Dynamic cache allocation and pricing in Information-Centric Networks (ICNs) is a challenging problem, especially when multiple content producers and competing ICN cache service providers are involved. Many existing ICN caching schemes generalize their frameworks as producer-agnostic architectures while considering a single ICN cache service provider. Realistically, as ICNs grow, multiple cache providers will compete for valuable content that would generate higher cache hits, and the ecosystem will inevitably become market-driven. In this paper, we investigate the dynamic cache allocation and price determination problem considering a caching system consisting of multiple content producers who act as the buyers and multiple competing ICN cache providers who act as the sellers of the caching resources. We propose a novel reverse auction-based caching and pricing scheme named SEMRA that aims to maximize the caching benefits of content producers. Simulation results demonstrate how the proposed scheme improved ICN caching over several caching metrics across varying cache sizes and popularity skewness values. Future work in this domain is highlighted in the conclusion.

In Information-Centric Networks (ICNs), caching decisions are mostly driven by request-centric mechanisms. Fluctuations in request rates and cache capacities typically have the most impact on where content would be cached. However, as ICNs expand in scale, producers may prefer certain nodes to cache their contents based on favorable properties, such as topological centrality, closeness of cache locations with respect to consumers, security measures, and service up-time. These factors would impact the valuation of caching nodes. Nevertheless, maximizing cache utilization via dynamic cache valuation is a challenging task in ICNs, largely due to inter-dependencies in model parameters. In this paper, we propose a novel caching model where content producers aim to dynamically valuate cache nodes to optimize caching. The model is built on a value-based utility function that considers dynamic and topological attributes of cache nodes, enabling a dynamic novel caching scheme named Max-Node Utility that aims to maximize caching utility. Simulation results demonstrate that Max-Node Utility outperforms current state-of-the art caching schemes by providing better caching utility, reducing access delay and increasing cache hit ratios across varying cache sizes and popularity skewness values. An outlook on the premise of producer-driven caching schemes is presented in the conclusion, to emphasize future directions in similar caching models.

Recent studies into streaming media delivery suggest that performance gains from ubiquitous caching in Information-Centric Networks (ICN) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieving multimedia content. Bitrate adaptation mechanisms, that drive video streaming, clash with caching mechanisms in ways that affect users' Quality of Experience (QoE). Cache performance also diminishes as consumers dynamically select content encoded at different bitrates. In this article we use this evidence to draw a novel insight: in adaptive streaming over ICN, bitrates should be prioritized alongside popularity and hit rates. We build on this insight to propose RippleCache as a family of cache placement schemes that safeguard high-bitrate content at the edge and push low-bitrate content into the network core. Doing so reduces contention of cache resources, as well as congestion in the network. To validate RippleCache claims we construct two separate implementations. We design RippleClassic as a benchmark solution that optimizes content placement by maximizing a measure for ICNs shown to have high correlation with QoE. In addition, our lighter-weight RippleFinder is then re-designed with distributed execution for application in large-scale systems. RippleCache performance gains are reinforced by evaluations in NS-3 against state-of-the-art baseline approaches, using standard measures of QoE as defined by the DASH Industry Forum. Our results demonstrate that RippleClassic and RippleFinder deliver content that suffers less oscillation and rebuffering, all while achieving the highest levels of video quality; thus indicating overall improvements to QoE.

Information-Centric Networks (ICN) are aiming to shift the current host-oriented Internet model towards a content-centric one, by focusing on highly scalable and efficient content distribution and retrieval. Content caching is a fundamental building block in ICN, optimized to enable fast, reliable, and scalable content distribution and delivery. In this paper, we propose a novel utility value-based caching scheme, named Max- Utility for maximizing the aggregated utility- value of an ICN cache service provider. This novel approach considers attributes of both the content, and its producer, to determine the aggregated value, and builds on a dynamic caching algorithm that aims to maximize the aggregated utility value to guide cache placement and replacement decisions. Simulation results demonstrate that, Max-Utility outperforms current state-of-the art caching schemes by providing better caching utility while significantly eliminating caching redundancy and incurring less access delay to retrieve good quality contents across varying cache sizes and popularity skewness values.

Video traffic is growing in dominance in today's Internet, prompting new challenges in timely delivery of video content. As Dynamic Adaptive Streaming (DAS) is becoming the de facto paradigm for video delivery, there is growing evidence on how caching improves users' Quality of Experience (QoE) in DAS. However, there is no consensus on how to maximize the utilization of in-network caching resources. Specifically, there are conflicting proposals on the impact of caching based on its distance (in hops) from the network edge. That is, contrasting ubiquitous network-wide caching to edge-caching. Supporters of the ubiquitous caching paradigm propose bitrate-aware caching schemes for optimizing video streaming, while counter-proposals suggest that edge-caching only the highest bitrate with online transcoding, may offer superior performance to ubiquitous caching. In this paper, we answer a contentious question: Can transcoding at the edge outperform bitrate-aware ubiquitous caching for DAS? We devise an extensive simulation environment using NS-3 to contrast both paradigms, experimenting with different bandwidth fluctuation patterns, under the FESTIVE user-based bitrate adaptation protocol. Caching performance was evaluated under five established QoE metrics, gauging delivered video quality, playback freezing and bitrate oscillation. We further assume zero processing delay for online transcoding at the network edge, to contrast to an upper bound performance from the edge-caching paradigm. Our experiments demonstrate that neither transcoding nor bitrate-aware caching offer a silver bullet for all cases. We present our insights on networking scenarios where each model would dominate in performance, and present our concluding remarks on their development.

Recent studies suggest that performance gains for content delivery over Information-centric Networks (ICNs) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieval of multimedia content. The bitrate adaptation mechanism that drives video streaming appears to clash with generic ICN caching techniques in ways that affect users' Quality of Experience (QoE). Cache performance diminishes as video consumers dynamically select content encoded at different bitrates. Motivated by preliminary evidence suggesting the merits of bitrate-based cache partitioning, we introduce a scheme to dissect the cache capacity of routers along a forwarding path according to dedicated bitrates. To facilitate this partitioning, we propose a guiding principle RippleCache, which stabilizes bandwidth fluctuation while achieving high cache utilization by safeguarding high-bitrate content on the edge and pushing low-bitrate content into the network core. We further propose a cache placement scheme, RippleFinder, to realize this RippleCache principle and highlight its impact on users' QoE by cache partitioning. The performance gains are reinforced by evaluations in NS-3. Measurements show RippleFinder can significantly reduce bitrate oscillation, while ensuring high video quality, indicating overall improvement to QoE.

The growing demand for video streaming is straining the Internet, and mandating a fundamental change in future networking paradigms. Current advancements in Information-centric Networks (ICN) promise a novel approach to intrinsically handling content dissemination, caching and retrieval. While streaming technologies are converging towards Dynamic Adaptive Streaming (DAS), in-network caching in ICN facilitates serving users with better video qualities, potentially beyond their actual bandwidth-mandated throughput. In this paper, we propose an assessment framework for adaptive video streaming to evaluate the performance of ICN caching schemes, and measure their impact on improving users' streaming experience. We present a thorough study of core performance metrics and adopt those metrics which are designed for video caching. We conduct experiments on a NS-3 based simulator, ndnSIM, and propose insights which will aid the development of future caching schemes that cater to inevitable bitrate variations.

The growing demand for video content is reshaping our view of the current Internet, and mandating a fundamental change for future Internet paradigms. A current focus on Information-Centric Networks (ICN) promises a novel approach to intrinsically handling large content dissemination, caching and retrieval. While ubiquitous in-network caching in ICNs can expedite video delivery, a pressing challenge lies in provisioning scalable video streaming over adaptive requests for different bit rates. In this paper, we propose novel video caching schemes in ICN, to address variable bit rates and content sizes for best cache utilization. Our objective is to maximize overall throughput to improve the Quality of Service (QoS). In order to achieve this goal, we model the dynamic characteristics of rate adaptation, deriving caps on average delay, and propose DaCPlace which optimizes cache placement decisions. Building on DaCPlace, we further present a heuristic scheme, StreamCache, for low-overhead adaptive video caching. We conduct comprehensive simulations on NS-3 (specifically under the ndnSIM module). Results demonstrate how DaCPlace enables users to achieve the least delay per bit and StreamCache outperforms existing schemes, achieving near-optimal performance to DaCPlace.

The growing demand for video streaming is straining the current Internet, and mandating a novel approach to future Internet paradigms. The advent of Information-Centric Networks (ICN) promises a novel architecture for addressing this exponential growth in data traffic, with ubiquitous caching to facilitate video delivery. In this paper, we present a novel in-network video caching policy in ICN, named StreamCache, catering to variable video contents with different sizes and bit rates. Our objective is improving the average throughput of users which consequently enhances the Quality of Experience (QoE), under the heterogeneity of users' devices and network conditions. StreamCache is a popularity-based policy, which operates distributively at routers, designed for the online processing in order to narrow the gap between the offline theoretical optimal solution and the real-world application. StreamCache operates in rounds, making caching decisions based on video request statistics and minimal cache coordination. We show that, StreamCache achieves near-optimal performance compared with the offline benchmark scheme, DASCache and outperforms current state-of-the-art protocols, such as ProbCache by presenting an elaborate evaluation carried out on ndnSIM, over NS-3.

The growing demand for video streaming is straining the current Internet, and mandating a novel approach to future Internet paradigms. The advent of Information-Centric Networks (ICN) promises a novel architecture for addressing this exponential growth in data-intensive services, of which video streaming is projected to dominate (in traffic size). In this paper, we present a novel strategy in ICNs for adaptive caching of variable video contents tailored to different sizes and bit rates. Our objective is to achieve optimal video caching to reduce access time for the maximal requested bit rate for every user. At its core, our approach capitalizes on a rigorous delay analysis and potentiates maximal serviceability for each user. We incorporate predictors for requested video objects based on a popularity index (Zipf distribution). In our proposed model, named DASCache, we present delay queuing analysis for cached objects, providing a cap on expected delay in accessing video content. In DASCache, we present a Binary Integer Programming (BIP) formulation for the cache assignment problem, which operates in rounds based on changes in content requests and popularity scores. DASCache reacts to changes in network dynamics that impact bit rate choices by heterogeneous users and enables users to stream videos, maximizing Quality of Experience (QoE). To evaluate the performance of DASCache, in contrast to current benchmarks in video caching, we present an elaborate performance evaluation carried out on ndnSIM, over NS-3.