Adaptive Real-Time Predictive Collaborative Content Discovery and Retrieval in Mobile Disconnection Prone Networks

Cognitive Caching at the Edges for Mobile Social Community Networks: A Multi-Agent Deep Reinforcement Learning Approach

“…CafRepCache serves subscribers with its local cache or by redirecting a request to a nearby collaborative cache, rather than forwarding to the original publisher. CafRepCache is built on multilayer predictive analytics and heuristics to capture and predict content interests coming from dynamic changing clusters [1,2,3,14,31,39] of subscribers in both random and scale-free network, and thus reduce content retrieval delay, improve cache efficiency and reduce resource consumption while enabling responsiveness to heterogeneous dynamically changing network topology, congestion avoidance and varying patterns of content publishers/subscribers. Authors in [30] present InfluentialCache, a proactive caching approach in small cellular device-to-device communication networks.…”

“…Note that we will use the term edge, agent and node interchangeably. At every epoch t, CognitiveCache edge adaptively and collaborative captures and predicts the dynamic changing network environment such as cache availability, complex users' content request patterns using predictive analytics and heuristics proposed in [1,2,3]. As a result, CognitiveCache edge can form a state of the network environment observed by not only itself but also its neighbour edges.…”

“…-Current large-scale networking systems have been evolving to adapt to the increasing complexity and dynamics of both underlying networking infrastructures and applications. Content caching in content delivery networks (CDNs) [63,64], such as AWS Cloudfront [25] and Azure CDN [61], allows improvement of users QoS and QoE but it still suffers from sparse network coverage, disconnections, network congestion and highly dynamic users' mobility and query patterns [1,2,6]. Many applications, such as remote health care and mobile social networks, need to be supported by next-generation mobile edge predictive content services which allow localized content storing and processing close to the users interested in it [1,6,7,32,33,34].…”

“…State-of-the-art edge services hosted in the mobile edge devices bring local data management, computation and inference capabilities to the edges to reduce the delay and improve the performance of data transport for end-users [67]. However, they still have limited support for surges of traffic, especially video streaming and dynamically changing networks due to users' mobility and content request patterns [1,2]. To enable fully local network, interest and privacy awareness self-organised multi-layer cognitive edge clouds have been proposed in [32,34] to host various services.…”

“…To enable fully local network, interest and privacy awareness self-organised multi-layer cognitive edge clouds have been proposed in [32,34] to host various services. Edge and fog computing [3,41,50] integration with various technologies including device-to-device (D2D) communication [30,43], contentcentric architecture [1,11], small cells [48], caching [1,2,3,8] are proposed to support complex networking data services. Intelligent caching services at the edges are envisaged to be important solution providing more localised and more responsive content services to mobile users compared to the traditional CDN approaches.…”