Cooperative Caching Mechanism Research Articles

Two-Hop Cooperative Caching and UAVs Deployment Based on Potential Game

This paper explores the joint cache placement and 3D deployment of Unmanned Aerial Vehicle (UAV) groups, utilizing potential game theory and a two-hop UAV cooperative caching mechanism, which could create a tradeoff between latency and coverage. The proposed scheme consists of three parts: first, the initial 2D location of UAV groups is determined through K-means, with the optimal altitude based on the UAV coverage radius. Second, to balance the transmission delay and coverage, the MOS (Mean Opinion Score) and coverage are designed to evaluate the performance of UAV-assisted networks. Then, the potential game is modeled, which transfers the optimization problem into the maximization of the whole network utility. The locally coupling effect resulting from action changes among UAVs is considered in the design of the potential game utility function. Moreover, a log-linear learning scheme is applied to solve the problem. Finally, the simulation results verify the superiority of the proposed scheme in terms of the achievable transmission delay and coverage performance compared with two other tested schemes. The coverage ratio is close to 100% when the UAV number is 25, and the user number is 150; in addition, this game outperforms the benchmarks when it comes to maximizing MOS of users.

Greedy cooperative cache placement for mobile edge networks with user preferences prediction and adaptive clustering

Caching the most likely to be requested content at the base stations in a cooperative manner can facilitate direct content delivery without fetching content from the remote content server in a mobile edge network. This alleviates the user-perceived latency, reduces the burden on backhaul and minimizes the duplicated content transmissions. In addition to static content popularity, it is also important to consider the users’ dynamic behaviour for real-time applications. Unevenly distributed users, heterogeneity of the user preferences, and activity level in large-scale mobile edge networks are important issues that have been addressed in this work. The user preference has been predicted with the long short-term memory (LSTM) to capture the dynamic user behaviour and then users are clustered based on the predicted user preferences. The cache placement problem is formulated as Integer linear programming to maximize the saved delay with deadline and capacity constraints. Further, we devise a greedy algorithm by proving the submodular property of the objective function with matroid constraints, which can achieve (1−1e) optimality with linear computational complexity. Extensive simulation results demonstrate that the proposed cooperative caching mechanism significantly improves the performance in terms of acceleration ratio, hit ratio, and cache utilization compared with existing mechanisms.

MEC-Based Cooperative Multimedia Caching Mechanism for the Internet of Vehicles

Multimedia applications are expected to widely deploy over vehicular networks. In order to meet the low-latency and high-speed transmission requirements of multimedia applications, edge caching is introduced to reduce the network traffic and the transmission delay. Due to the limited storage of the edge cache server, an efficient approach for the content management plays a decisive role for the edge cache performance. This paper proposes a vehicle-to-infrastructure-based cooperative caching mechanism for Internet of Vehicles to improve the edge cache utilization. The system model is established with the goal of maximizing the cooperative caching hit rate. To jointly consider the collaborations between macrobase stations (MBS) and multiple roadside units (RSU), we propose a reinforcement learning algorithm to adaptively control the cache management. According to the content popularity and the network status, the proposed algorithm can dynamically adjust cached content across relevant MBSs and RSUs. The simulation results show that the proposed cooperative caching mechanism significantly improve the cache utilization and the quality of services.

Cooperative cache update using multi-agent recurrent deep reinforcement learning for mobile edge networks

Caching the most likely to be requested content at the base stations in a cooperative manner can facilitate direct content delivery without fetching content from the remote content server and thus alleviate the user-perceived latency, reduce the burden on backhaul and minimize the duplicated content transmissions. Content popularity plays a vital role, and it drives caching on edge. In the literature, earlier works considered the content popularity either known earlier or obtained on prediction. However, the content popularity is time-varying and unknown in reality, so the above assumption makes it less practical. Therefore, this paper considers the cooperative cache replacement problem in a realistic scenario where the edge nodes are unaware of the content popularity in mobile edge networks. To address this problem, the main contribution of this paper is to design an intelligent content update mechanism using multi-agent deep reinforcement learning in dynamic environments. With the goal of maximizing the saved delay with deadline and capacity constraints, we formulate the cache replacement problem as Integer linear programming problem. Considering the dynamic nature of the content popularity, high dimensional parameters, and for an intelligent caching decision, we model the problem as a partially observable Markov decision process and present an efficient deep reinforcement learning algorithm by embedding the long short-term memory network (LSTM) into a multi-agent deep deterministic policy gradient formalism. The LSTM inclusion reduces the instability produced by partial observability of the environment. Extensive simulation results demonstrate that the proposed cooperative caching mechanism significantly improves the performance in terms of reward, acceleration ratio and hit ratio compared with existing mechanisms.

Joint optimization of cache placement and request routing in unreliable networks

Edge caching is a prevailing media delivery technology where data is hosted at the edge nodes with computing and storage capability in close proximity to users, in order to expand the backhaul network capacity and enhance users' quality of experience (QoE). The existing work in this area often neglects the fact that large-scale distributed cache networks are not particularly reliable and many edge nodes are prone to failure. In this paper we investigate and develop a novel, cooperative caching mechanism for content placement and request routing. We aim to minimize the content access delay and achieve the optimization in polynomial time, taking into account failures in an unreliable network environment with limited edge storage and bandwidth. We introduce two optimization algorithms: 1) a primal-dual algorithm that is based on the Lagrangian dual decomposition and subgradient method, and 2) a greedy-based approximation algorithm with a proven approximation ratio. Numerical results show that the proposed algorithms outperform other comparative approaches in synthetic and real network environments, and the approximation algorithm is particularly suitable for networking scenarios with sparse node connectivity and resources in short supply.

CL-ADMM: A Cooperative-Learning-Based Optimization Framework for Resource Management in MEC

We consider the problem of the intelligent and efficient resource management framework in mobile-edge computing (MEC), which can reduce delay and energy consumption, and features distributed optimization and efficient congestion avoidance. In this article, we present a cooperative learning framework for resource management in MEC from an alternating direction method of multipliers (ADMMs) perspective, named the CL-ADMM framework. First, computing a task requires both the user personal data and corresponding program that processes it, to efficiently cache program in a group, a novel program popularity estimation scheme is proposed, which is based on a semi-Markov process model. Then, a greedy program cooperative caching mechanism is established, which can effectively reduce delay and energy consumption. Second, to address group congestion, a dynamic task migration scheme based on improved cooperative Q-learning is proposed, which can effectively reduce delay and alleviate congestion. Third, to minimize delay and energy consumption for resource allocation in a group, we formulate it as an optimization problem with a large number of variables, and then exploit a novel ADMM-based scheme to solve this problem, which can reduce the complexity of the problem with a new set of auxiliary variables, these subproblems are all convex problems that can be solved by using a primal-dual approach, which guarantees its convergence. Finally, we prove its convergence by using the Lyapunov theory. The numerical results demonstrate the effectiveness of the CL-ADMM framework in reducing delay and energy consumption in MEC.

Smart-Cluster-Based Distributed Caching for Fog-IoT Networks

The idea of co-operative caching in a cache-enabled wireless network has gained much interest due to its services in terms of short service delay and improved transmission rate at the user end. In this article, we consider a co-operative caching mechanism for a fog-enabled Internet of Things (IoT) network. We propose a delay-minimizing policy for fog nodes (FNs), where the goal is to reduce the service delay for the IoT nodes, also known as terminal nodes (TNs). To this end, a novel smart clustering mechanism is proposed, aiming to efficiently assign FNs to the TNs while improving the network benefit by finding a tradeoff between the delay and the network’s energy consumption. We perform mathematical analysis and extensive simulations to highlight the potential gain and the proposed policy.

Profit‐oriented cooperative caching algorithm for hierarchical content centric networking

Cooperative caching among nodes is a hot topic in Content Centric Networking (CCN). However, the cooperative caching mechanisms are performed in an arbitrary graph topology, leading to the complex cooperative operation. For this reason, hierarchical CCN has received widespread attention, which provides simple cooperative operation due to the explicit affiliation between nodes. In this study, the authors propose a heuristic cooperative caching algorithm for maximising the average provider earned profit under the two-level CCN topology. This algorithm divides the cache space of control nodes into two fractions for caching contents which are downloaded from different sources. One fraction caches duplicated contents and the other caches unique contents. The optimal value of the split factor can be obtained by maximising the earned profit. Furthermore, they also propose a replacement policy to support the proposed caching algorithm. Finally, simulation results show that the proposed caching algorithm can perform better than some traditional caching strategies.

Optimal cache placement with local sharing: An ISP guide to the benefits of the sharing economy

Traffic patterns on the Internet are changing, with video and user-generated content (UGC) taking an increasing share of the volume. The dramatic increase of content providers and content peering has shown to decrease the profit of ISPs. In this work, we suggest the use of a cooperative caching mechanism between peering ISPs to farther mitigate this problem. We devise a model for cooperative cache placement with global knowledge, and design a solution for total objects placement that maximizes the demand satisfied from local or shared cache, under the following constraints: (1) The local demand is known at each ISP; (2) ISPs share only if they can satisfy at least the same demand as before the sharing. We introduce sharing cost, reciprocity, and fairness constraints. Through extensive experiments that are based on typical demand distribution, we demonstrate the clear benefits of sharing for reducing costs at ISPs, with an increase of available content in the cache of up to 35%.

Social-aware cooperative caching mechanism in mobile social networks

To provide efficient content sharing service in a mobile social networks (MSNs), the social-aware cooperative caching mechanism is proposed. The mechanism considers the social tie strength, trustworthiness, and encountering probability among users to quantify the ability of a user, in terms of providing content sharing service to others. By analyzing the caching behaviors of users, a community-based content caching model is established to accurately estimate the popularity of contents. Based on user's ability of providing content sharing service and content popularity, the cooperative caching scheme is designed to provide differentiated content sharing services for various contents. The proposed solution synthetically considers social relations among users, content popularity, and user's mobility so that it provides a better performance for content sharing in MSNs. This is also confirmed in the simulations which show that the proposed cooperative caching mechanism not only effectively improves the success ratio of content sharing, but also greatly improves the user cache utilization efficiency.

A cooperative caching and transmission mechanism towards QoE-aware for surveillance video based on cloud computing environment

In cloud computing environment, the limited communication capability of a single wireless access technology cannot meet the strict requirements of video transmission. In video transmission system, the feedback information and quality of service (QoE) can be obtained from the receiver through the union of transmission and reception. Based on which, a quality adaptive algorithm towards QoE for surveillance video is proposed. A QoE-aware buffer model is established in this paper, which can change the dynamic network band. Width and user experience match the quality of video transmission and reduce the number of video quality level switching. In the proposed algorithm, the buffer length is controlled within the target length, which will reduce the probability of buffer overflow and underflow. The data packets are layered according to the importance of the measurement parameters in encoding macroblocks, and they are smoothly transited by using buffers during the quality switching, which will reduce the quality level step by step and improve the user experience. Finally, the simulation results show that the proposed mechanism has slightly improved PSNR and throughput performance compared with several other comparison algorithms. And the average image PSNR of the proposed algorithm has been improved by 6.5 dB compared with other algorithms.

Cache-Aware Query Optimization in Multiapplication Sharing Wireless Sensor Networks

Hosting multiple applications in a shared infrastructure of wireless sensor networks is a trend nowadays, and sharing sensory data for answering concurrent applications is a promising and energy-efficient strategy. To address this challenge, this paper proposes an energy-efficient query optimization mechanism for supporting multiple concurrent applications leveraging our two-tier cooperative caching mechanism. Specifically, query requests for concurrent applications are represented as binary strings, which are reduced to a single one for avoiding the reprocessing of shared subquery requests. This reduced query request is answered through our cooperative caching mechanism, where sensory data, which are highly possible to be reused for answering forthcoming query requests, are cached at the sink node (SN). Besides, the gray model GM(1, 1) is adopted for forecasting sensory data units which may be interested mostly by forthcoming query requests. These units of sensory data may be prefetched from the network and cached at the SN. Experimental evaluation shows that this approach can reduce the energy consumption significantly, and improve the network capacity to an extent, especially when the number of concurrent query requests is relatively large.

A Novel Two-Tier Cooperative Caching Mechanism for the Optimization of Multi-Attribute Periodic Queries in Wireless Sensor Networks

Wireless sensor networks, serving as an important interface between physical environments and computational systems, have been used extensively for supporting domain applications, where multiple-attribute sensory data are queried from the network continuously and periodically. Usually, certain sensory data may not vary significantly within a certain time duration for certain applications. In this setting, sensory data gathered at a certain time slot can be used for answering concurrent queries and may be reused for answering the forthcoming queries when the variation of these data is within a certain threshold. To address this challenge, a popularity-based cooperative caching mechanism is proposed in this article, where the popularity of sensory data is calculated according to the queries issued in recent time slots. This popularity reflects the possibility that sensory data are interested in the forthcoming queries. Generally, sensory data with the highest popularity are cached at the sink node, while sensory data that may not be interested in the forthcoming queries are cached in the head nodes of divided grid cells. Leveraging these cooperatively cached sensory data, queries are answered through composing these two-tier cached data. Experimental evaluation shows that this approach can reduce the network communication cost significantly and increase the network capability.

Cooperative Media Data Streaming with Scalable Video Coding

Peer-to-peer (P2P) streaming has become a promising approach for disseminating streaming media content from the server to a large number of interested clients. It still faces many challenges, however, such as high churn rate of peer clients, uplink bandwidth constraints of participating peers, and heterogeneity of client capacities. To resolve these issues, this paper presents a new P2P streaming framework that combines with the advantages of both mesh-based multisource overlay networks and scalable video coding techniques, specifically with multiple description coding (MDC), to improve the streaming quality of participating clients. In this paper, an optimized allocation policy (OAP) algorithm was proposed for multidescriptions allocation. Extensive simulations show that the proposed system achieves higher quality of service by peer-assisted cooperative streaming and MDC coding. In addition, we investigate an efficient cooperative caching mechanism for streaming service. The target is to provide low-latency and high-quality services by virtue of peer collaboration. The storage and replacement of caching content are based on a segment-based strategy. Through comparison, we demonstrate the effectiveness of the proposed scheme and compare with the traditional LRUF scheme through extensive experiments over large Internet-like topologies. Results show that the system outperforms some previous schemes in resource utilization and is more robust and resilient to node departure, which demonstrate that it is well suited for quality adaptive cooperative streaming applications.

Weblins: A scalable WWW cluster-based server

With the ever-growing web traffic, cluster-based web servers have become very important to the Internet infrastructure. Thus, making the best use of all available resources in the cluster to achieve high performance is a significant research issue. In this paper, we present Weblins, a cluster-based web server that can achieve good throughput. Weblins has Gobelins operating system as platform. Gobelins is an efficient single system image operating system that transparently makes use of the resources available in the cluster. The architecture of Weblins is fully distributed. Weblins implements a content-aware request distribution policy via a new interface on top of Gobelins. Popular web files are dynamically replicated on all nodes via a cooperative caching mechanism. For the non-popular files, the requests are handed-off to the corresponding nodes via the TCP Handoff protocol. Simulation results show that the strategy used by Weblins is more suitable for cluster-based Web severs in comparison with pure content-aware strategy and pure cooperative caching strategy.