Cooperative Caching Framework Research Articles

Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

Proactive Content Caching Scheme in Urban Vehicular Networks

Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities and extreme complexity of business scenarios pose an enormous challenge to content caching and distribution in vehicular networks. To tackle this problem, this paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework. Firstly, we profoundly analyze the spatio-temporal correlation between the historical vehicle trajectory of user requests and construct the system model to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Meanwhile, we probe into privacy protection strategies to realize privacy-preserved prediction model. Furthermore, based on trajectory and popular content prediction results, content caching strategy is studied, and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Finally, simulations are provided to verify the superiority of our proposed scheme and algorithms. It shows that the proposed algorithms effectively improve the performance of the considered system in terms of hit ratio and average delay, and narrow the gap to the optimal caching scheme comparing with the traditional schemes.

Device-to-device streaming video caching for cellular download cost reduction

Abstract This paper proposes a Device-to-Device cooperative caching framework for streaming video consumption by mobile users. For downloading video with cooperative caching, a mobile device first searches within its neighboring Bluetooth-connected peers for whole or parts of the requested video before downloading them from a Content Provider’s server. A specific cooperative caching framework, namely, value-based caching is proposed in which the value of caching a hierarchically coded streaming video segment is defined for given pricing and video sharing models. Within this framework, we develop an Adaptive Quality (AQ) provisioning algorithm that minimizes the overall video content provisioning cost incurred due to the bandwidth usage of a cellular network. Detailed simulation experiments for functional validation and performance evaluation in comparison with other comparable caching strategies are then performed under different protocols and network parameters. It is shown that the proposed mechanism is able to appreciably reduce the overall video provisioning cost in the presence of end-user mobility.

Distributed Deep Learning at the Edge: A Novel Proactive and Cooperative Caching Framework for Mobile Edge Networks

We propose two novel proactive cooperative caching approaches using deep learning (DL) to predict users’ content demand in a mobile edge caching network. In the first approach, a content server (CS) takes responsibilities to collect information from all mobile edge nodes (MENs) in the network and then performs the proposed DL algorithm to predict the content demand for the whole network. However, such a centralized approach may disclose the private information because MENs have to share their local users’ data with the CS. Thus, in the second approach, we propose a novel distributed deep learning (DDL)-based framework. The DDL allows MENs in the network to collaborate and exchange information to reduce the error of content demand prediction without revealing the private information of mobile users. Through simulation results, we show that our proposed approaches can enhance the accuracy by reducing the root mean squared error (RMSE) up to 33.7% and reduce the service delay by 47.4% compared with other machine learning algorithms.

Swarm and Fuzzy Based Cooperative Caching Framework to Optimize Energy Consumption Over Multimedia Wireless Sensor Networks

Rapid technological advancement has spurred an impressive growth of industrial application for Multimedia Wireless Sensor networks. The entire sensor nodes are operated with the help of battery. An approach towards evolving low energy networking has been a great challenge, especially a battery which is worked storage and the nodes that are forwarded. More energy is consumed during data transmission and query processing. The possible solution is to minimize energy consumption by the use of cooperative caching method. In this research work, a Mobility with Clustering based Energy Efficient Cooperative Caching (MCE2C2) method is proposed. In this framework, the reservation node selection is performed using fuzzy interference system. The data transfer from the intermediate node and reservation node are performed by swarm intelligence based routing. When the sensor node accepts the data, the caching decision is evaluated by two parameters such as information present index and content drop time. The cache performance is improved by using the clustering mechanism and integrating the cache consistency and the cache replacement Push---Pull and Energy efficient Cache Replacement algorithms are used respectively. By using MATLAB and NS2 simulator, the performance of MCE2C2 is evaluated and the results are compared with existing approaches. As a result of this the sensor node energy is improved.

Content Distribution for 5G Systems Based on Distributed Cloud Service Network Architecture

Future mobile communications face enormous challenges as traditional voice services are replaced with increasing mobile multimedia and data services. To address the vast data traffic volume and the requirement of user Quality of Experience (QoE) in the next generation mobile networks, it is imperative to develop efficient content distribution technique, aiming at significantly reducing redundant data transmissions and improving content delivery performance. On the other hand, in recent years cloud computing as a promising new content-centric paradigm is exploited to fulfil the multimedia requirements by provisioning data and computing resources on demand. In this paper, we propose a cooperative caching framework which implements State based Content Distribution (SCD) algorithm for future mobile networks. In our proposed framework, cloud service providers deploy a plurality of cloudlets in the network forming a Distributed Cloud Service Network (DCSN), and pre-allocate content services in local cloudlets to avoid redundant content transmissions. We use content popularity and content state which is determined by content requests, editorial updates and new arrivals to formulate a content distribution optimization model. Data contents are deployed in local cloudlets according to the optimal solution to achieve the lowest average content delivery latency. We use simulation experiments to validate the effectiveness of our proposed framework. Numerical results show that the proposed framework can significantly improve content cache hit rate, reduce content delivery latency and outbound traffic volume in comparison with known existing caching strategies.

Cooperative caching versus proactive replication for location dependent request patterns

Today's trend to create and share personal content, such as digital photos and digital movies, results in an explosive growth of a user's personal content archive. Managing such an often distributed collection becomes a complex and time consuming task, indicating the need for a personal content management system that provides storage space transparently, is quality-aware, and is available at any time and at any place to end-users. A key feature of such a Personal Content Storage Service (PCSS) is the ability to search worldwide through the dataset of personal files. Due to the extremely large size of the dataset of personal content, a centralized solution is no longer feasible and an interesting approach for an efficient distributed PCSS implementation is to use a structured Peer-to-Peer network, and more in particular a Distributed Hash Table (DHT), providing a logarithmic lookup performance in the number of network nodes. In order to further increase the lookup performance, a caching layer is typically used between the application layer and the DHT. These caching strategies are location neutral, and usually do not exploit location dependence of request patterns. In this article we present our cooperative caching framework and introduce the cooperative Request Times Distance (RTD) caching algorithm. Since, lookup patterns in a PCSS typically have a power law popularity distribution and exhibit location dependent requests patterns, the proposed caching solution takes into account popularity and distance metrics. To enable cooperation between caches we introduce an update protocol, which only occasionally introduces one hop delay for a lookup operation. We present a systematic analysis of the caching framework and compare the cooperative caching algorithm to the state-of-the-art Beehive replication strategy. The cooperative RTD caching solution shows that when request patterns are more localized, the increase in lookup performance through cooperation is significantly better than Beehive.