Cache Status Research Articles

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.

Collaborative Cache-Aided Relaying Networks: Performance Evaluation and System Optimization

This paper studies a multi-tier cache-aided relaying network, where the destination <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> is randomly located in the network and it requests files from the source <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> through the help of cache-aided base station (BS) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> relays. In this system, the multi-tier architecture imposes a significant impact on the system collaborative caching and file delivery, which brings a big challenge to the system performance evaluation and optimization. To address this problem, we first evaluate the system performance by deriving analytical outage probability expression, through fully taking into account the random location of the destination and different file delivery modes related to the file caching status. We then perform the asymptotic analysis on the system outage probability when the signal-to-noise ratio (SNR) is high, to enclose some important and meaningful insights on the network. We further optimize the caching strategies among the relays and BS, to improve the network outage probability. Simulations are performed to show the effectiveness of the derived analytical and asymptotic outage probability for the proposed caching strategy. In particular, the proposed caching is superior to the conventional caching strategies such as the most popular content (MPC) and equal probability caching (EPC) strategies.

Caching scheme for information‐centric networks with balanced content distribution

SummaryInformation‐centric network (ICN) emphasizes on content retrieval without much bothering about the location of its actual producer. This novel networking paradigm makes content retrieval faster and less expensive by shifting data provisioning into content holder rather than content owner. Caching is the feature of ICN that makes content serving possible from any intermediate device. An efficient caching is one of the primary requirements for effective deployment of ICN. In this paper, a caching approach with balanced content distribution among network devices is proposed. The selection of contents to be cached is determined through universal and computed using Zipf's law. The dynamic change in popularity of contents is also considered to take make caching decisions. For balancing the cached content across the network, every router keeps track of its neighbor's cache status. Three parameters, the proportionate distance of the router from the client (pd), the router congestion (rc), and the cache status (cs), are contemplated to select a router for caching contents. The new caching approach is evaluated in the simulated environment using ndnSIM‐2.0. Three state‐of‐the‐art approaches, Leave Copy Everywhere (LCE), centrality measures‐based algorithm (CMBA), and a probability‐based caching (probCache), are considered for comparison. The proposed method of caching shows the better performance compared to the other three protocols used in the comparison.

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 UAV-Enabled Data Dissemination Protocol With Proactive Caching and File Sharing in V2X Networks

In Vehicle-to-Everything (V2X) networks, where all vehicles and infrastructures are interconnected for information sharing, data dissemination is increasingly playing a significant role in superior and pluralistic communication services. To empower the efficiency of data dissemination, in this paper, we propose a novel unmanned aerial vehicle (UAV)-enabled scheduling protocol consisting of a proactive caching policy and a file sharing strategy in V2X networks. In the proactive caching process, we deploy UAVs as flying base stations (BSs) with caching capability, where we propose a UAV dynamic trajectory scheduling (DTS) algorithm to optimize the caching duration. Whereas in the file sharing strategy, based on the previous vehicular caching status, we provide a framework of file sharing cycle for data dissemination scheduling and employ a channel prediction algorithm to alleviate communication overhead. Moreover, we propose a relay ordering algorithm to effectively improve the file sharing process. Simulation results demonstrate that, our proposed scheduling protocol can enhance the efficiency of data dissemination and achieve an improved network performance in terms of caching process, system throughput, and file sharing latency in V2X networks.

Energy and Delay Optimization for Cache-Enabled Dense Small Cell Networks

In Cache-enabled dense small cell networks (DSCNs), energy consumption and file delivery delay are two critical performance metrics. On the other hand, power control is an essential mechanism to mitigate inter-cell interference in DSCNs. However, the impact of power control at small base stations (SBSs) on the system performance when caches are involved has not been well studied. Observing energy consumption and file delivery delay are coupled with each other in cache-enabled DSCNs, different from existing studies, we attempt to optimize these two performance metrics at the same time, taking into account sophisticated power control at SBSs. Firstly, we formulate the energy-delay optimization problem as a utility maximization problem, where cooperative power control among SBSs, file placement and user association are considered. Then, we solve the optimization problem in two stages (i.e., caching stage and delivery stage), based on the fact that caching is performed during off-peak time. At the caching stage, a local popular file placement policy is proposed by estimating user preference at each SBS. At the delivery stage, with given caching status at SBSs, the optimization problem is further decomposed by Benders’ decomposition method. An efficient algorithm is proposed to approach the optimal association and power solution by iteratively shrinking the gap of the upper and lower bounds. Finally, extensive simulations are performed to validate our analytical and algorithmic work. The results demonstrate that the proposed algorithms can achieve a better tradeoff between energy consumption and file delivery delay than the existing algorithms.

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.

Fairness and Transmission-Aware Caching and Delivery Policies in OFDMA-Based HetNets

Recently, wireless edge caching has emerged as a promising technology for future wireless networks to cope with exponentially increasing demands for high data rate and low latency multimedia services by proactively storing contents at the network edge. Here, we aim to design efficient cache placement and delivery strategies for an orthogonal frequency division multiple access (OFDMA)-based cache-enabled heterogeneous cellular network (C-HetNet) which operates in two separated phases: caching phase (CP) and delivery phase (DP). Since guaranteeing fairness among mobile users (MUs) is not well investigated in cache-assisted wireless networks, we first propose two delay-based fairness schemes called proportional fairness (PF) and min-max fairness (MMF). The PF scheme deals with minimizing the total weighted latency of MUs while MMF aims at minimizing the maximum latency among them. In the CP, we propose a novel proactive fairness and transmission-aware cache placement strategy (CPS) corresponding to each target fairness scheme by exploiting the flexible wireless access and backhaul transmission opportunities. Specifically, we jointly perform the allocation of physical resources as storage and radio, and user association to improve the flexibility of the CPSs. Moreover, in the DP of each fairness scheme, an efficient delivery policy is proposed based on the arrival requests of MUs, CSI, and caching status. Numerical assessments demonstrate that our proposed CPSs outperform the total latency of MUs up to 27 percent compared to the conventional baseline popular CPSs.

QoS-aware secure transaction framework for internet of things using blockchain mechanism

The Internet of Things (IoT) paradigm enables an enormous network with millions of connected smart things to control almost all aspect of services. In an IoT scenario, self-configured things are dynamically connected in a universal network. The small things are broadly distributed in a real world model with some essential processing capacity and finite storage. Security, privacy and reliability are the most important pillars of the IoT infrastructure. It is challenging to share personal data, highly confidential professional data over a centralized system. The infrastructure itself is capable to provide the guarantee to transmit data safely. However, a special attention for the data along with data consistency is required. Cyber attackers and hackers mostly target the centralized systems. To triumph over these types of situations we propose a security based blockchain service along with some encryption approaches for a secure communication. The encryption and decryption is performed by a hash based secret key. Risk modelling and relative risk reduction also done to prevent attacks under a virtual private network in IoT domain. Before and after attack users also wish to know quality of service (QoS). We have developed a mobile application framework which provides desire output of a user from different aspect of QoS for better understanding. The MQTT broker performs as a negotiator to convey data between cloud consumer and cloud provider. In QoS monitoring section user get chance to reconsider availability, reliability and responsibility like QoS factors while recovering resources and actual resources before attacking system based on CPU utilization, cache status and storage. As per experiment result we able to recover 100% RAM, 97.64% CPU utilization and 78.7% storage using proposed encryption and algorithm on blockchain infrastructure.

Cache-Aided Non-Orthogonal Multiple Access: The Two-User Case

In this paper, we propose a cache-aided non-orthogonal multiple access (NOMA) scheme for spectrally efficient downlink transmission. The proposed scheme not only reaps the benefits associated with NOMA and caching, but also exploits the data cached at the users for interference cancellation. As a consequence, caching can help to reduce the residual interference power, making multiple decoding orders at the users feasible. The resulting flexibility in decoding can be exploited for improved NOMA detection. We characterize the achievable rate region of cache-aided NOMA and derive the Pareto optimal rate tuples forming the boundary of the rate region. Moreover, we optimize cache-aided NOMA for minimization of the time required for completing file delivery. The optimal decoding order and the optimal transmit power and rate allocation are derived as functions of the cache status, the file sizes, and the channel conditions. Simulation results confirm that, compared to several baseline schemes, the proposed cache-aided NOMA scheme significantly expands the achievable rate region and increases the sum rate for downlink transmission, which translates into substantially reduced file delivery times.

Cache pressure-aware caching scheme for content-centric networking

Content centric networking (CCN) is a new networking paradigm to meet the growing demand for content access in the future. Because of its important role in accelerating content retrieval and reducing network transmission load, in-network caching has become one of the core technologies in CCN and has attracted wide attention. The existing caching schemes often lack sufficient consideration of node cache status and the temporal validity of user requests, and thus the cache efficiency of the network is greatly reduced. In this paper, a cache pressure-aware caching scheme is proposed, which comprehensively takes into account various factors such as content popularity, cache occupancy rate, cache replacement rate, and the validity period of the Interest packet to achieve reasonable cache placement and replacement. Simulation results show that the proposed scheme effectively improves the cache hit rate and the resource utilization while decreasing the average response hops.

Cost-Aware Resource Allocation for Optimization of Energy Efficiency in Fog Radio Access Networks

Taking full advantage of centralized cooperation and edge caching, fog radio access network has been considered as a promising paradigm to provide high spectral efficiency and energy efficiency. However, the overheads on fronthaul transmission and content caching also significantly affect the affordability, which makes it an increasingly urgent problem to achieve the performance improvement with reasonable overheads. In this paper, the economical energy efficiency (E 3 ) metric is adopted to comprehensively consider the impacts on different aspects. Under the constraints of maximum transmitting power, cache status, and fronthaul capacity, a resource allocation problem is formulated, in which throughput, energy consumption, as well as cost on fronthaul transmission and content caching are jointly considered. The problem is solved using fractional programming, weighted minimum mean square error approach, and greedy algorithm, and an adaptive transmitting method selection algorithm is proposed. The simulation results demonstrate the effectiveness and gains of the proposed algorithm, and the corresponding key factors impacting on E 3 are accordingly analyzed and evaluated.

Cache-Enabled Physical Layer Security for Video Streaming in Backhaul-Limited Cellular Networks

In this paper, we propose a novel wireless caching scheme to enhance the physical layer security of video streaming in cellular networks with limited backhaul capacity. By proactively sharing video data across a subset of base stations (BSs) through both caching and backhaul loading, secure cooperative joint transmission of several BSs can be dynamically enabled in accordance with the cache status, the channel conditions, and the backhaul capacity. Assuming imperfect channel state information (CSI) at the transmitters, we formulate a two-stage non-convex mixed-integer robust optimization problem for minimizing the total transmit power while providing quality of service (QoS) and guaranteeing communication secrecy during video delivery, where the caching and the cooperative transmission policy are optimized in an offline video caching stage and an online video delivery stage, respectively. Although the formulated optimization problem turns out to be NP-hard, low-complexity polynomial-time algorithms, whose solutions are globally optimal under certain conditions, are proposed for cache training and video delivery control. Caching is shown to be beneficial as it reduces the data sharing overhead imposed on the capacity-constrained backhaul links, introduces additional secure degrees of freedom, and enables a power-efficient communication system design. Simulation results confirm that the proposed caching scheme achieves simultaneously a low secrecy outage probability and a high power efficiency. Furthermore, due to the proposed robust optimization, the performance loss caused by imperfect CSI knowledge can be significantly reduced when the cache capacity becomes large.

Exploiting Caching for Millimeter-Wave TCP Networks: Gain Analysis and Practical Design

Millimeter-wave (mmWave) communications are a key enabler of gigabit access using large spectrum resources. Most relevant works focus on the design of radio access networks (RANs), but a transport layer design tends to be overlooked despite its importance as a data supply to the RAN. Recent studies reported that transmission control protocol (TCP)—the de facto standard for the transport layer—is problematic in mm-Wave communications because the sending rate cannot be controlled due to the drastic channel status change between line-of-sight (LoS) and non-LoS (NLoS); this is known as TCP performance collapse problem (TPCP). The TPCP can be alleviated if a cache is deployed in a base station, such that TCP packets from servers are stored in the cache during NLoS and forwarded to the RAN immediately when the channel becomes LoS. This paper overcomes the TPCP by making full use of the mmWave spectrum via efficient cache utilization. Here, a caching gain is analyzed and defined as the end-to-end rate ratio between TCPs with and without cache. This results show that the gain is maximized by forwarding packets to the RAN whenever the wireless channel is available. Based on this, we propose a novel cache-enabled TCP framework called mmWave TCP (mmTCP) with two key functionalities. The first is a batch retransmission, where packets likely to be lost during NLoS are simultaneously retransmitted once the channel becomes LoS. The second is an online cache management, where the cache is being reallocated to different users given the current channel and cache status. The performance of the mmTCP is evaluated by a realistic network simulator–version 3 under different environments, such that 48% and 10% end-to-end rate improvements are made in single- and multi-user cases, respectively.

Stochastic Content-Centric Multicast Scheduling for Cache-Enabled Heterogeneous Cellular Networks

Caching at small base stations (SBSs) has demonstrated significant benefits in alleviating the backhaul requirement in heterogeneous cellular networks (HetNets). While many existing works focus on what contents to cache at each SBS, an equally important problem is what contents to deliver so as to satisfy dynamic user demands given the cache status. In this paper, we study optimal content delivery in cache-enabled HetNets by taking into account the inherent multicast capability of wireless medium. We consider stochastic content multicast scheduling to jointly minimize the average network delay and power costs under a multiple access constraint. We establish a content-centric request queue model and formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). By using \emph{relative value iteration} and special properties of the request queue dynamics, we characterize some properties of the value function of the MDP. Based on these properties, we show that the optimal multicast scheduling policy is of threshold type. Then, we propose a structure-aware optimal algorithm to obtain the optimal policy. We also propose a low-complexity suboptimal policy, which possesses similar structural properties to the optimal policy, and develop a low-complexity algorithm to obtain this policy.

Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN

This paper presents a content-centric transmission design in a cloud radio access network (cloud RAN) by incorporating multicasting and caching. Users requesting a same content form a multicast group and are served by a same cluster of base stations (BSs) cooperatively. Each BS has a local cache and it acquires the requested contents either from its local cache or from the central processor (CP) via backhaul links. We investigate the dynamic content-centric BS clustering and multicast beamforming with respect to both channel condition and caching status. We first formulate a mixed-integer nonlinear programming problem of minimizing the weighted sum of backhaul cost and transmit power under the quality-of-service constraint for each multicast group. Theoretical analysis reveals that all the BSs caching a requested content can be included in the BS cluster of this content, regardless of the channel conditions. Then we reformulate an equivalent sparse multicast beamforming (SBF) problem. By adopting smoothed $\ell_0$-norm approximation and other techniques, the SBF problem is transformed into the difference of convex (DC) programs and effectively solved using the convex-concave procedure algorithms. Simulation results demonstrate significant advantage of the proposed content-centric transmission. The effects of three heuristic caching strategies are also evaluated.

Distributed proxy cache technology based on autonomic computing in smart cities

With the rapid growth of Internet services in smart cities, large network flow has caused network congestion among users, thereby deteriorating network service quality and user experience. In this study, a distributed proxy cache management platform based on autonomous decision is proposed. The platform realizes highly efficient automatic cache management by taking the advantages of autonomous perception and decision. First, the platform establishes the autonomous management framework of proxy cache on the basis of the idea of feedback loop. Next, the URL is distributed evenly among cache nodes through the hash ring storage mechanism with virtual nodes. Cache status adjustment strategies are made based on historical status sequences and the gray prediction model. A method that is used to predict content hot-rank based on domain name set is proposed to reduce loads of cache nodes. Second, the platform designs a distributed proxy cache management system by depending on the autonomous management framework of proxy cache and the cache management mechanism of autonomous decision, and the Cache Hot Spots Migrate algorithm is suggested for the dynamic migration and integration of virtual nodes on the hash ring. A system knowledge base is established through status sequence and corresponding adjustment strategies. Finally, experimental results on the hash ring, content hot-rank prediction, and adjustment of autonomous decision of cache status are analyzed, thereby confirming that the distributed proxy cache management mechanism based on autonomous decision could manage cache nodes effectively and automatically, and could improve the overall performance of the system.

Supporting faulty banks in NUCA by NoC assisted remapping mechanisms

The many-core SoC is a future trend technology, and the process yield will face many unpredictable challenges. Nonuniform cache architecture (NUCA) can improve the performance of many-core SoC for embedded systems. It embeds a NoC into the cache memory to enhance the data access by distributing traffic loads to several banks in parallel. Providing fault-tolerant mechanism in NUCA is very important because the chip can still work efficiently when some memory banks are unusable. In this paper, we design a specific router working with static and dynamic cache remapping policies to support faulty banks in NUCA. When a L2 cache bank in NUCA is unusable, static remapping policy (SRP) selects a suitable neighbor cache bank according to the collected remapping cost to assist with the cache access by considering cache status and traffic status of the router. We also propose a dynamic remapping policy (DRP) to select the suitable cache bank dynamically at runtime to fit the real loading status of neighbor nodes around the faulty bank. The experimental results show that the average improvement of the SRP is approximated to 26 %, and the average improvement of the DRP is approximated to 28 %.

Application controlled caching for web servers

Today's web servers must have the ability to deal with large data sets, and their performance mainly depends on the control mechanism of the disc cache. Though the cache replacement algorithms in operating systems generally perform well, application-specific policies can often perform much better. In this paper, we present the application-controlled caching in user space (ACCUS) mechanism for web server disc caching. With this mechanism, an application can schedule the service requests based on the cache status of the requested files to improve its performance so that the system can gain a high parallelism of CPU processing, networking I/O and disc I/O. An application can service the cached files with a higher priority to the ones not cached, which helps reduce the latency caused by disc I/O blocking. Meanwhile, the application can enforce a policy for domain-specific cache management to obtain a higher cache hit ratio. ACCUS is implemented in two web servers of different models, Flash and userver. Despite the great differences in architectures, the empirical results show that both servers can achieve high throughput under heavy workloads with ACCUS enabled. Performance analysis for two other typical web servers, Apache™ and Zeus Web Server are also conducted and the results indicate that ACCUS can achieve significant improvement in performance.

Coordinated enroute multimedia object caching in transcoding proxies for tree networks

Transcoding is a promising technology that allows systems to effect a quality-versus-size tradeoff on multimedia objects. As audio and video applications have proliferated on the Internet, caching in transcoding proxies has become an important technique for improving network performance, especially in mobile networks. This article addresses the problem of coordinated enroute multimedia object caching in transcoding proxies for tree networks. We formulate this problem as an optimization problem based on our proposed model, in which multimedia object caching decisions are made on all enroute caches along the routing path by integrating both object placement and replacement policies and cache status information along the routing path of a request is used to determine the optimal locations for caching multiple versions of the same multimedia object. We propose an optimal solution using dynamic programming to compute the optimal locations. We also extend this solution to solve the same problem for several constrained cases, including constraints on the cost gain per node and on the number of versions to be placed. Our model is evaluated on different performance metrics through extensive simulation experiments. The implementation results show that our model significantly outperforms existing models that consider Web caching in transcoding proxies either on a single path or at individual nodes.