dynamic vehicle communities are constructed based on an improved community overlap propagation algorithm. By monitoring the content popularity and cache distribution in the dynamic communities, this paper proposes the vehicle-adaptive caching scheme which guarantees the cache diversity and improves the packet delivery rate. After that, a vehicle-adaptive interest packet routing scheme is proposed based on relationship strength and interest degree for both inter/intra communities. The experiment results indicate that the proposed mechanism can improve the network performance in terms of the packet delivery rate, the average hop, the average delay and the overhead.

Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs

With the rapid development of mobile communication and the sharp increase of smart mobile devices, wireless data traffic has experienced explosive growth in recent years, thus injecting tremendous traffic into the network. Fog Radio Access Network (F-RAN) is a promising wireless network architecture to accommodate the fast growing data traffic and improve the performance of network service. By deploying content caching in F-RAN, fast and repeatable data access can be achieved, which reduces network traffic and transmission latency. Due to the capacity limit of caches, it is essential to predict the popularity of the content and pre-cache them in edge nodes. In general, the classic prediction approaches require the gathering of users’ personal information at a central unit, giving rise to users’ privacy issues. In this paper, we propose an intelligent F-RANs framework based on federated learning (FL), which does not require gathering user data centrally on the server for training, so it can effectively ensure the privacy of users. In the work, federated learning is applied to user demand prediction, which can accurately predict the content popularity distribution in the network. In addition, to minimize the total traffic cost of the network in consideration of user content requests, we address the allocation of storage resources and content placement in the network as an integrated model and formulate it as an Integer Linear Programming (ILP) problem. Due to the high computational complexity of the ILP problem, two heuristic algorithms are designed to solve it. Simulation results show that the performance of our proposed algorithm is close to the optimal solution.

Delay Gain Analysis of Wireless Multicasting for Content Distribution

In this work, we provide a comprehensive analysis of stability properties and delay gains that wireless multicasting capabilities, as opposed to more traditional unicast transmissions, can provide for content distribution in mobile networks. In particular, we propose a model and characterize the average queue-length (and hence average delay) performance of unicasting and various multicasting strategies for serving a dynamic user population at the wireless edge. First, we show that optimized static randomized multicasting (we call it `blind multicasting') leads to stable-everywhere operation irrespective of the network loading factor (given by the ratio of the demand rate to the service rate) and the content popularity distribution. In contrast, traditional unicasting suffers from unstable operation when the loading factor approaches one, although it outperforms blind multicasting at small loading factor levels. This motivates us to study `work-conserving multicast' policies next that always outperform unicasting while still offering stable-everywhere operation. Then, in the worst-case of uniformly-distributed content popularity, we explicitly characterize the scaling of the average queue-length (and hence delay) under a first-come-first-serve multicast strategy as a function of the database size and the loading factor. Consequently, this work provides the fundamental limits, as well as the guidelines, for the design and performance analysis of efficient multicasting strategies for wireless content distribution.

Residual Energy-Aware Caching in Energy Harvesting-Based Mobile D2D Network

Caching popular contents on mobile devices is a promising technique to alleviate the backhaul data rate requirements. Since both file placement and data exchange among mobile devices consume energy, the energy status of the device can have a significant impact on the caching utility of the whole system. This work considers the caching optimization in a cellular network, where mobile devices are charged with energy harvested from the ambient environment. As the devices can collect the file segments from the local storage via device-to-device (D2D) links and cellular links, we aim at minimizing the percentage of file segment that should be collected from the base station (BS) by optimizing the file placement scheme on the devices. We prove that the optimal file placement scheme cannot be found in polynomial time by showing the intractability of the problem. Since the problem is NP-hard, we propose a residual energy-aware file placement algorithm based on the popularity distribution of contents and causality of energy arrival. In particular, we show that the proposed algorithm can fastly find a suboptimal file placement solution using dynamic programming. Extensive simulation results show that the proposed algorithm can effectively reduce the percentage of file segments collected from the BS compared with other two conventional caching methods.

A Realization of Fog-RAN Slicing via Deep Reinforcement Learning

To meet the wide range of 5G use cases in a cost-efficient way, network slicing has been advocated as a key enabler. Unlike the core network slicing in a virtualized environment, radio access network (RAN) slicing is still in its infancy and the corresponding realization is challenging. In this paper, we investigate the realization approach of fog RAN slicing, where two network slice instances for hotspot and vehicle-to-infrastructure scenarios are concerned and orchestrated. In particular, the framework for RAN slicing is formulated as an optimization problem of jointly tackling content caching and mode selection, in which the time-varying channel and unknown content popularity distribution are characterized. Due to the different users’ demands and the limited resources, the complexity of original optimization problem is significant high, which makes traditional optimization approaches hard to be directly applied. To deal with this dilemma, a deep reinforcement learning algorithm is proposed, whose core idea is that the cloud server makes proper decisions on the content caching and mode selection to maximize the reward performance under the dynamical channel state and cache status. The simulation results demonstrate the performance in terms of hit ratio and sum transmit rate can be significantly improved by the proposal.

Cache Enabled Cellular Network: Algorithm for Cache Placement and Guarantees

This letter presents a novel algorithm for content placement in the small base stations (SBSs) caches in a heterogeneous wireless network. The problem of maximizing the average rate of cache hit in a heterogeneous wireless network for a given probability distribution of content popularity and a given network topology, under the constraint of cache size at each SBS is proposed. The optimal cache placement algorithm turns out to be NP-Hard, and hence, a novel approximate solution is presented. Further, the theoretical guarantees on the performance of the proposed algorithm is also provided. Finally, simulation results demonstrate that the proposed edge caching strategy performs better than the conventional greedy caching policy, least recently used, and least frequently used algorithms.

Analytical characterization of cache replacement policy impact on content delivery time in information‐centric networks

SummaryInformation‐centric networking (ICN) has emerged as a promising candidate for designing content‐based future Internet paradigms. ICN increases the utilization of a network through location‐independent content naming and in‐network content caching. In routers, cache replacement policy determines which content to be replaced in the case of cache free space shortage. Thus, it has a direct influence on user experience, especially content delivery time. Meanwhile, content can be provided from different locations simultaneously because of the multi‐source property of the content in ICN. To the best of our knowledge, no work has yet studied the impact of cache replacement policy on the content delivery time considering multi‐source content delivery in ICN, an issue addressed in this paper. As our contribution, we analytically quantify the average content delivery time when different cache replacement policies, namely, least recently used (LRU) and random replacement (RR) policy, are employed. As an impressive result, we report the superiority of these policies in term of the popularity distribution of contents. The expected content delivery time in a supposed network topology was studied by both theoretical and experimental method. On the basis of the obtained results, some interesting findings of the performance of used cache replacement policies are provided.

Multi-node D2D communications for wireless video delivery over cellular networks

Video service is one of the popular services among mobile users, which plays an important role in network traffic growth and owns the largest data traffic compared to other types of communications. Over the last few years, users’ considerable enthusiasm to watch live video streams has captivated the attention of researchers to the networks equipped with content saving devices. In these devices the video content is saved beforehand within the edge devices (small stations and smart devices) for fast delivery. In this study, we propose a new method for sending video content, based on Device-to-Device (D2D) communications and providing multi-relational and multi-user opportunities. Based on the random nature of the popularity of video content and the random requests from users on the network, the authors investigate the probability of sending content to multiple users using the random distribution of popularity of users simultaneously. In this method, several users’ requests are responded by a single transmission. Extensive simulations result of the proposed method reveals the significant effect of the random distribution of video content popularity and shows that using multi-link D2D connections can cause considerable improvements in the efficiency of the network in terms of the network throughput.

Caching mechanism for mobile edge computing in V2I networks

AbstractThe caching‐enabled mobile edge computing (MEC) system becomes increasingly crucial to the future long‐term evolution vehicle‐to‐infrastructure (LTE‐V2I) network, especially for fast delivery of popular content of delay‐sensitive applications in the backhaul capacity–limited vehicular networks. Most related studies mainly focus on computation offloading strategy design and coding caching, while the deployment strategy design of caching capacities for high‐speed vehicles has been overlooked. In this paper, we investigate the deployment problem in LTE‐V2I networks with particular consideration on high‐speed vehicles. First, in a one‐way scenario, a joint optimization framework is formulated to minimize the cache size of the MEC system, meanwhile maximizing the average downloading percentage, where the MEC system's backhaul capacities, vehicle speeds, content popularity distributions, and sides of the highway are taken into account. The impacts of LTE‐V2I system parameters, eg, vehicle speed, on the objective function are revealed through the one‐way optimization. Then, the two‐way optimization is formulated and solved by developing an iterative optimization approach based on the aforementioned one‐way scenario. Particularly, the optimal caching allocation between two ways is derived. Simulation results validate the effectiveness of the proposed approaches even with low backhaul capacities and high vehicle speed.

Multi-Agent Reinforcement Learning for Efficient Content Caching in Mobile D2D Networks

To address the increase of multimedia traffic dominated by streaming videos, user equipment (UE) can collaboratively cache and share contents to alleviate the burden of base stations. Prior work on device-to-device (D2D) caching policies assumes perfect knowledge of the content popularity distribution. Since the content popularity distribution is usually unavailable in advance, a machine learning-based caching strategy that exploits the knowledge of content demand history would be highly promising. Thus, we design D2D caching strategies using multi-agent reinforcement learning in this paper. Specifically, we model the D2D caching problem as a multi-agent multi-armed bandit problem and use Q-learning to learn how to coordinate the caching decisions. The UEs can be independent learners (ILs) if they learn the Q-values of their own actions, and joint action learners (JALs) if they learn the Q-values of their own actions in conjunction with those of the other UEs. As the action space is very vast leading to high computational complexity, a modified combinatorial upper confidence bound algorithm is proposed to reduce the action space for both IL and JAL. The simulation results show that the proposed JAL-based caching scheme outperforms the IL-based caching scheme and other popular caching schemes in terms of average downloading latency and cache hit rate.

Deep learning-based edge caching for multi-cluster heterogeneous networks

In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. Numerical results demonstrate the great approximation to the optimum and generalization ability.