Content Replication Research Articles

Automated Over-the-Top Service Copyright Distribution Management System Using the Open Digital Rights Language

As the demand and diversity of digital content increase, consumers now have simple and easy access to digital content through Over-the-Top (OTT) services. However, the rights of copyright holders remain unsecured due to issues with illegal copying and distribution of digital content, along with unclear practices in copyright royalty settlements and distributions. In response, this paper proposes an automated OTT service copyright distribution management system using the Open Digital Rights Language (ODRL) to safeguard the rights of copyright holders in the OTT service field. The proposed system ensures that the rights to exercise copyright transactions and agreements, such as trading of copyright, can only be carried out when all copyright holders of a single digital content agree based on the Threshold Schnorr Digital Signature. This approach takes into account multiple joint copyright holders, thereby safeguarding their rights. Furthermore, it ensures fair and transparent distribution of copyright royalties based on the ratio information outlined in ODRL. From the user’s perspective, the system not only provides services proactively based on the rights information specified in ODRL, but also employs zero-knowledge proof technology to handle sensitive information in OTT service copyright distribution, thereby addressing existing privacy concerns. This approach not only considers joint copyright holders, but also demonstrates its effectiveness in resolving prevalent issues in current OTT services, such as illegal digital content replication and distribution, and the unfair settlement and distribution of copyright royalties. Applying this proposed system to the existing OTT services and digital content market is expected to lead to the revitalization of the digital content trading market and the establishment of an OTT service environment that guarantees both vitality and reliability.

Beyond 1 Million Nodes—Crowdsourced Video CDN: Architecture, Technology, and Economy

The video service has already become one of the Internet killer applications. To deliver the ever growing video traffic with the Quality of Experience (QoE) guarantee, deploying the infrastructure of Content Delivery Network (CDN) closer and closer to users has been a noticeable trend. And this trend becomes more promising when recently CDNs providers start utilizing the storage and network resources of the edge network devices (e.g., the Wi-Fi Access Points) for content delivery [13], i.e., crowdsourced CDN. In this article, we envision the crowdsourced CDN and propose a set of practical strategies to guide the implementation of such new paradigm. Firstly, we perform a large-scale measurement to explore the geo-content popularity and the geo-heterogeneity of the resource distribution. Benefiting from the measurement observations, we design the key solutions for the system operation, including regional content popularity prediction based on Markov Decision Process (MDP), region partition for the edge device organization and the collaborative content replication. Moreover, we consider the economic issues and propose a sequential auction mechanism to cope with the resource allocation when multiple Content Providers (CPs) compete for the edge resources for their content caching. We also demonstrate the performance gain of our design with data-driven simulation.

Reliable Provisioning With Degraded Service Using Multipath Routing From Multiple Data Centers in Optical Metro Networks

With the adoption of edge computing, several data centers are available within the footprint of an optical metro network, and contents are replicated in multiple locations. Such a wide content replication offers a unique opportunity to provide better services to users, especially for content-based services, e.g., video delivery. Thus, a service-provisioning scheme can embrace this opportunity to optimize network resource utilization, improve reliability, and achieve lower latency. In this study, we propose a reliable service-provisioning scheme that selects the optimal subset of data centers hosting the desired content and inversely multiplexes a content request over multiple link-disjoint paths. We formulate an integer linear program and develop heuristics for the problem, and use them to solve various complex and realistic network instances. Numerical data show that, compared to conventional service-provisioning schemes such as multipath routing from a single data center or dedicated-path protection, our proposed scheme efficiently utilizes network resources, improves reliability, and reduces latency; hence, it is suitable for the above-mentioned services.

Context-based caching in mobile information-centric networks

Wireless networking is expected to sustain the direct interaction between personal users’ devices, and to provide connectivity on large-scale resource-constrained devices. However, conventional networking protocols fail in large scale mobile wireless environments, due to node mobility, dynamic topologies, and intermittent connectivity. Information-Centric Networking (ICN) has been considered the most promising candidate to overcome the drawbacks of host-centric architectures where Named Data Networking (NDN) is one of the well-known and studied architectures within the ICN paradigm.The main objective of this work is to improve both content availability and network performance in mobile environments regarding the ICN paradigm. This is provided through a context-based approach for the caching admission policy providing in-network caching and content replication, facilitating the efficient and timely delivery of information. Content popularity, freshness, proximity, source mobility type and network density are some of the metrics considered in the caching decision. We conducted a comparative study between our proposal and the NDN caching strategy by using two different datasets with real mobility and connectivity traces, addressing intermittent communication. According to our results, we observed that using a multi-criteria context-based cache admission policy improves cache hits, cache evictions, and request satisfaction ratios in mobile environments, thus improving content delivery and network efficiency.

Delay-Based Network Utility Maximization Modelling for Congestion Control in Named Data Networking

Content replication and name-based routing lead to a natural multi-source and multipath transmission paradigm in NDN. Due to the unique connectionless characteristic of NDN, current end-to-end multipath congestion control schemes (e.g. MPTCP) cannot be used directly on NDN. This paper proposes a Network Utility Maximization (NUM) model to formulate multi-source and multipath transmission in NDN with in-network caches. From this model, a family of receiver-driven transmission solutions can be derived, named as path-specified congestion control. The path-specified congestion control enables content consumers to separate the traffic control on each path, which consequently facilitates fair and efficient bandwidth sharing amongst all consumers. As a specific instance, a Delay-based Path-specified Congestion Control Protocol (DPCCP) is presented, which utilizes queuing delays as signals to measure and control congestion levels of different bottlenecks. In addition, a set of high-performance congestion control laws are designed to accelerate bandwidth and fairness convergence towards the optimum defined by the NUM model. Finally, DPCCP is compared with state-of-the-art solutions. The experimental evaluations show that DPCCP outperforms existing solutions in terms of bandwidth utilization, convergence time and packet loss.

NCDN: A Node-Failure Resilient CDN Solution with Reinforcement Learning Optimization

Content Delivery Networks (CDNs) have enabled large-scale, reliable, and efficient content distribution over the Internet. Although CDNs have been very successful in serving a large portion of Internet traffic, they have several drawbacks. Despite their distributed nature, they rely on largely centralized management and replication. This can affect availability in case of node failure. Further, CDNs are complex infrastructures that span multiple layers of the networking stack. To address these issues, in this paper, we introduce NCDN, a novel highly distributed system for large-scale delivery of content and services. NCDN is designed to provide resilience against node failure through location-independent storage and replication of content. This is achieved through a two-layer architecture: the first layer (exposure layer) exposes services implemented by NCDN (e.g., Web, SFTP) to clients; the second layer (hidden layer) provides reliable distributed storage of content and application state. Content in NCDN’s hidden layer is stored and exchanged as Named Data Network (NDN) content packets. We employ the reinforcement learning (RL) to dynamically learn the optimal numbers of duplicates for different type of contents, because the RL agent has the advantage of not requiring expert labels or knowledge and instead the ability to learn directly from its own interaction with the world. The combination of NDN and RL brings NCDN fine-grained, fully decentralized content replication mechanisms. We compare the performance and resilience of NCDN to those of an idealized CDN via extensive simulations. Our results show that NCDN is able to provide higher availability than CDNs (between 8% and 100% higher under the same conditions), without substantially increasing content retrieval delay.

Optimal strategies for floating anchored information with partial infrastructure support

Floating Content (FC) is a communication paradigm to locally share ephemeral content without direct support from infrastructure. It is based on constraining the opportunistic replication of content in a way that strikes a balance between minimizing resource usage and maximizing content availability among the intended recipients. However, existing approaches to management of FC schemes are unfit for realistic scenarios with non-uniform user distributions, resulting in heavy overdimensioning of resources allocated to FC. In this work, we propose a new version of FC, called Cellular Floating Content (CFC), which optimizes the use of bandwidth and memory by adapting the content replication and storage strategies to the spatial distribution of users, and to their mobility patterns. The main idea underlying our approach is to partition users into small “local communities”, and to optimally weight their contributions to the FC paradigm according to their specific mobility features, and to the resources required to achieve a target performance level. We characterize numerically the properties of the optimal strategies in a variety of mobility patterns and traffic conditions, showing the accuracy of our approach, and the significant savings it enables in the amount of resources necessary to run FC, which in a realistic setup can be as high as 27% with respect to traditional FC dimensioning strategies.

Advanced event‐base exhibition management mobile applications social networks using fuzzy reasoning‐based routing cum forwarding algorithm

Abstract5G is the fifth‐generation mobile innovation that not only just expands the downloading and transferring speeds over the mobile networks, however, it additionally decreases the latency, which is the time taken by a system to respond, builds energy proficiency, and offers increasingly stable system associations. It has been seen that the present Internet structure is not appropriate for current and developing traffic requests. Many content distributions advances such as content Delivery Networks and shared frameworks have developed to allow content access by name as instead of server area or address. In addition, to respond to the expanding volumes of traffic for such applications as video on demand and distributed cloud computing, numerous works have been attempted to empower reserving, content replication, and preparing inside the system. Such advancements are exceptionally dependent on the distribution channel and execute as an overlay on the current Internet structure, which results in various inefficient result aspects. In recent years, it is highly difficult to develop a routing cum forwarding algorithm in order to overcome the existing problems, the introduced fuzzy reasoning‐based routing cum forwarding algorithm. In this fuzzy method, the mobile devices evaluate the transmission priority and decide the transmission decision too. On comparing to the existing routing algorithms, the fuzzy concept should have reduced energy consumption, increased time delay, and high transmission efficiency and the above parametric results are verified by the fact of the fuzzy algorithm.

DISPERSE: A Decentralized Architecture for Content Replication Resilient to Node Failures

This paper introduces DISPERSE, a distributed scalable architecture for delivery of content and services that provides resilience against node failure through location-independent storage and replication of content. Current content delivery networks (CDNs) have, at least to some degree, a centralized structure thus susceptible to a single point of failure. DISPERSE addresses this limitation by implementing a fully de-centralized structure. DISPERSE is a two-layer architecture: the first layer (front-end layer) exposes services (e.g., Web, SFTP) to clients; the second layer (back-end layer) provides reliable distributed storage of content and application state. Content in DISPERSE's back-end layer is stored and exchanged as Named Data Network (NDN) content objects. This allows DISPERSE to implement fine-grained, location-independent, fully decentralized content replication mechanisms. We validate the performance of DISPERSE under two node failure scenarios. In the first scenario, content can be stored in any DISPERSE node, and all nodes are equally likely to fail. In this scenario, we use non-linear optimization techniques to determine the optimal number of content copies under availability and latency constraints. In the second scenario, different nodes fail with different probabilities, and content is stored in nodes according to its value, node failure probability, and resource availability. This scenario is addressed as an instance of the minimum cost flow problem. Our results show that DISPERSE reduces the failure of content retrieval by five orders of magnitude compared to common CDN implementations, without significantly increasing content retrieval delay. Further, numerical results show that DISPERSE improves content availability by a factor of 1.3× - 2.3× when deploying the minimum cost flow algorithm.

Handover-Aware Content Replication for Mobile-CDN

This letter proposes a handover-aware content replication mechanism for mobile content delivery networks. This mechanism allows us to select the most optimal surrogate servers and assign these to the mobile clients through built-in tables. The optimal surrogate selection process begins by predicting the mobility of clients through Gauss-Markov model. Then, it continues with server modeling based on G/G/1 queuing system for load estimation. We also use the k -order autoregressive model to observe the future values of the estimated loads. The proposed strategy reduces the content replication delays and packet losses by 53% and 42%, respectively.

Effect of Recommendations on Serving Content with Unknown Demand

We consider the task of content replication in distributed content delivery systems used by Video-on-Demand (VoD) services with large content catalogs. The prior work in this area focuses on the setting where each request is generated independent of all past requests. Motivated by the fact that most popular VoD services offer recommendations to users based on their viewing history, in a departure from existing studies, we study the setting with time-correlation in requests coming from each user. We use a Markovian process to model each user’s request process. In addition to introducing time-correlation in user requests, our model is consistent with empirically observed properties of the request process for VoD services with recommendation engines. In the setting where the underlying Markov Chain is unknown and has to be learned from the very requests the system is trying to serve, we show that separating the task of estimating content popularity and using the estimates to design a static content replication policy is strictly sub-optimal. To prove this, we show that an adaptive policy, which jointly performs the task of estimation and content replication, outperforms all policies that separate the task of estimation and content replication.

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

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

Replicating Coded Content in Crowdsourcing-Based CDN Systems

Recently, crowdsourcing-based content delivery networks (CDN) emerge as a promising technology that can distribute massive video content to a vast number of Internet users by crawling bandwidth and storage resources from Internet end devices. Any ordinary Internet users with excessive resources can be recruited into such systems as mini-servers. Different from edge servers equipped with dedicated resources in traditional CDNs, the resource of a single mini-server is scarce and volatile that can vary severely with time, since its bandwidth is shared by many different applications. How to build a robust high performance crowdsourcing-based CDN system has attracted contributions from both academia and industry, but how to solve the drawback caused by unstable uploading bandwidth is still a challenging problem. So far, a prevalent methodology is to migrate the strategies implemented by traditional CDNs into crowdsourcing-based CDN systems based on the fact that these two kinds of systems share many similarities. In this paper, our argument is that the content delivery time can be reduced by replicating coded content on mini-servers (which is almost useless for edge servers in traditional CDNs) to enable downloading users to automatically adapt their downloading progress with oscillating bandwidth capacity from different mini-servers. Theoretical model is created to derive the performance improvement (evaluated in term of average file downloading time) achieved by our strategy, which is further validated via simulation. This paper not only provides system designers a more efficient content replication solution, but also can push forward the development of the crowdsourcing-based CDNs.

Infrastructure upgrade framework for Content Delivery Networks robust to targeted attacks

Content Delivery Networks (CDNs) are crucial for enabling delivery of services that require high capacity and low latency, primarily through geographically-diverse content replication. Optical networks are the only available future-proof technology that meets the reach and capacity requirements of CDNs. However, the underlying physical network infrastructure is vulnerable to various security threats, and the increasing importance of CDNs in supporting vital services intensifies the concerns related to their robustness. Malicious attackers can target critical network elements, thus severely degrading network connectivity and causing large-scale service disruptions. One way in which network operators and cloud computing providers can increase the robustness against malicious attacks is by changing the topological properties of the network through infrastructure upgrades.This work proposes a framework for CDN infrastructure upgrade that performs sparse link and replica addition with the objective of maximizing the content accessibility under targeted link cut attacks. The framework is based on a newly defined content accessibility metric denoted as μ-ACA which allows the network operator to gauge the CDN robustness over a range of attacks with varying intensity. Two heuristics, namely Content-Accessibility-Aware Link Addition Heuristic (CAA-LAH), and Content-Accessibility-Aware Replica Addition Heuristic (CAA-RAH) are developed to perform strategic link and replica placement, respectively, and hamper attackers from disconnecting users from the content even in severe attack scenarios. Extensive experiments on real-world reference network topologies show that the proposed framework effectively increases the CDN robustness by adding a few links or replicas to the network.

Optimal Information Centric Caching in 5G Device-to-Device Communications

Device-to-Device (D2D) communications are a prominent feature of 5G systems, introduced to provide a native support to distributed services in mobile environments. D2D technologies enable straight interactions between mobile terminals without a compulsory involvement of base stations. In this manuscript, we study and propose an optimized caching strategy to content distribution on top of D2D technology, based on Information Centric Networking (ICN) principles. The rationale is that ICN architectures can provide seamless support to mobile services and decouple contents from node identifiers, thus providing a promising match with D2D requirements. To this end, a novel fluid-based model in proposed hereby that catches the interplay between ICN functionalities, D2D requirements, and 5G specifications. Then, based on this model, an optimal content replication problem is formulated, encompassing caching overhead and system load. Additionally, this problem is thoroughly analyzed to prove that it has an optimal solution with time threshold form. A practical algorithm $\varsigma ^*$ -OCP is further proposed in order to implement the optimal caching control in realistic environments. Finally, a massive simulation campaign is carried out to test the proposed algorithm in comparison to state-of-the-art solutions.

Efficient replication for vehicular content distribution

We consider a vehicular content replication system which makes use of the deployed Access Points (APs) to maximize the vehicular download progress of delay-tolerant contents through replication in the APs' local storage. The replication system for vehicular users is quite different from the traditional system for static Web users. The transient connection period between the vehicle and the AP makes it difficult for the vehicle to download the entire file requested and thus the content retrieval is usually across several APs. Such characteristic poses two problems: (1) replication in units of entire file may be inefficient in terms of resource utilization; (2) the actual contribution of an individual AP can be affected by the other correlated APs during the content retrieval. To deal with these challenges, we formulate the vehicular content retrieval into an offline optimization model that helps establish the performance bounds of replication algorithms in maximizing vehicular download progress. A real vehicular trace is also thoroughly analyzed. Then we propose an efficient and distributed replication algorithm explicitly taking into account the content popularity, vehicle-AP contact pattern and content availability among correlated APs. Simulation based on real vehicular trace proves the effectiveness of the proposed replication system. The performance in terms of download rate and completion ratio has at least 15% to 20% improvement against the algorithms under comparison.

Resource Pooling in Large-Scale Content Delivery Systems

Content delivery networks are a key infrastructure component used by Video on Demand (VoD) services to deliver content over the Internet. We study a content delivery system consisting of a central server and multiple co-located caches, each with limited storage and service capabilities. This work evaluates the performance of such a system as a function of the storage capacity of the caches, the content replication strategy, and the service policy. This analysis can be used for a system-level optimization of these design choices. The focus of this work is on understanding the benefits of allowing caches to pool their resources to serve user requests. We show that the benefits of resource pooling depend on the popularity profile of the contents offered by the VoD service. More specifically, if the popularity does not vary drastically across contents, then resource pooling leads to an order wise reduction in central server transmission rate as the system size grows. On the other hand, if the content popularity is skewed, the central server transmission rate is of the same order with and without resource pooling.

ANALYSIS OF CONTENT REPLICATION MECHANISM IN RANDOM SEARCH IN PEER-TO-PEER NETWORKS

Background. Nowadays, file-sharing peer-to-peer (P2P) networks are becoming increasingly popular when providing video over the Internet. Successful reception of a given service is determined by how quickly and qualitatively the necessary content stored in the memory of the peers of this network is found. In structured systems a limited number of transitions are used to find the ordered file, however, for unstructured systems, where random search is used, the delay can be significant, especially when searching for rare files. At the same time, it is unstructured systems that can support complex requests with metainformation, which provides their priority development. There arises a question of how quickly the given file with a certain number of active peers can be obtained. Objective. The aim of the paper is the analysis of the state of downloaded content on the nodes of unstructured P2P networks. Methods. Synthesis of the mathematical model of video content replication taking into account the network load and performance analysis based on this model has been performed for the 3 most used algorithms: unified, proportional and “square root”. Numerical quantitative studies have been performed by means of calculation. Results. A mathematical model that allows estimating the average number of attempts to replicate the ordered file taking into account the size of the network memory has been developed. The examples of the dependence of the average file search size for different numbers of active peers have been obtained. Conclusions. The mathematical model of video content replication necessary for the successful search of the requested file is obtained taking into account the network memory size determined by the number of active peers. The obtained dependence of the average number of request attempts when searching for the desired files for the three replication algorithms allows concluding that the “square root” algorithm is expedient. Keywords: peer-to-peer networks; mathematical models; files; performance; algorithm.

Manycast, Anycast, and Replica Placement in Optical Inter-Datacenter Networks

The expanding adoption of cloud-based services in recent years puts stringent requirements on datacenters (DCs) and their interconnection networks. Optical inter-datacenter networks represent the only viable option for satisfying the huge bandwidth required to replicate and update content for cloud-based services across geographically dispersed datacenters. In addition to content replication and synchronization, optical inter-datacenter networks must also support communication between datacenters and end-users. The resulting new traffic patterns and the enormous traffic volumes call for new capacityefficient approaches for inter-datacenter network designs that incorporate both transport and datacenter resource planning. This paper introduces an integrated approach to optimally place content replicas across DCs by concurrently solving the routing and wavelength assignment (RWA) problem for both inter-DC content replication and synchronization traffic following the manycast routing paradigm, and end-user-driven user-to-DC communication following the anycast routing paradigm, with the objective to reduce the overall network capacity usage. To attain this goal, the manycast, anycast, and replica placement (MARP) problem is formulated as an integer linear program to find optimal solutions for smaller problem instances. Due to the problem complexity, a scalable and efficient heuristic algorithm is developed to solve larger network scenarios. Simulation results demonstrate that the proposed integrated MARP strategy can significantly reduce the network capacity usage when compared to the benchmarking replica placement and RWA schemes aimed at minimizing the resources consumed by either of the two types of traffic independently.

Resource Auto-Scaling and Sparse Content Replication for Video Storage Systems

Many video-on-demand (VoD) providers are relying on public cloud providers for video storage, access, and streaming services. In this article, we investigate how a VoD provider may make optimal bandwidth reservations from a cloud service provider to guarantee the streaming performance while paying for the bandwidth, storage, and transfer costs. We propose a predictive resource auto-scaling system that dynamically books the minimum amount of bandwidth resources from multiple servers in a cloud storage system to allow the VoD provider to match its short-term demand projections. We exploit the anti-correlation between the demands of different videos for statistical multiplexing to hedge the risk of under-provisioning. The optimal load direction from video channels to cloud servers without replication constraints is derived with provable performance. We further study the joint load direction and sparse content placement problem that aims to reduce bandwidth reservation cost under sparse content replication requirements. We propose several algorithms, and especially an iterative L 1 -norm penalized optimization procedure, to efficiently solve the problem while effectively limiting the video migration overhead. The proposed system is backed up by a demand predictor that forecasts the expectation, volatility, and correlation of the streaming traffic associated with different videos based on statistical learning. Extensive simulations are conducted to evaluate our proposed algorithms, driven by the real-world workload traces collected from a commercial VoD system.