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With increasing demand for video streaming applications, exploiting cached content at the network edge becomes paramount to prevent congestion in the link between the wireless access network and the content providers. However, it is often challenging to exploit the caches in current client-driven video streaming solutions due to two key reasons. First, even those clients interested in the same content might request different quality levels as a video content is encoded into multiple qualities to match a wide range of network conditions and device capabilities. Second, the clients, who select the quality of the next chunk to request, are unaware of the cached content at the network edge. Hence, it becomes imperative to develop network-side solutions to exploit caching, in particular for the scenarios in which multiple video clients compete for some bottleneck capacity. In this article, we propose EdgeDASH which is a network-side control logic running at a WiFi AP to facilitate the use of cached video content. In particular, an AP can assign a client station to a video quality different than its request, in case the alternative quality provides a better utility. This includes, for example, a function of bits delivered from the cache, video bit rate, and the buffer stalls. We formulate the quality assignment problem as an optimization problem and develop several heuristics with polynomial complexity. Our simulations show that EdgeDASH facilitates significant cache hits and decreases the buffer stalls only by changing the client's request by one quality level, however with some increase in session instability. From our analysis, we conclude that EdgeDASH benefits are more visible especially when the clients with identical interests compete for a bottleneck link's capacity, over the baseline where the clients determine the quality adaptation.

The paper is motivated by the urgent need, imposed by the COVID-19 pandemic, for trustworthy access to secure communication systems with the highest achievable availability and minimum latency. In this regard, we focus on an ultra-dense wireless network consisting of Femto Access Points (FAPs) and Unmanned Aerial Vehicles (UAVs), known as caching nodes, where there are more than one possible caching node to handle user's request. To efficiently cope with the dynamic topology of wireless networks and time-varying behavior of ground users, our focus is to develop an efficient connection scheduling framework, where ground users are autonomously trained to determine the optimal caching node, i.e., UAV or FAP. Our aim is to minimize users' access delay by maintaining a trade-off between the energy consumption of UAVs and the occurrence of handovers. To achieve these objectives, we formulate a multi-objective optimization problem and propose the Convolutional Neural Network (CNN) and Q-Network-based Connection Scheduling (CQN-CS) framework. More specifically, to solve the constructed multi-objective connection scheduling problem, a deep Q-Network model is developed as an efficient Reinforcement Learning (RL) approach to train ground users to handle their requests in an optimal and trustworthy fashion within the coupled UAV-based femtocaching network. The effectiveness of the proposed CQN-CS framework is evaluated in terms of the cache-hit ratio, user's access delay, energy consumption of UAVs, handover, lifetime of the network, and cumulative rewards. Based on the simulation results, the proposed CQN-CS framework illustrates significant performance improvements in companion to Q-learning and Deep Q-Network (DQN) schemes across all the aforementioned aspects.

Information centric networking (ICN) shifts the focus of existing internet architecture from host-oriented to content-oriented model by enabling in-network caching and content-based forwarding. These ICN features help to increase network performance by decreasing content discovery delay, content server load, and network congestion. To route a content interest inside network such that content can be fetched with minimal time is a challenging task in ICN. The performance of the ICN routing protocol can be significantly improved if the decisions related to content chunk placement and request forwarding are taken in a cooperative fashion. This paper describes a novel strategy for co-operative caching joint request forwarding in ICN, focusing on decreasing content retrieval latency. To do so, the caching strategy leverages the concept of connected dominating set (CDS) for creating a virtual backbone network to eliminate caching redundancy and reduce content discovery delay. It considers content chunk placement and request forwarding tasks as strongly co-related procedures. It exploits the caching information so that the request is forwarded to a content router (CR) with the maximum likelihood of carrying needed data using the betweenness centrality (BC) of CR. The proposed approach also uses the Markov chain-based model to estimate CS hit likelihood and use it as decisional parameter while forwarding the interest packet. This mechanism helps to fetch the content within the shortest possible time duration. The CCJRF-ICN adopts the Dijkstra’s shortest path routing and works in collaboration with the CDS-driven caching joint forwarding mechanism. The simulation study of CCJRF-ICN is done inside ns-3 based ndnSIM-2.0 simulator with performance measures like content store (CS) hit ratio, content discovery delay, mean hop distance, network load, and network overhead. The simulation outcomes demonstrate that CCJRF-ICN outperforms the state-of-the-art strategies for realistic topologies (GEANT, US-26, Euro-28) and shows improvement up to 5-35% against stated performance measures.

In this paper, we analyze the impact of caching on the performance of a cache enabled system with heterogeneous traffic where one of the users need to be served with confidential data. In this setup, a wireless helper system always serves a dedicated user and it can also serve a user requesting cached content. A cellular network access point is also available to serve the latter user if it cannot retrieve the requested data from the helper’s cache. The impact of caching and secrecy on throughput and delay performance for each user is then examined when the access point can deploy superposition coding to serve both users simultaneously. Two decoding schemes are considered in this work. The first decoding scheme treats interference from parallel transmissions as noise while the second one utilizes the parallel transmission to apply successive decoding for the intended data. Furthermore, network and cache related factors are identified and their impact on the overall performance of the system are analyzed. In order to find the optimal transmission power allocations, two distinct optimization problems are set in this context comparing the two decoding schemes. This will assist to identify the benefits of the considered decoding schemes for each user satisfying the secrecy requirements of the dedicated user and reducing its impact on the overall performance of the system. Funding: Swedish Foundation for Strategic Research (SSF)Swedish Foundation for Strategic Research; Swedish Research Council (VR)Swedish Research Council; Center for Industrial Information Technology (CENIIT); Excellence Center at Linkoping-Lund in Information Technology (ELLIIT); Indo-Sweden Project, Department of Science and Technology, India

Offloading data traffic from Infrastructure-to-Device (I2D) to Device-to-Device (D2D) communications is a powerful tool for reducing congestion, energy consumption, and spectrum usage of mobile cellular networks. Prior network-level studies on D2D data offloading focus on high level performance metrics as the offloading efficiency, and take into account the radio propagation aspects by using simplistic wireless channel models. In this work, we consider a D2D data offloading protocol tailored to highly dynamic scenarios as vehicular environments, and evaluate its performance focusing on physical layer aspects, like energy consumption and spectral efficiency. In doing this, we take into account more realistic models of the wireless channel, with respect to the simplistic ones generally used in the previous studies. Our objective is twofold: first, to quantify the performance gain of the considered D2D offloading protocol with respect to a classic cellular network, based on I2D communications, in terms of energy consumption and spectral efficiency. Second, to show that using simplistic channel models may prevent to accurately evaluate the performance gain. Additionally, the use of more elaborated models allows to obtain insightful information on relevant system-level parameters settings, which would not be possible to obtain by using simple models. The considered channel models range from widely used models based on deterministic path loss formulas, to more accurate ones, which include effects like multipath fading and the associated frequency selectivity of wideband channels. These models have been proposed and validated, in the recent years, through large-scale measurements campaigns. Our results show that the considered protocol is able to achieve a reduction in the energy consumption of up to 35%, and an increase in the system spectral efficiency of 50%, with respect to the benchmark cellular system. The use of different channel models in evaluating these metrics may result, in the worst case, in a sixfold underestimation of the achieved improvement.

Sharding is a method for allocating data items to nodes of a distributed caching or storage system based on the result of a hash function computed on the item’s identifier. It is ubiquitously used in key-value stores, CDNs and many other applications. Despite considerable work that has focused on the design and implementation of such systems, there is limited understanding of their performance in realistic operational conditions from a theoretical standpoint. In this paper we fill this gap by providing a thorough modeling of sharded caching systems, focusing particularly on load balancing and caching performance aspects. Our analysis provides important insights that can be applied to optimize the design and configuration of sharded caching systems.

Fog radio access networks (F-RANs) can effectively alleviate fronthaul loads and reduce content transmission delay by migrating cloud services to the network edge. This paper addresses a cooperative caching scenario in F-RAN, where each mobile user can acquire the requested contents from any one of its associated fog-computing-based access points (F-APs). However, caching disparate contents in different F-APs will lead to different content delivery delays, since mobile users suffer from diverse channel fadings and interferences when they download contents from different F-APs. Considering limited caching storage in each F-AP, diverse user preferences, unpredictable user mobility and time-varying channel states, an average transmission delay minimization problem is formulated. With the aid of dueling deep-Q-network framework, a delay-aware cache update policy is proposed for mobile users in F-RAN. The proposed cache update policy will decide to replace the stored contents in F-APs with the proper contents at each time slot. Compared with first in first out, least recently used and least frequently used caching policies, simulation experiments are performed to evaluate the performance of the proposed algorithm. Simulation results illustrate that the proposed caching policy yields better average hit ratio and lower average transmission delay than other traditional caching policies.

Various advanced and mission-critical applications are enabled by the emerging technologies in fifth-generation (5G) mobile communication systems. To ensure improved quality of experience (QoE) of users, 5G and beyond networks require ultra-reliable low-latency communications (URLLC). The successful realization of the URLLC entails the advent of new technological concepts. Therefore, this article presents an overview of the enabling techniques for the URLLC. Classification of the enabling techniques is done and an extensive review of the literature is presented to identify the state-of-the-art techniques, limitations, and the potential approaches for alleviating the limitations. It is observed that artificial intelligence (AI)-enabled edge computing and caching solutions are widely explored as promising techniques to effectively guarantee low latency and reliable content acquisition while reducing redundant network traffic and improving the QoE. Therefore, we present a classification of the AI-enabled edge caching solutions and discuss various mechanisms of the caching agents. In particular, we investigate the use of deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) algorithms. Subsequently, we analyze the performance of the state-of-the-art edge caching schemes and demonstrate the performance gains of FL frameworks over conventional centralized and decentralized DL and DRL frameworks. We confirm that FL edge caching is a viable mechanism in 5G and beyond networks. On the other hand, it is shown that the IEEE 802.1 time sensitive networking and the emerging IETF deterministic networking standards present effective mechanisms when deterministic networks with bounded ultra-low latency are considered. Finally, we present the open issues and opportunities for further research.

Data communication in the present Internet paradigm is dependent on fixed locations that disseminate similar data several times. As a result, the number of problems has been generated in which location dependency is the most crucial for communication. Therefore, Named Data Networking (NDN) is a new network architecture that revolutionized the handling gigantic amount of data generated from diverse locations. The NDN offers in-network cache which is the most beneficial feature to reduce the difficulties of location-based Internet paradigms. Moreover, it mitigates network congestion and provides a short stretch path in the data downloading procedure. The current study explores a new comparative analysis of popularity-based cache management strategies for NDN to find the optimal caching scheme to enhance the overall network performance. Therefore, the content popularity-based caching strategies are comparatively and extensively studied in an NDN-based simulation environment in terms of most significant metrics such as hit ratio, content diversity ratio, content redundancy, and stretch ratio. In this analysis, the Compound Popular Content Caching Strategy (CPCCS) has performed better in terms to enhance the overall NDN-based caching performance. Therefore, it is suggested that the CPCCS will perform better to achieve enhanced performance in emerging environments such as, Internet of Things (IoT), Fog computing, Edge computing, 5G, and Software Defined Network (SDN).

In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching.

Autoencoders implemented by artificial neural networks (ANNs) are utilized to learn the latent space representation of data in an unsupervised manner, and they have been widely used in recommender systems. For instance, several collaborative denoising autoencoder (CDAE) models have shown that their performance gains outperform that of the collaborative filtering based (CF-based) models. In this work, a near-optimal Top- $K$ forecasting solution is proposed for our advanced autoencoder recommender systems. We propose a method which utilizes CDAE model in predicting the Top- $K$ popular videos in an upcoming time period. In order to improve the prediction accuracy, we also propose an autoencoder based recommendation algorithm with the help of $K$ -means clustering that upgrades the performance of the original autoencoder model. The experimental results show that our method increases significantly the Average Precision (AP) and Recall values by nearly 30%. We then further utilize our proposed autoencoder model with clustering in predicting Top- $K$ popular videos. The applications of predicting Top- $K$ popular videos can be used in the video delivery for the Mobile Edge Computing (MEC) environment to avoid bottleneck in the constricted capacity of backhaul link. Namely, the performance gain will be upgraded if our proposed method precisely predicts and caches the Top- $K$ popular videos in advance with the help of a better forecasting model.

The ever-increasing number of diverse and computation-intensive Internet of things (IoT) applications is bringing phenomenal growth in global Internet traffic. Mobile devices with limited resource capacity (i.e., computation and storage resources) and battery lifetime are experiencing technical challenges to satisfy the task requirements. Mobile edge computing (MEC) integrated with IoT applications offloads computation-intensive tasks to the MEC servers at the network edge. This technique shows remarkable potential in reducing energy consumption and delay. Furthermore, caching popular task input data at the edge servers reduces duplicate content transmission, which eventually saves associated energy and time. However, the offloaded tasks are exposed to multiple users and vulnerable to malicious attacks and eavesdropping. Therefore, the assignment of security services to the offloaded tasks is a major requirement to ensure confidentiality and privacy. In this article, we propose a green and secure MEC technique combining caching, cooperative task offloading, and security service assignment for IoT networks. The study not only investigates the synergy between energy and security issues, but also offloads IoT tasks to the edge servers without violating delay requirements. A resource-constrained optimization model is formulated, which minimizes the overall cost combining energy consumption and probable security-breach cost. We also develop a two-stage heuristic algorithm and find an acceptable solution in polynomial time. Simulation results prove that the proposed technique achieves notable improvement over other existing strategies.

International audience; This paper focuses on similarity caching systems, in which a user request for an object o that is not in the cache can be (partially) satisfied by a similar stored object o , at the cost of a loss of user utility. Similarity caching systems can be effectively employed in several application areas, like multimedia retrieval, recommender systems, genome study, and machine learning training/serving. However, despite their relevance, the behavior of such systems is far from being well understood. In this paper, we provide a first comprehensive analysis of similarity caching in the offline, adversarial, and stochastic settings. We show that similarity caching raises significant new challenges, for which we propose the first dynamic policies with some optimality guarantees. We evaluate the performance of our schemes under both synthetic and real request traces.

Recently, many Low Earth Orbit (LEO) satellite networks are being implemented to provide seamless communication services for global users. Since the high mobility of LEO satellites, handover strategy has become one of the most important topics for LEO satellite systems. However, the limited on-board caching resource of satellites make it difficult to guarantee the handover performance. In this paper, we propose a multiple attributes decision handover strategy jointly considering three factors, which are caching capacity, remaining service time and the remaining idle channels of the satellites. Furthermore, a caching-aware intelligent handover strategy is given based on the deep reinforcement learning (DRL) to maximize the long-term benefits of the system. Compared with the traditional strategies, the proposed strategy reduces the handover failure rate by up to nearly 81% when the system caching occupancy reaches 90%, and it has a lower call blocking rate in high user arrival scenarios. Simulation results show that this strategy can effectively mitigate handover failure rate due to caching resource occupation, as well as flexibly allocate channel resources to reduce call blocking.

Mobile Edge Computing (MEC) is relatively a novel concept in the parlance of Computational Offloading. MEC signifies the offloading of intensive computational tasks to the cloud which is generally positioned at the edge of a mobile network. Being in an embryonic stage of development, not much research has yet been done in this field despite its potential promises. However, with time the advantages are gaining growing attention and MEC is gradually taking over some of the resource-intensive functionalities of a traditional centralized cloud-based system. Another new idea called Task Caching is emerging rapidly with the offloading policy. This joint optimization idea of Task Offloading and caching is relatively a very new concept. It has been in use for reducing energy consumption and delay time for mobile edge computing. Due to the encouraging offshoots from some of the current research on the joint optimization problem, this research initiative aims to take the progress forward. The work improves upon the “prioritization of the tasks” by adopting a very practical approach discussed forward, and proposes a different way for Task Offloading and caching to the edge of the cloud, thereby bringing a significant enhancement to the QoS of MEC.

Standard time-to-live (TTL) cache management prescribes the storage of entire files, or possibly fractions thereof, for a given amount of time after a request. As a generalization of this approach, this letter proposes the storage of a time-varying, diminishing, fraction of a requested file. Accordingly, the cache progressively evicts parts of the file over an interval of time following a request. The strategy, which is referred to as soft-TTL, is justified by the fact that traffic traces are often characterized by arrival processes that display a decreasing, but non-negligible, probability of observing a request as the time elapsed since the last request increases. An optimization-based analysis of soft-TTL is presented, demonstrating the important role played by the hazard function of the inter-arrival request process, which measures the likelihood of observing a request as a function of the time since the most recent request.

To address the drastic growth of data traffic dominated by streaming of video-on-demand files, mobile edge caching/computing (MEC) can be exploited to develop intelligent content caching at mobile network edges to alleviate redundant traffic and improve content delivery efficiency. Under the MEC architecture, content providers (CPs) can deploy popular video files at MEC servers to improve users' quality of experience (QoE). Designing an efficient content caching policy is crucial for CPs due to the content dynamics, unknown spatial-temporal traffic demands, and limited service capacity. The knowledge of users' preference is very useful and important for efficient content caching, yet often unavailable in advance. Under this circumstance, machine learning can be used to learn the users' preference based on historical demand information and decide the video files to be cached at the MEC servers. In this paper, we propose a multi-agent reinforcement learning (MARL)-based cooperative content caching policy for the MEC architecture when the users' preference is unknown and only the historical content demands can be observed. We formulate the cooperative content caching problem as a multi-agent multi-armed bandit problem and propose a MARL-based algorithm to solve the problem. The simulation experiments are conducted based on a real dataset from MovieLens and the numerical results show that the proposed MARL-based cooperative content caching scheme can significantly reduce content downloading latency and improve content cache hit rate when compared with other popular caching schemes.

As a promising architecture in the future networks, fog radio access network (F-RAN) can effectively improve network performance due to the advantages in both communication and computation aspects. In this paper, the data caching scheme is studied in a device-to-device (D2D) enabled the F-RAN from the social point of view. First, we help each access point (AP) to ascertain its best belonging community in terms of the content preference based on the projective adaptive resonance theory neural network (PART NN). The APs within identical community cache data cooperatively, which can reduce the caching redundancy. Considering the content preference of the AP may change dynamically, a community update scheme is also studied after the community construction is completed. In order to further reduce the content delivery delay, the D2D technology is resorted to and the most appropriate user equipment (UE) is selected to cache data for other UEs within each AP’s coverage. After that, an ant colony optimization scheme is proposed to find out the most requested contents needed to be cached by such most appropriate UE. The effectiveness of the proposed scheme is finally validated by the simulation experiments.

The proliferation of network devices and novel bandwidth hungry applications over the existing network imposes novel challenges in terms of fulfilling the users’ requirements. Dense deployment of small cells is thought to be a promising solution to fulfill these requirements. However, the user association in such dense networks becomes challenging and can greatly affect the network performance, as a user in such dense deployments can be connected to any of the available base stations. Traditionally, the user association has been performed based on the signal strength, however, such an approach does not apply when taking into account novel bandwidth hungry applications. Moreover, in recent years, a successful paradigm has been proposed to handle such bandwidth hungry applications, i.e., caching at small cell base stations. In this paper, we aim to solve this joint problem of user association and content caching in a dense small cell setting. To solve this problem, we present a novel iterative scheme that uses matching theory and a learning approach to find a suboptimal solution of the joint NP hard problem. Note that the user association and cache placement are strongly coupled, i.e., the association of users at a base station will determine the cache placement at base stations and the availability of cache at base stations will force the users to change their associations. Simulation results show that the proposed scheme (i.e., cache aware user association) significantly outperforms the cache unaware scheme and achieves a performance gain of up to 31% in terms of normalized utility and saves up to twice the backhaul bandwidth. Moreover, the proposed scheme also achieves up to 82% of the utility obtained by the optimal solution.

Proactive content caching at the wireless network edge, such as users and small base stations (SBSs), is an effective way to deal with high mobile traffic. In this paper, based on the user mobility and social relationships, we investigate the optimal caching strategy in device-to-device (D2D) communications underlying heterogeneous networks, where several SBSs are within the coverage of a macrobase station (MBS). Except for SBSs, important users (IUs) hired by an operator also cache files. First, assuming that user preference, i.e., the probability distribution of different file requests of users, are unknown, we cluster users and predict each user preference based on their history file requests by fitting to the Zipf distribution. Second, we derive the closed-form expression of the average system cost by jointly considering the mobility of users and the social relationship between them. With the purpose of minimizing the average system cost, we optimize the SBSs and IU caching strategies. The optimization problem is NP-complete. To solve the problem, we demonstrate that this problem belongs to the minimization of a supermodular function over a partition matroid, and thus, we provide a locally greedy caching algorithm with an approximation ratio of 2 to obtain the sub-optimal solution in polynomial time. Finally, since the operator can reduce the system cost by hiring more IUs, requiring higher payments, we reach a tradeoff by determining the number of IUs. The simulation results show that the proposed caching strategy outperforms the traditional caching strategy, and the suboptimal solution obtained by proposing the greedy algorithm is close to the optimal solution.

Named data networking (NDN) and edge cloud computing (ECC) are emerging technologies that are considered as the most representative technologies for the future Internet. Both technologies are the promising enabler for the future Internet such as fifth generation (5G) and beyond which requires fast information response time. We believe that clear benefits can be achieved from the interplay of NDN and ECC and enables future network technology to be much more flexible, secure and efficient. In this paper, therefore, we integrate NDN with ECC in order to achieve fast information response time. Our framework is based on N-Tier architecture and comprises of three main Tiers. The NDN is located at the Tier1 (Things/end devices) and comprises of all the basic functionalities that connect Internet of Things (IoT) devices with Tier 2 (Edge Computing), where we have deployed our Edge node application. The Tier 2 is then further connected with Tier 3 (Cloud Computing), where our Cloud node application is deployed at multiple hops on the Microsoft Azure Cloud machine located in Virginia, WA, USA. We implement an NDN-based ECC framework and the outcomes are evaluated through testbed and simulations in terms of interest aggregation, round trip time (RTT), service lookup time with single query lookup time and with various traffic loads (load-based lookup time) from the IoT devices. Our measurements show that enabling NDN with edge computing is a promising approach to reduce latency and the backbone network traffic and capable of processing large amounts of data quickly and delivering the results to the users in real time.

Proactive content caching in a fog radio access network (F-RAN) is an efficient technique used to alleviate delivery delay and traffic congestion. However, the symmetric caching of the content is impractical due to the dissimilarity among the contents popularity. Therefore, in this paper, a multi-objective random caching scheme to balance the successful transmission probability (STP) and delay in wireless backhauled F-RAN is proposed. First, stochastic geometry tools are utilized to derive expressions of the association probability, STP, and average delivery delay. Next, the complexity is reduced by considering the asymptotic STP and delay in the high signal-to-noise ratio (SNR) regime. Then, aiming at maximizing the STP or minimizing the delay, the multi-objective cache placement optimization problem is formulated. A novel projected multi-objective cuckoo search algorithm (PMOCSA) is proposed to obtain the Pareto front of the optimal cache placement. The numerical results show that PMOCSA outperforms the original multi-objective cuckoo search algorithm (MOCSA) in terms of convergence to a feasible Pareto front and its rate. It also shows that the proposed multi-objective caching scheme significantly outperforms the well-known benchmark caching schemes by up to 40% higher STP and 85% lower average delay.

Caching at the edge of wireless networks is a key technology to reduce traffic in the backhaul link. However, ${a}$ concentrated amount of requests during peak-periods may cause the outage of the system, meaning that the network is not able to serve the whole set of demands. The outage probability is a fundamental metric to take into account during the network design. In this paper, we derive the analytical expression of the outage probability as a function of the total amount of users requests, library size, requests distribution, cache size and capacity constraints on the backhaul resources. In particular, we focus on a scenario where end-users have no direct connection to the master node which holds the complete library of content that can be requested. A general formulation of the outage is derived and studied for two relevant caching schemes, i.e. the random caching scheme and the most popular caching schemes. The exact closed form expressions presented in this paper provide useful insights on how requests, memory and resources can be balanced when the parameters of a cache-enabled network have to designed.

International audience; We consider a dense cellular network, in which a limited-size cache is available at every base station (BS). Coordinating content allocation across the different caches can lead to significant performance gains, but is a difficult problem even when full information about the network and the request process is available. In this paper we present q LRU- Δ , a general-purpose online caching policy that can be tailored to optimize different performance metrics also in presence of coordinated multipoint transmission techniques. The policy requires neither direct communication among BSs, nor a priori knowledge of content popularity and, under stationary request processes, has provable performance guarantees.

The communication network is growing with some unique characteristics, such as consumers repeatedly request the same content to the server, similarity in local demand trend, and dynamic changes to requests within a specific period. Therefore, a different network paradigm is needed to replace the IP network, namely Named Data Network (NDN). The content store, which acts as a crucial component in the NDN nodes is a limited resource. In addition, a cache mechanism is needed to optimize the router&rsquo, s content store by exploiting the different content services characters in the network. This paper proposes a new caching algorithm called Cache Based on Popularity and Class (CAPIC) with dynamic mechanism, and the detail explanation about the static method also presented. The goal of Static-CAPIC was to enhance the total cache hit ratio on the network by pre-determining the cache proportion for each content class. However, this technique is not appropriate to control the cache hit ratio for priority class. Therefore, the Dynamic-CAPIC is used to provide flexibility to change the cache proportion based on the frequency of requests in real-time. The formula involves considering the consumers&rsquo, request all the time. It gives a higher cache hit ratio for the priority content class. This method outperforms Static-CAPIC, and the LCD+sharing scheme in the total network cache hit ratio parameter and channels it to the priority class.

The limited caching capacity of the local cache enabled Base station (BS) decreases the cache hit ratio (CHR) and user satisfaction ratio (USR). However, Cache enabled multi-tier cellular networks have been presented as a promising candidate for fifth generation networks to achieve higher CHR and USR through densification of networks. In addition to this, the cooperation among the BSs of various tiers for cached data transfer, intensify its significance many folds. Therefore, in this paper, we consider maximization of CHR and USR in a multi-tier cellular network. We formulate a CHR and USR problem for multi-tier cellular networks while putting major constraints on caching space of BSs of each tier. The unsupervised learning algorithms such as K-mean clustering and collaborative filtering have been used for clustering the similar BSs in each tier and estimating the content popularity respectively. A novel scheme such as cluster average popularity based collaborative filtering (CAP-CF) algorithm is employed to cache popular data and hence maximizing the CHR in each tier. Similarly, two novel methods such as intra-tier and cross-tier cooperation (ITCTC) and modified ITCTC algorithms have been employed in order to optimize the USR. Simulations results witness, that the proposed schemes yield significant performance in terms of average cache hit ratio and user satisfaction ratio compared to other conventional approaches.

International audience; Current mobile cloud database systems are widespread and require special considerations for mobile devices. Although many systems rely on numerous metrics for use and optimization, few systems leverage metrics for decisional cache replacement on the mobile device. In this paper we introduce the Lowest Scored Replacement (LSR) policy-a novel cache replacement policy based on a predefined score which leverages contextual mobile data and user preferences for decisional replacement. We show an implementation of the policy using our previously proposed MOCCAD-Cache as our decisional semantic cache and our Normalized Weighted Sum Algorithm (NWSA) as a score basis. Our score normalization is based on the factors of query response time, energy spent on mobile device, and monetary cost to be paid to a cloud provider. We then demonstrate a relevant scenario for LSR, where it excels in comparison to the Least Recently Used (LRU) and Least Frequently Used (LFU) cache replacement policies.

Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.

In this work, we consider a system where external requests arrive for status updates of a remote source, which is monitored by an energy harvesting (EH) sensor. The requests are placed in an aggregator that has direct communication with the sensor and is also equipped with storage space to cache a previously generated status update. A probabilistic model is considered to determine whether a new request will be served with a fresh update from the EH sensor or with a cached update from the aggregator. In the first case, the fresh update will replace the cached one in the aggregator. Assuming that the energy arrivals at the sensor can be modeled by a Bernoulli process, we characterize the average Age of Information (AoI) of the source seen at the aggregator as a function of the external request probability, the battery charging probability, and the probability that a fresh update will be generated by the EH sensor. Our numerical results reveal some insights about the role of caching in EH-based status updating systems.

Wireless device-to-device (D2D) caching networks are studied, in which n nodes are distributed uniformly at random over the network area. Each node caches M files from the library of size m &ge, M and independently requires a file from the library. Each request will be served by cooperative D2D transmission from other nodes having the requested file in their cache memories. In many practical sensor or Internet of things (IoT) networks, there may exist simple sensor or IoT devices that are not able to perform real-time rate and power control based on the reported channel quality information (CQI). Hence, it is assumed that each node transmits a file with a fixed rate and power so that an outage is inevitable. To improve the outage-based throughput, a cache-enabled interference cancellation (IC) technique is proposed for cooperative D2D file delivery which first performs IC, utilizing cached files at each node as side information, and then performs successive IC of strongly interfering files. Numerical simulations demonstrate that the proposed scheme significantly improves the overall throughput and, furthermore, such gain is universally achievable for various caching placement strategies such as random caching and probabilistic caching.

FutureWei Technologies, 16th International Conference on Network and Service Management, CNSM 2020, 2nd International Workshop on Analytics for Service and Application Management, AnServApp 2020 and 1st International Workshop on the Future Evolution of Internet Protocols, IPFuture 2020 -- 2 November 2020 through 6 November 2020 -- -- 165563, Dynamic Adaptive Streaming over HTTP (DASH) is a technology designed to deliver video to the end-users in the most efficient way possible by providing the users to adapt their quality during streaming. In DASH architecture, the original content encoded into video streams in different qualities. As a protocol running over HTTP, the caches play an important role in DASH environment. Utilizing the cache capacity in these systems is an important problem where there are more than one encoded video files generated for each video content. In this paper, we propose a caching approach for DASH systems by predicting the future qualities of DASH clients. For the prediction, we use learning model, and the qualities that will be cached are determined by using this model. The learning model is designed using Recurrent Neural Networks (RNNs) and also Long Short Term Memory (LSTM) which is a special type of RNNs with default behavior of remembering information for long periods of time. We also utilize SDN technology to get some of the outputs for the learning algorithm. The simulation results show that predicting future qualities helps to reduce the underruns of the clients when cache storage is utilized. © 2020 IFIP., 115E449, ACKNOWLEDGMENT This work is funded by the Scientific and Technological Research Council of Turkey (TUBITAK) Electric, Electronic and Informatics Research Group (EEEAG) under grant 115E449.

International audience; The Content-Centric Networking (CCN) is an efficient traffic handling technology by accessing content with its name instead of its physical location and achieving in-network caching. Indeed, CCN caching ensures high content availability, network traffic reduction, and low retrieval latency which reduces congestion and improves end-to-end delay. Moreover, it could greatly improve the efficiency of content delivery in Wireless Sensor Networks (WSNs). In this article, we propose to exploit this feature to enable CCN in WSN environments. The CCN architecture enables the content caching on each sensor-node in WSN and several research studies have been devoted to the caching management issue in such a context. However, caching the content on all the nodes is not a good strategy in terms of resource utilization. It is, therefore, necessary to determine where to cache and how to handle it in order to optimize the resources while realizing a high-interest satisfaction rate. Thus, our objective is to study existing caching strategies and propose a novel one that takes into account the node centrality and its distance from the source of the content. Through extensive simulations, we examine the performance of our scheme under different configurations and demonstrate how it outperforms the traditional caching strategies such as LCE (Leave Copy Everywhere) and LCD (Leave Copy Down).

While the cost of the access network could be considerably reduced by the use of caching, this is not currently happening because content providers (CPs), who alone have the detailed demand data required for optimal content placement, have no natural incentive to use them to minimize access network operator (ANO) expenditure. We argue that ANOs should therefore provide such an incentive in the form of direct subsidies paid to the CPs in proportion to the realized savings. We apply coalition game theory to design the required subsidy framework and propose a distributed algorithm, based on Lagrangian decomposition, allowing ANOs and CPs to collectively realize the optimal memory for bandwidth tradeoff. The considered access network is a cache hierarchy with per-CP central office caches, accessed by all ANOs, at the apex, and per-ANO dedicated bandwidth and storage resources at the lower levels, including wireless base stations, that must be shared by multiple CPs.

Bloom filters are widely used in networking to accelerate checks and in particular to avoid accessing slow memories when a match will not be found. Unfortunately, filtering requires several on-chip memory bits per element and thus when the tables are large and the on-chip memory small is not applicable. In those cases, caching the most frequently accessed elements on-chip seems the only viable option. However, the key is typically formed by several packet header fields, which means that each cache entry consumes a significant amount of on-chip memory bits. In this paper, an efficient scheme to cache negatives, that is elements that will not find a match on the table is presented. In more detail, the proposed scheme enables the caching of negatives using less than 16 bits per entry regardless of the size of the key. This translates to a reduction of at least 6x in the size of the cache when used for example flow tracking.

International audience; A high throughput does not necessarily translate to a good Quality of Experience, especially in a satellite context. The round trip time, for instance, also has a tremendous impact on the reactivity of applications and thus on the Quality of Experience. Content delivery networks are massively used for over‐the‐top services in terrestrial network: They reduce the load of network and the delay as they draw the content closer to the end user. In a satellite system, the content delivery network presents a good opportunity for enhancing the end users' Quality of Experience and can change the conventional use of performance‐enhancing proxies. This paper investigates the satellite as access link for home networks or a backhaul link for small cells for a 5G perspective. We analysed the impact of caching on both gateway side and satellite terminal side for 2 on‐trend services: web browsing and adaptive video streaming (dynamic adaptive streaming over HTTP). The main contribution is an evaluation of transparent caching through a satellite platform. The caching policy is out of the scope of this paper. One large part of the testbed is based on an open‐source platform, OpenSAND that emulates the satellite system. To confirm the results, some real experiments have been conducted on a commercial satellite link. As expected, the transparent caching at the satellite terminal side can increase the Quality of Experience to its upper border as long as the content is available in the cache. For the satellite gateway cache, the performances exceed the expectations. Although the application experiences the satellite delay in this case, the traversal time of different Internet service provider networks also delays the delivery of content. Then it may have a greater impact on reactivity than the satellite itself. Through careful analysis of the different results, we noticed some issues. Transparent caching is unable to cache encrypted or dynamic content. Moreover, a misuse of caching can provoke bad behaviour of dynamic adaptive streaming over HTTP mechanisms and severely decrease the Quality of Experience. We designed a solution that in addition to solving the issue, alleviates the storage space of satellite.

International audience; We focus on a dense cellular network, in which a limited-size cache is available at every Base Station (BS). In order to optimize the overall performance of the system in such scenario, where a significant fraction of the users is covered by several BSs, a tight coordination among nearby caches is needed. To this end, this paper introduces a class of simple and fully distributed caching policies, which require neither direct communication among BSs, nor a priori knowledge of content popularity. Furthermore, we propose a novel approximate analytical methodology to assess the performance of interacting caches under such policies. Our approach builds upon the well known characteristic time approximation [1] and provides predictions that are surprisingly accurate (hardly distinguishable from the simulations) in most of the scenarios. Both synthetic and trace-driven results show that the our caching policies achieve excellent performance (in some cases provably optimal). They outperform state-of-the-art dynamic policies for interacting caches, and, in some cases, also the greedy content placement, which is known to be the best performing polynomial algorithm under static and perfectly-known content popularity profiles.

While augment reality applications are becoming popular, more and more data-hungry and computation-intensive tasks are delay-sensitive. Mobile edge computing is expected to an effective solution to meet the low latency demand. In contrast to previous work on mobile edge computing, which mainly focus on computation offloading, this paper introduces a new concept of task caching. Task caching refers to the caching of completed task application and their related data in edge cloud. Then, we investigate the problem of joint optimization of task caching and offloading on edge cloud with the computing and storage resource constraint. We formulate this problem as mixed integer programming which is hard to solve. To solve the problem, we propose efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm. Finally, the simulation experimental results show that our proposed TCO algorithm outperforms others in terms of less energy cost.

The spinal cord plays a key role for big data processing in the central nervous system, which is composed of the brain and spinal cord. A close look to the spinal cord reveals that the main functions of fog nodes such as communication, computation, and storage capability define what spinal cord does in the central nervous system. Based on this analogy, a new network architecture is described dubbed brain-spinal cord-nerve network that bears a striking resemblance to cloud-fog-thing network architecture under consideration for 5G networks. A stochastic geometry analysis is performed for this network to specify the optimum number of special neurons at the spinal cord responsible for learning. Additionally, to provide an alternative model for some fundamental motor skills in our daily life such as driving, swimming, dancing, and the brain-spinal cord-nerve network is modeled as a coded cache. These findings can be quite useful for neuroscientists who may want to apply the fog network model to the central nervous system with the ultimate aim of treating serious central nervous system diseases. Lastly, a novel coded caching structure is developed for fog networks inspired by the central nervous system.

In-network caching is a key feature of information-centric networking (ICN). However, challenges still exist in ICN caching such as how to place content replicas among cache nodes to maximize cache system benefits without introducing too much overhead. In this paper, we formulate the content placement problem and propose a distributed probabilistic caching strategy to enhance cache efficiency. Each node makes cache decision individually and caches passing content with certain probability, which is proportional to content popularity and content placement benefit. As a component of our scheme, we also propose an accurate method to predict the variations of content popularity. Besides, a global-popularity-based caching scheme is proposed to be used as a benchmark for performance evaluation. We conduct extensive simulations based on ndnSIM and evaluate our scheme on tree, intra-AS, and inter-AS topologies. Results indicate it outperforms the state-of-art schemes in terms of cache hit ratio, access latency, cache operation cost, and link bandwidth savings. It can achieve dramatic performance improvement, even in the case of a small cache size. In particular, the reduction of caching operation can reach up to two orders of magnitude. We also examine the impacts of various replacement policies on cache performance and perform overhead analysis. Finally, we give a simplified implementation of our scheme and validate it via simulations.

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.

Information centric network (ICN) is progressively becoming the revolutionary paradigm to the traditional Internet with improving data (content) distribution on the Internet along with global unique names. Some ICN-based architecture, such as named data network (NDN) and content centric network (CCN) has recently been developed to deal with prominent advantages to implement the basic idea of ICN. To improve the Internet services, its architecture design is shifting from host-centric (end-to-end) communication to receive-driven content retrieval. A prominent advantage of this novel architecture is that networks are equipped with transparent in-network caching to accelerate the content dissemination and improve the utilization of network resources. The gigantic increase of global network traffic poses new challenges to CCN caching technologies. It requires extensive flexibility for consumers to get information. One of the most imperative commonalities of CCN design is ubiquitous caching. It is broadly accepted that the in-network caching would improve the performance. ICN cache receives on several new characteristics: cache is ubiquitous, cache is transparent to application, and content to be cached is more significant. This paper presents a complete survey of state-of-art CCN-based probabilistic caching schemes aiming to address the caching issues, with certain focus on minimizing cache redundancy and improving the accessibility of cached content.

Caching popular contents is a promising way to offload the mobile data traffic in wireless networks, but so far the potential advantage of caching in improving physical layer security (PLS) is rarely considered. In this paper, we contribute to the design and theoretical understanding of exploiting the caching ability to improve the transmission security in a wireless heterogeneous network. Two transmission schemes, namely Symbol-level scheme and Bit-level scheme, are considered in such network to improve PLS by utilizing the content property of cached files. In Symbol-level scheme, the base station (BS) ensures the secrecy of communication by transmitting the requested file along with a pre-cached file of the user such that the eavesdropper’s channel is degraded. In Bit-level scheme, the BS encodes the requested file with a pre-cached file to fully resist the overhearing of eavesdroppers in bit level, e.g., network coding. Accordingly, the node locations of BSs, users and eavesdroppers are first modeled as mutually independent Poisson point processes and the corresponding file access protocol is developed. We consider two scenarios where each scheme is employed respectively, and then derive analytical expressions of two secrecy metrics in terms of average secrecy rate and secrecy coverage probability. Numerical results are provided to show the significant security advantages of the proposed schemes and to characterize the impacts of the cache ability and network resources on the security performance.

In this paper, we investigate the joint design of transmit beamforming over physical layer and file dissemination strategy at base stations (BSs) over transport layer in the cache-enabled cloud radio access networks. Our objective is to minimize the total user file download delay while satisfying each BS’s power constraint and file dissemination proportion constraints. This problem is very challenging because of the highly coupled optimization variables in the objective function. To address the untractable non-convex problem, the original problem is decomposed into two subproblems. The first subproblem is a non-convex fractional program problem, and by transforming it into a second-order cone program, an iterative algorithm based on an inner approximation method is proposed to achieve the optimal beamforming vectors. For the other subproblem, we propose a sparse optimization algorithm and prove that there exists a closed-form solution in terms of $l_{0}$ -norm. Then, the original non-convex problem is addressed in an iterative manner by employing the two proposed sub-algorithms. Numerical results demonstrate that the proposed cross-layer optimization algorithms can efficiently reduce the total user download delay.

Computer system and network performance can be significantly improved by caching frequently used information. When the cache size is limited, the cache replacement algorithm has an important impact on the effectiveness of caching. In this paper we introduce time-to-live (TTL) approximations to determine the cache hit probability of two classes of cache replacement algorithms: h-LRU and LRU(m). These approximations only require the requests to be generated according to a general Markovian arrival process (MAP). This includes phase-type renewal processes and the IRM model as special cases. We provide both numerical and theoretical support for the claim that the proposed TTL approximations are asymptotically exact. In particular, we show that the transient hit probability converges to the solution of a set of ODEs (under the IRM model), where the fixed point of the set of ODEs corresponds to the TTL approximation. We use this approximation and trace-based simulation to compare the performance of h-LRU and LRU(m). First, we show that they perform alike, while the latter requires less work when a hit/miss occurs. Second, we show that as opposed to LRU, h-LRU and LRU(m) are sensitive to the correlation between consecutive inter-request times. Last, we study cache partitioning. In all tested cases, the hit probability improved by partitioning the cache into different parts each being dedicated to a particular content provider. However, the gain is limited and the optimal partition sizes are very sensitive to the problem's parameters. (C) 2017 Elsevier B.V. All rights reserved.

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.

The ephemeral content popularity seen with many content delivery applications can make indiscriminate on-demand caching in edge networks highly inefficient, since many of the content items that are added to the cache will not be requested again from that network. In this paper, we address the problem of designing and evaluating more selective edge-network caching policies. The need for such policies is demonstrated through an analysis of a dataset recording YouTube video requests from users on an edge network over a 20-month period. We then develop a novel workload modelling approach for such applications and apply it to study the performance of alternative edge caching policies, including indiscriminate caching and cache on kth request for different k. The latter policies are found able to greatly reduce the fraction of the requested items that are inserted into the cache, at the cost of only modest increases in cache miss rate. Finally, we quantify and explore the potential room for improvement from use of other possible predictors of further requests. We find that although room for substantial improvement exists when comparing performance to that of a perfect "oracle" policy, such improvements are unlikely to be achievable in practice. Funding Agencies|Center for Industrial Information Technology (CEN-IIT); Natural Sciences and Engineering Research Council (NSERC) of Canada

The emergence of in-vehicle entertainment systems and self-driving vehicles, and the latter's need for high-resolution, up-to-date maps, will bring a further increase in the amount of data vehicles consume. Considering how difficult WiFi offloading in vehicular environments is, the bulk of this additional load will be served by cellular networks. Cellular networks, in turn, will resort to caching at the network edge in order to reduce the strain on their core network, an approach also known as mobile edge computing, or fog computing. In this work, we exploit a real-world, large-scale trace coming from the users of the We-Fi app in order to (i) understand how significant the contribution of vehicular users is to the global traffic demand; (ii) compare the performance of different caching architectures; and (iii) studying how such a performance is influenced by recommendation systems and content locality. We express the price of fog computing through a metric called price-of-fog, accounting for the extra caches to deploy compared to a traditional, centralized approach. We find that fog computing allows a very significant reduction of the load on the core network, and the price thereof is low in all cases and becomes negligible if content demand is location specific. We can therefore conclude that vehicular networks make an excellent case for the transition to mobile-edge caching: thanks to the peculiar features of vehicular demand, we can obtain all the benefits of fog computing, including a reduction of the load on the core network, reducing the disadvantages to a minimum., Comment: 16 pages, 10 figures. Expanded version for VehCOM journal, presented first at ACM MobiHoc 2016, Paderborn, DE, July 2016

Current TCP/IP based network is suffering from the tremendous usage of IP. Recently, content centric network (CCN) is proposed as an alternative of the future network architecture. In CCN, data itself, which is authenticated and secured, is a name and can be directly requested at the network level instead of using IP and DNS. Moreover, routers in CCN have caching abilities. Then end users can obtain the data from routers instead of remote server if the content has been stored in the routers, thus the overall network performance could be improved by reducing the transmission hops. Orthogonally, video plays a more and more important role nowadays and dominates the network traffic. Response time of each video request greatly affects the quality of user experience (QoE), users may even abandon the requested video service if they have to wait for long time before the video playback. Hence how to provide fast-start video delivery in CCN is critical. In this paper, we target to provide users fast-start video delivery in CCN. Specifically, we design a new caching policy for popularity-aware video caching in topology-aware CCN. And we propose to encode the video using scalable video coding (SVC) for fast-start video delivery and cache each video layer separately following the designed caching policies. Given an assigned weight by users, the tradeoff between the waiting time and received video quality is studied. Simulations are conducted to verify the performances and the results show that the proposed scheme outperforms state-of-the-art schemes significantly in typical scenarios.

This is the supplementary materials for the paper "Exploring Domain Name Based Features on the Effectiveness of DNS Caching", which will be appeared in ACM SIGCOMM Computer Communication Review (CCR). The data_training includes the feature vector extracted from each entry and the labeled classification attribute (Disposable/Reused) when training and testing the classifier. The classification: The evaluations of classifiers in the paper are built with the rpart and randomForest packages in R. Our experiments use the 2/3 percentage split for training and testing. Also, to handle the unbalanced data size, the prior probability has been set by the parms parameter in rpart and the classwt parameter in randomForest. The trace_log includes the anonymized trace log captured at a local DNS server of one campus network. We removed the actual domain names by simply assigning an integer when a new domain appears, which remains the capability of identifying whether one domain is re-appeared in the trace log. For every entry, the log also attaches the calculated value of each feature studied in the paper, which gives the possibility to run a trace-driven simulation applied with the proposed policies, i.e., the classification results (assuming infinite TTLs). The fifo.resolver and lru.resolver are the simple scripts to simulate the First-In-First-Out and the Least-Recently-Use replacement policies, respectively. The scripts directly use the off-line classification results to cache or discard DNS queries. They can be easily modified to simulate the scenario without the proactive elimination and to handle the TTLs (In revised experiments, we use the TTL values captured off-line, which is emitted from the datasets).

Information-centric networking (ICN) is a promising solution for most of Internet applications where the content represents the core of the application. However, the proposed solutions for the ICN architecture are associated with many complexities including pervasive caching in the Internet and incompatibility with legacy IP networks, so the deployment of ICN in real networks is still an open problem. In this paper, we propose a backward-compatible ICN architecture to address the caching issue in particular. The key idea is implementing edge caching in ICN, using a coalition of end clients and edge servers. Our solution can be deployed in IP networks with HTTP requests. We performed a trace-driven simulation for analyzing peer-assisted information-centric networking (PICN) benefits using IRCache and Berkeley trace files. The results show that on average, PICN decreases the latency for 78% and increases the content retrieval speed for 69% compared with a direct download from the original Web servers. When comparing PICN with a solution based on central proxy servers, we show that the hit ratio obtained using a small cache size in each PICN client is almost 14% higher than the hit ratio obtained with a central proxy server using an unlimited cache storage.