Distributed Edge Caching Research Articles

Distributed Edge Caching for Zero Trust-Enabled Connected and Automated Vehicles: A Multi-Agent Reinforcement Learning Approach

Distributed Edge Caching for Zero Trust-Enabled Connected and Automated Vehicles: A Multi-Agent Reinforcement Learning Approach

Labeled graph partitioning scheme for distributed edge caching

Distributed edge caching could address latency and congestion problems in large-scale data access effectively, improving system throughput and performance. However, the lack of specialized edge caching solutions for graph data resulted in low cache hit rates and high data access latency. This limitation stemmed from existing graph partitioning schemes that overlooked the specific requirements of edge caching, such as communication overhead between edge servers, data query frequency, and query patterns. To overcome these challenges, we proposed LGPE, a labeled graph partitioning scheme for distributed edge caching based on frequent query patterns. LGPE generated frequent query patterns according to the user’s historical query subgraphs and then performed labeled graph partitioning based on the frequent pattern, ensuring that the labeled graph divided into edge servers had a high hit rate for frequent user queries while satisfying a minimum edge cut. Evaluation on real datasets from various application domains demonstrated that LGPE achieved approximately 40% higher cache hit rates compared to benchmarks like METIS and SCOTCH, and it also performed well in terms of edge cuts in special graph cases.

Privacy-Preserving Federated Deep Learning for Cooperative Hierarchical Caching in Fog Computing

Over the past few years, fog radio access networks (F-RANs) have become a promising paradigm to support the tremendously increasing demands of multimedia services, by pushing computation and storage functionalities toward the edge of networks, closer to users. In F-RANs, distributed edge caching among fog access points (F-APs) can effectively reduce network traffic and service latency as it places popular contents at local caches of F-APs rather than the remote cloud. Due to the limited caching resources of F-APs and spatiotemporally fluctuant content demands from users, many cooperative caching schemes were designed to decide which contents are popular and how to cache them. However, these approaches often collect and analyze the data from Internet-of-Things (IoT) devices at a central server to predict the content popularity for caching, which raises serious privacy issues. To tackle this challenge, we propose a federated learning-based cooperative hierarchical caching scheme (FLCH), which keeps data locally and employs IoT devices to train a shared learning model for content popularity prediction. FLCH exploits horizontal cooperation between neighbor F-APs and vertical cooperation between the baseband unit (BBU) pool and F-APs to cache contents with different degrees of popularity. Moreover, FLCH integrates a differential privacy mechanism to achieve a strict privacy guarantee. Experimental results demonstrate that FLCH outperforms five important baseline schemes in terms of the cache hit ratio, while preserving data privacy. Moreover, the results show the effectiveness of the proposed cooperative hierarchical caching mechanism for FLCH.

Single vs Distributed Edge Caching for Dynamic Content

Existing content caching mechanisms are predominantly geared towards easy-access to content that is static once created. However, numerous applications, such as news and dynamic sources with time-varying states, generate ‘dynamic’ content where new updates replace previous versions. This motivates us in this work to study the freshness-driven caching algorithm for dynamic content, which accounts for the changing nature of data content. In particular, we provide new models and analyses of the average operational cost both for the single and distributed edge caching scenarios. In both scenarios, we characterize the performance of the optimal solution and develop algorithms to select the content and the update rate that the user(s) must employ to have low-cost access to fresh content. Moreover, our work reveals new and easy-to-calculate key metrics for quantifying the caching value of dynamic content in terms of their refresh rates, popularity, number of users in the distribute edge caching group, and the fetching and update costs associated with the optimal decisions. We compare the proposed freshness-driven caching strategies with benchmark caching strategies like cache the most popular content. Results demonstrate that freshness-driven caching strategies considerably enhance the utilization of the edge caches with possibly orders-of-magnitude cost reduction. Furthermore, our investigations reveal that the distributed edge caching scenario, benefiting from the multicasting property of wireless service to update the cached content, can be cost-effective compared to the single edge caching, as the number of edge caches increases.

AutoSight: Distributed Edge Caching in Short Video Network

In recent years there has been a rapid increase of short video traffic in CDN. While the video contributors change from large video studios to distributed ordinary end users, edge computing naturally matches the cache requirements from short video network. But distributed edge caching exposes some unique characteristics: non-stationary user access pattern and temporal and spatial video popularity pattern, which severely challenge the edge caching performance. While the QoE in traditional CDN has been much improved, prior solutions become invalid in solving the above challenges. In this article, we present AutoSight, a distributed edge caching system for short video network, which significantly boosts cache performance. AutoSight consists of two main components, solving the above two challenges respectively: the CoStore predictor, which solves the non-stationary and unpredictability of local access pattern, by analyzing the complex video correlations; and a caching engine Viewfinder, which solves the temporal and spatial video popularity problem by automatically adjusting future horizon according to video life span. All these inspirations and experiments are based on the real traces of more than 28 million videos with 100 million accesses from 488 servers located in 33 cities. Experiment results show that AutoSight brings significant boosts on distributed edge caching in short video network.

A Mean Field Game-Based Distributed Edge Caching in Fog Radio Access Networks

In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the optimization problem as a stochastic differential game (SDG) which captures the dynamics of F-AP states. To address both the intractability problem of the SDG and the caching capacity constraint, we propose to solve the optimization problem in a distributive manner. Firstly, a mean field game (MFG) is converted from the original SDG by exploiting the ultra-dense property of F-RANs, and the states of all F-APs are characterized by a mean field distribution. Then, an iterative algorithm is developed that enables each F-AP to obtain the mean field equilibrium and caching control without extra information exchange with other F-APs. Secondly, a fractional knapsack problem is formulated based on the mean field equilibrium, and a greedy algorithm is developed that enables each F-AP to obtain the final caching policy subject to the caching capacity constraint. Simulation results show that the proposed scheme outperforms the baselines.

Design of Binary Erasure Code With Triple Simultaneous Objectives for Distributed Edge Caching in Industrial Internet of Things Networks

For one moving Internet of Things (IoT) collector to download files from nearby industrial IoT devices, storing network coded files into multiple IoT devices can achieve good reliability performance if the following $(n, k)$ erasure property (EP) is fulfilled: $k$ source packets are encoded into $n$ packets, and $k$ packets chosen from these $n$ packets in an arbitrary manner can reconstruct all the source packets. Besides, low decoding complexity is desired for time-sensitive applications and energy-limited moving IoT collectors, and binary zigzag decoding (BZD) achieves significantly low decoding complexity. The objective of previous EP-BZD designs is unilateral, which limits its application scenarios. In this article, a novel EP-BZD code is designed, which achieves good tradeoff among largest storage room overhead (SRO), SRO variance, and wide range of $(n, k)$ . To implement such a code, the source packets are shifted by several bits, respectively, and then Xored together. The numbers of bits shifted are represented by a matrix, which is obtained from a specially constructed triangle by taking a certain maximal submatrix. The triangle is obtained by a series of steps, including the construction of base vector, parallelogram, trapezoid, etc. The proof that the proposed code possesses EP and BZD simultaneously is also provided. A series of comparisons verify that the proposed code achieves significantly better tradeoff among triple objectives than existing EP-BZD codes.

Softwarization and Network Coding in the Mobile Edge Cloud for the Tactile Internet

Future communication systems, such as those enabling the Tactile Internet, will face disruptive changes compared to the state-of-the-art systems, which are 1) highly dynamic topology changes; 2) replacement of the end-to-end paradigm by real mesh topologies; and 3) a massive number of devices. To overcome these disruptive changes, future communication systems will substitute specialized hardware with generic hardware boxes and the softwarization paradigm. Furthermore, this approach will allow for a quick deployment of new services, which was known to the cloud service already. In this paper, we will introduce the most prominent candidates for softwarization such as software-defined networking (SDN) and network function virtualization (NFV) and explain the importance of these technologies for the upcoming 5G communication system and Tactile Internet applications realizing novel mobile edge computing, storage, and networking solutions. Specifically, we will discuss use cases of SDN/NFV such as network coding as a service, and ultrareliable distributed edge caching. Finally, we will describe our holistic testbed at the 5G Lab Germany as a fundamental step toward creating an experiment infrastructure that anticipates the 5G communication systems and Tactile Internet applications.

Social-Aware Edge Caching in Fog Radio Access Networks

Fog radio access networks (F-RANs) are becoming an emerging and promising paradigm for fifth generation cellular communication systems. In F-RANs, distributed edge caching techniques among remote radio heads (RRHs) and user equipment (UE) can effectively alleviate the burdens on the fronthaul toward the base band unit pool and the bandwidth of the RANs. However, it is still not clear as to how social relationships affect the performance of edge caching schemes. This paper attempts to analyze the impact of mobile social networks (MSNs) on the performance of edge caching in F-RANs. We propose a Markov-chain-based model to analyze edge caching among edge nodes (i.e., RRHs and MSNs), as well as data sharing among the potential MSNs from the viewpoint of content diffusion in the F-RANs. Moreover, we analyze the edge caching schemes among UE to minimize the bandwidth consumption in the RANs. Finally, the optimal edge caching strategies among RRHs in terms of caching locations and time are introduced to minimize the bandwidth consumption of fronthaul and storage costs in the F-RANs. Simulation results show that the proposed edge caching schemes among UE and RRHs are able to reduce the bandwidth consumption of RANs and fronthaul effectively.