Backhaul Traffic Load Research Articles

GCS: Collaborative video cache management strategy in multi-access edge computing

With the rapid development of multimedia services in wireless mobile networks, the video data traffic has increased exponentially. Traditional video traffic service based on cloud computing caused a large amount of traffic load and longer access delay, which severely reduces the quality of service (QoS) of users. Multi-Access Edge Computing (MEC) can directly serve user requests so as to greatly reduce the traffic load and shorten access delay. At present, distributed caching is widely used in the caching deployment of base stations (BSs). However, the caching capacity of a single BS is generally particularly limited, which will degrade the performance of wireless mobile network. In this paper, a collaborative caching scheme in the heterogeneous MEC networks is designed, and the edge caching of macro base station (MBS) and small base stations (SBSs) is utilized to bring storage resources closer to users. In addition, an optimization problem of content caching is formulated to minimize the total delay cost of all users requesting content in the MEC networks. In order to solve this problem, cache management strategy (GCS) is proposed, which consists of greedy cache placement strategy and greedy cache update strategy. Finally, numerical simulations demonstrate that the GCS scheme effectively improves the cache hit rate, and significantly reduces the average delay and backhaul traffic load.

Joint edge caching and dynamic service migration in SDN based mobile edge computing

In recent years, with the rapid growth in the demand for online streaming services, video streaming platforms are becoming more and more popular, and users' demand for low latency and high-quality services is increasing. Therefore, in order to be able to allocate caching resources reasonably to serve as many user requests as possible, and reduce latency and energy consumption, the edge cooperative caching method based on delay and energy consumption balance in SDN based mobile edge computing is proposed. In the proposed caching method, firstly, the multilayer perceptron neural network is used to predict the video content requested by the mobile user. Secondly, an objective function to minimize delay and energy consumption is established, and an edge cache optimization model is constructed. Finally, the branch-and-bound algorithm is used to obtain the optimal edge cache strategy. Meanwhile, to provide users with seamless service migration to ensure service continuity and high-quality services, the dynamic service migration method based on deep Q learning is proposed. In the proposed service migration method, firstly, the service migration problem is expressed as a Markov decision process. Secondly, the service migration process is analyzed and a service migration reward function is constructed. Finally, deep Q learning is used to obtain the optimal service migration strategy. In the experiment, the proposed edge caching algorithm can effectively improve the cache hit rate, reduce the backhaul traffic load, and control the average access delay and energy cost. Moreover, the proposed service migration algorithm can effectively reduce the number of service migrations and transmission cost, improve the success rate of service migration, and reduce the average traffic consumed by migration.

Cache-Enabled Power Line Communication Networks: Caching Node Selection and Backhaul Energy Optimization

Power line communication (PLC) advances smart grid technology by offering a convenient and efficient data transmission service. However, due to the severe PLC channel condition and ever-growing traffic load in the PLC backhaul, reliable communications among widely distributed in-home PLC users is highly energy-consuming. In this paper, we put forth a cache-enabled PLC network comprising a single master base station (MBS) and mutiple slave base stations (SBSs) each bound with an in-home network to alleviate the backhaul traffic load and reduce the backhaul energy consumption. In the proposed scheme, the SBSs with the local caches pre-download the popular contents from the MBS through the PLC backhaul, and the in-home users request the contents from nearby caching nodes (CNs) or the MBS. Apart from the cache-enabled PLC design, our contributions also lie in the selection of CNs and optimal content placement algorithm. First, we propose a selection algorithm to activate the SBSs with high influence as the CNs, according to a directed graph that models the requesting relations among all SBSs. Second, we design an optimal content placement algorithm to minimize the backhaul energy consumption for the contents with different sizes and user preferences. Simulation results show that our cache-enabled PLC scheme with the caching node selection and optimal content placement can dramatically reduce the backhaul energy consumption, compared with the existing caching strategies.

Caching and resource allocation in small cell networks

Small Cell Network (SCN) is regarded as a promising way to improve the network capacity for it’s high link quality, low deployment cost and high spectrum utility efficiency. However, several issues limit the performance of SCNs, such as the interference from adjacent cells, limited backhaul resource, and the limited access resource. In this paper, we study cache placement problem with cooperative transmission. Specifically, our caching scheme minimizes the backhaul traffic load, meanwhile guaranteeing the spectral efficiency requirement of the network. Particularly, our scheme allows the BS without the locally cached file to participate the CoMP transmission by downloading through backhaul in real-time. Afterwards, we propose a resource allocation scheme to reduce the file downloading time.

A View Synthesis-Based 360° VR Caching System Over MEC-Enabled C-RAN

With the development of virtual reality (VR) technology, the future of VR systems is evolving from single-user wired connections to multi-user wireless connections. However, wireless online rendering and transmission incur extra processing and transmission latency, as well as higher bandwidth requirements. To meet the requirements of wireless VR applications and enhance the quality of the VR user experience, this paper designs a view synthesis-based 360° VR caching system over Cloud Radio Access Network (C-RAN), where both mobile edge computing (MEC) and hierarchical caching are supported. In the system, an MEC-Cache Server is deployed in the pooled Base band Units (BBU pool) and used for view synthesis and caching. In addition, the remote radio heads (RRHs) can also cache some video contents. If the requested content of a specific view is cached in the BBU pool or RRHs, or can be synthesized with the aid of the cached adjacent views, it is unnecessary to request the content from the remote VR video source server. Therefore, the transmission latency and backhaul traffic load for VR services can be decreased. We formulate a hierarchical collaborative caching problem aiming to minimize the transmission latency, which is proved NP-hard. To address the impractical expenses of the offline optimal method, an online MaxMinDistance caching algorithm with low complexity is proposed. Numerical simulation results demonstrate that the proposed caching strategy provides significantly improved cache hit rate, backhaul traffic load, transmission latency, and Quality of Experience (QoE) performances relative to conventional caching strategies.

Cross-Entropy Method for Content Placement and User Association in Cache-Enabled Coordinated Ultra-Dense Networks

Due to the high splitting-gain of dense small cells, Ultra-Dense Network (UDN) is regarded as a promising networking technology to achieve high data rate and low latency in 5G mobile communications. In UDNs, each User Equipment (UE) may receive signals from multiple Base Stations (BSs), which impose severe interference in the networks and in turn motivates the possibility of using Coordinated Multi-Point (CoMP) transmissions to further enhance network capacity. In CoMP-based Ultra-Dense Networks, a great challenge is to tradeoff between the gain of network throughput and the worsening backhaul latency. Caching popular files on BSs has been identified as a promising method to reduce the backhaul traffic load. In this paper, we investigated content placement strategies and user association algorithms for the proactive caching ultra dense networks. The problem has been formulated to maximize network throughput of cell edge UEs under the consideration of backhaul load, which is a constrained non-convex combinatorial optimization problem. To decrease the complexity, the problem is decomposed into two suboptimal problems. We first solved the content placement algorithm based on the cross-entropy (CE) method to minimize the backhaul load of the network. Then, a user association algorithm based on the CE method was employed to pursue larger network throughput of cell edge UEs. Simulation were conducted to validate the performance of the proposed cross-entropy based schemes in terms of network throughput and backhaul load. The simulation results show that the proposed cross-entropy based content placement scheme significantly outperform the conventional random and Most Popular Content placement schemes, with with 50% and 20% backhaul load decrease respectively. Furthermore, the proposed cross-entropy based user association scheme can achieve 30% and 23% throughput gain, compared with the conventional N-best, No-CoMP, and Threshold based user association schemes.

Data Correlation-Aware Resource Management in Wireless Virtual Reality (VR): An Echo State Transfer Learning Approach

In this paper, the problem of wireless virtual reality (VR) resource management is investigated for a wireless VR network in which VR contents are sent by a cloud to cellular small base stations (SBSs). The SBSs will collect tracking data from the VR users, over the uplink, in order to generate the VR content and transmit it to the end-users using downlink cellular links. For this model, the data requested or transmitted by the users can exhibit correlation, since the VR users may engage in the same immersive virtual environment with different locations and orientations. As such, the proposed resource management framework can factor in such spatial data correlation, to better manage uplink and downlink traffic. This potential spatial data correlation can be factored into the resource allocation problem to reduce the traffic load in both uplink and downlink. In the downlink, the cloud can transmit 360 contents or specific visible contents that are extracted from the original 360 contents to the users according to the users' data correlation to reduce the backhaul traffic load. For uplink, each SBS can associate with the users that have similar tracking information so as to reduce the tracking data size. This data correlation-aware resource management problem is formulated as an optimization problem whose goal is to maximize the users' successful transmission probability, defined as the probability that the content transmission delay of each user satisfies an instantaneous VR delay target. To solve this problem, an echo state networks (ESNs) based transfer learning is introduced. By smartly transferring information on the SBS's utility, the proposed transfer-based ESN algorithm can quickly cope with changes in the wireless networking environment.

Cooperative Hierarchical Caching and Request Scheduling in a Cloud Radio Access Network

In this article, we propose a novel cooperative hierarchical caching framework in a Cloud Radio Access Network (C-RAN), in which a new cloud-cache at Cloud Processing Unit (CPU) is envisioned to bridge the storage-capacity/delay-performance gap between the traditional edge-based and core-based caching paradigms. A delay-cost model is introduced and the cache placement problem is formulated that aims at minimizing the average delay-cost of content delivery in the network. Given the NP-completeness of the cache placement problem, we propose a low-complexity heuristic cache-management strategy comprising of a proactive cache-distribution algorithm and a reactive cache-replacement algorithm. Furthermore, a Cache-Aware Request Scheduling (CARS) algorithm is devised in order to optimize online the tradeoff between content download rate and content access delay. Via extensive numerical simulations—carried out using both real-world YouTube video requests and synthetic content requests—it is demonstrated that the proposed cache-management strategy outperforms traditional caching strategies in terms of cache hit ratio, average content access delay, and backhaul traffic load. Additionally, it is shown that the proposed CARS algorithm achieves superior tradeoff performance over traditional approaches that optimize either users’ rate or access delay alone.

Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks

Caching close to users in a radio access network (RAN) has been identified as a promising method to reduce a backhaul traffic load and minimize latency in 5G and beyond. In this paper, we investigate a novel community detection inspired by a proactive caching scheme for device-to-device (D2D) enabled networks. The proposed scheme builds on the idea that content generated/accessed by influential users is more probable to become popular and thus can be exploited for pro-caching. We use a Clustering Coefficient based Genetic Algorithm (CC-GA) for community detection to discover a group of cellular users present in close vicinity. We then use an Eigenvector Centrality measure to identify the influential users with respect to the community structure, and the content associated to it is then used for pro-active caching using D2D communications. The numerical results show that, compared to reactive caching, where historically popular content is cached, depending on cache size, load and number of requests, up to 30% more users can be satisfied using a proposed scheme while achieving significant reduction in backhaul traffic load.

Extensive Cooperative Caching in D2D Integrated Cellular Networks

Device-to-device (D2D) communication is a promising supplement to cellular networks, since it provides high rate transmission without changing network infrastructure. For a D2D integrated cellular network, caching popular content (e.g., multimedia files) at a base station (BS) or a D2D device near to a requesting user not only reduces content delivery delay, but also alleviates backhaul traffic load. In order to minimize content delivery delay, in this letter, we propose an extensive cooperative caching (EC-Caching) scheme, which allows inter-BS, inter-device and between-BS-and-device cooperation in content caching. Simulation results demonstrate that the extension in degree of cooperation between the caching nodes contributes significant performance gain in terms of content delivery delay and cache hit rate, especially in populated areas.

Cooperative Hierarchical Caching in 5G Cloud Radio Access Networks

Over the last few years, C-RAN is proposed as a transformative architecture for 5G cellular networks that brings the flexibility and agility of cloud computing to wireless communications. At the same time, content caching in wireless networks has become an essential solution to lower the content- access latency and backhaul traffic loading, leading to user QoE improvement and network cost reduction. In this article, a novel cooperative hierarchical caching (CHC) framework in C-RAN is introduced where contents are jointly cached at the BBU and at the RRHs. Unlike in traditional approaches, the cache at the BBU, cloud cache, presents a new layer in the cache hierarchy, bridging the latency/capacity gap between the traditional edge-based and core-based caching schemes. Trace-driven simulations reveal that CHC yields up to 51 percent improvement in cache hit ratio, 11 percent decrease in average content access latency, and 18 percent reduction in backhaul traffic load compared to the edge-only caching scheme with the same total cache capacity. Before closing the article, we discuss the key challenges and promising opportunities for deploying content caching in C-RAN in order to make it an enabler technology in 5G ultra-dense systems.