Cache Servers Research Articles

Cooperative Cache in Cognitive Radio Networks: A Heterogeneous Multi-Agent Learning Approach

Deploying distributed cache in cognitive radio networks (CRNs), which spreads popular contents to the edge of network during the off-peak time through spectrum sharing, can reduce the deliver delay to users nearby without causing severe interference to the primary network. However, due to the un-predicable contents requirement as well as the band occupation of primary users, it is non-trivial to optimize the cache storage and contents fetching strategy of users dynamically. The letter proposes a heterogeneous multi-agent deep deterministic policy gradient (MADDPG) approach, which takes users and cache servers as two different types of agents to learn the cooperation and competition for mutual benefits. The numeral simulation demonstrates that comparing with the other single or homogeneous deep reinforcement learning (DRL) approaches, the proposed heterogeneous MADDPG can further reduce the delivery delay of users and enhance the cache efficiency of SBSs.

Energy-Aware Coded Caching Strategy Design With Resource Optimization for Satellite-UAV-Vehicle-Integrated Networks

The Internet of Vehicles (IoV) can offer safe and comfortable driving experience, by the enhanced advantages of space–air–ground-integrated networks (SAGINs), i.e., global seamless access, wide-area coverage, and flexible traffic scheduling. However, due to the huge popular traffic volume, limited cache/power resources, and the heterogeneous network infrastructures, the burden of backhaul link will be seriously enlarged, degrading the energy efficiency of IoV in SAGIN. In this article, to implement the popular content severing multiple vehicle users (VUs), we consider a cache-enabled satellite-UAV-vehicle-integrated network (CSUVIN), where the geosynchronous Earth orbit (GEO) satellite is regard as a cloud server, and unmanned aerial vehicles are deployed as edge caching servers. Then, we propose an energy-aware coded caching strategy employed in our system model to provide more multicast opportunities, and to reduce the backhaul transmission volume, considering the effects of file popularity, cache size, request frequency, and mobility in different road sections (RSs). Furthermore, we derive the closed-form expressions of total energy consumption both in single-RS and multi-RSs scenarios with asynchronous and synchronous services schemes, respectively. An optimization problem is formulated to minimize the total energy consumption, and the optimal content placement matrix, power allocation vector, and coverage deployment vector are obtained by well-designed algorithms. We finally show, numerically, our coded caching strategy can greatly improve energy efficient performance in CSUVINs, compared with other benchmarked caching schemes under the heterogeneous network conditions.

Information‐Centric Networking based content collection and caching mechanism for network public opinion

With the global influence of the COVID epidemic, network public opinion control is particularly important especially for the purpose of stabilizing the panic at home and abroad. Effective public opinion collection and caching mechanism has a positive significance for the rapid spread of network public opinion. Therefore, by analyzing the accurate and rapid requirements of public opinion communication, this paper introduces the concept of Information‐Centric Networking (ICN) to build a public opinion communication system. At the same time, the corresponding public opinion collection and caching mechanism is designed to optimize the dissemination process of the public opinion. The natural distributed structure of ICN makes the process of public opinion collection and caching distributed. Specifically, a suitable cache server is added between different public opinion collection servers via the distributed search engines. The experimental results show that the proposed distributed public opinion collection and caching mechanism can effectively deal with the spread of public opinion under the environment of the global COVID epidemic, including improving the accuracy of the public opinion transmission in time.

Joint Task Offloading and Caching for Massive MIMO-Aided Multi-Tier Computing Networks

In this paper, a massive multiple-input multiple-output (MIMO) relay assisted multi-tier computing (MC) system is employed to enhance the task computation. We investigate the joint design of the task scheduling, service caching and power allocation to minimize the total task scheduling delay. To this end, we formulate a robust non-convex optimization problem taking into account the impact of imperfect channel state information (CSI). In particular, multiple task nodes (TNs) offload their computational tasks either to computing and caching nodes (CCN) constituted by nearby massive MIMO-aided relay nodes (MRN) or alternatively to the cloud constituted by nearby fog access nodes (FAN). To address the non-convexity of the optimization problem, an efficient alternating optimization algorithm is developed. First, we solve the non-convex power allocation optimization problem by transforming it into a linear optimization problem for a given task offloading and service caching result. Then, we use the classic Lagrange partial relaxation for relaxing the binary task offloading as well as caching constraints and formulate the dual problem to obtain the task allocation and software caching results. Given both the power allocation, as well as the task offloading and caching result, we propose an iterative optimization algorithm for finding the jointly optimized results. The simulation results demonstrate that the proposed scheme outperforms the benchmark schemes, where the power allocation may be controlled by the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR).

Energy-Aware Collaborative Service Caching in a 5G-Enabled MEC With Uncertain Payoffs

Mobile edge computing (MEC) is an enabling technology for low-latency AI applications, by caching AI services originally deployed in remote data centers to 5G base stations in network edge. Due to limited computing resource of 5G base stations, not all services can be cached in base stations to meet the resource demands of user requests. Also, if the workload of a 5G base station reaches to its resource capacity, the energy consumption of the base station will be pushed up exponentially. To reduce the energy consumption and overcome resource limitations on base stations, an alternative is to allow the base stations to collaborate with each other to admit user requests. In this paper, we investigate the problem of collaborative service caching and request offloading between a 5G-enabled MEC and remote data centers, while meeting the quality of service (QoS) requirements of users, and resource capacities on base stations that are operated by multiple selfish network service providers. We aim to maximize the total payoff of all base stations. To this end, we first propose a two-stage optimization framework: In the first stage, we develop a mechanism that adopts a best-reply rule for dynamically distributed coalition formation. In the second stage, we propose a near-optimal payoff allocation method by devising a randomized algorithm with a provable approximation ratio. We then evaluate the performance of the proposed optimization framework by extensive experimental simulations. Simulation results show that the proposed framework outperforms its counterparts by achieving at least 30% higher payoff and 20% lower energy consumption of base stations.

A Profit-Aware Coalition Game for Cooperative Content Caching at the Network Edge

The user demands to delay-sensitive applications have put forward new requirements for the mobile networks. Edge caching as a promising way is proposed to enhance the Quality of Service (QoS) for end users at the network edge. Given the widely distributed edge nodes, the content providers (CPs) usually prefer to integrate them with cooperative caching services, by forming a cache coalition. Although such a coalition could be beneficial as a whole, it neglects the profits of individual members, which is one major concern in forming the coalition itself. Besides, due to the poor scalability, the conventional cooperation scheme, which only considers fixed edge nodes, cannot adapt to the spatial and temporal imbalance of user requests. In this article, we tackle the problems of coalition establishment and profit allocation among the coalition members. Particularly, by adopting both fixed edge nodes and mobile vehicles as caching nodes, we propose a hybrid service provisioning framework and cooperative service caching and workload scheduling methods. To maximize the profits in managing the caching resources in the established coalition, we devise an optimization model with a mixed-integer programming (MIP), in which the QoS requirements of end users and caching capacities of each coalition member are also considered as constraints. In addition, based on the Hedonic game theory, we propose a dynamic coalition algorithm to guide each member to join or leave the coalition at each time slot out of its own profits. The experimental results demonstrate that compared to the cases only considering fixed caching nodes, our hybrid caching scheme can improve: 1) the overall profit of the coalition by 53% and 2) the average profit of individual participants by 42%, respectively.

A Hybrid Genetic Algorithm for Service Caching and Task Offloading in Edge-Cloud Computing

Edge-cloud computing is increasingly prevalent for Internet-of-thing (IoT) service provisioning by combining both benefits of edge and cloud computing. In this paper, we aim to improve the user satisfaction and the resource efficiency by service caching and task offloading for edge-cloud computing. We propose a hybrid heuristic method to combine the global search ability of the genetic algorithm (GA) and heuristic local search ability, to improve the number of satisfied requests and the resource utilization. The proposed method encodes the service caching strategies into chromosomes, and evolves the population by GA. Given a caching strategy from a chromosome, our method exploits a dual-stage heuristic method for the task offloading. In the first stage, the dual-stage heuristic method pre-offloads tasks to the cloud, and offloads tasks whose requirements cannot be satisfied by the cloud to edge servers, aiming at satisfying as many tasks’ requirements as possible. The second stage re-offloads tasks from the cloud to edge servers, to get the utmost out of limited edge resources. Experimental results demonstrate the competitive edges of the proposed method over multiple classical and state-of-the-art techniques. Compared with five existing scheduling algorithms, our method achieves 11.3%–23.7% more accepted tasks and 1.86%–18.9% higher resource utilization.

A Particle Swarm Optimization With Lévy Flight for Service Caching and Task Offloading in Edge-Cloud Computing

Edge-cloud computing is an efficient approach to address the high latency issue in mobile cloud computing for service provisioning, by placing several computing resources close to end devices. To improve the user satisfaction and the resource efficiency, this paper focuses on the task offloading and service caching problem for providing services by edge-cloud computing. This paper formulates the problem as a constrained discrete optimization problem, and proposes a hybrid heuristic method based on Particle Swarm Optimization (PSO) to solve the problem in polynomial time. The proposed method, LMPSO, exploit PSO to solve the service caching problem. To avoid PSO trapping into local optimization, LMPSO adds lévy flight movement for particle updating to improve the diversity of particle. Given the service caching solution, LMPSO uses a heuristic method with three stages for task offloading, where the first stage tries to make full use of cloud resources, the second stage uses edge resources for satisfying requirements of latency-sensitive tasks, and the last stage improves the overall performance of task executions by re-offloaded some tasks from the cloud to edges. Simulated experiment results show that LMPSO has upto 156% better user satisfaction, upto 57.9% higher resource efficiency, and upto 155% greater processing efficiency, in overall, compared with other seven heuristic and meta-heuristic methods.

Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

With the development of internet of vehicles, the traditional centralized content caching mode transmits content through the core network, which causes a large delay and cannot meet the demands for delay-sensitive services. To solve these problems, on basis of vehicle caching network, we propose an edge collaborative caching scheme. Road side unit (RSU) and mobile edge computing (MEC) are used to collect vehicle information, predict and cache popular content, thereby provide low-latency content delivery services. However, the storage capacity of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time. Through content sharing, collaborative caching can relieve the storage burden on caching servers. Therefore, we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching (MVECC) scheme, so as to realize the collaborative caching among cloud, edge and vehicle. MVECC uses deep reinforcement learning to predict what needs to be cached on RSU, which enables RSUs to cache more popular content. In addition, MVECC also introduces a mobility-aware caching replacement scheme at the edge network to reduce redundant cache and improving cache efficiency, which allows RSU to dynamically replace the cached content in response to the mobility of vehicles. The simulation results show that the proposed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.

Joint power-trajectory-scheduling optimization in a mobile UAV-enabled network via alternating iteration

This work focuses on an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system based on device-to-device (D2D) communication. In this system, the UAV exhibits caching, computing and relaying capabilities to periodically provide specific service to cellular users and D2D receiver nodes in the appointed time slot. Besides, the D2D transmitter can provide additional caching services to D2D receiver to reduce the pressure of the UAV. Note that communication between multi-type nodes is mutually restricted and different links share spectrum resources. To achieve an improved balance between different types of node, we aim to maximize the overall energy efficiency while satisfying the quality-of-service requirements of the cellular nodes. To address this problem, we propose an alternating iteration algorithm to jointly optimize the scheduling strategies of the user, transmitting power of the UAV and D2D-TX nodes, and UAV trajectory. The successive convex approximation, penalty function, and Dinkelbach method are employed to transform the original problem into a group of solvable subproblems and the convergence of the method is proved. Simulation results show that the proposed scheme performs better than other benchmark algorithms, particularly in terms of balancing the tradeoff between minimizing UAV energy consumption and maximizing throughput.

Research on the Method of Implementing Named Data Network Interconnection Based on IP Network

In order to extend the application scope of NDN and realize the transmission of different NDNs across IP networks, a method for interconnecting NDN networks distributed in different areas with IP networks is proposed. Firstly, the NDN data resource is located by means of the DNS mechanism, and the gateway IP address of the NDN network where the data resource is located is found. Then, the transmission between different NDNs across the IP network is implemented based on the tunnel technology. In addition, in order to achieve efficient and fast NDN data forwarding, we have added a small number of NDN service nodes in the IP network, and proposed an adaptive probabilistic forwarding strategy and a link cost function-based forwarding strategy to make NDN data obtaining the cache service provided by the NDN service node as much as possible. The results of analysis and simulation experiments show that, the interconnection method of NDN across IP network proposed is generally effective and feasible, and the link cost function forwarding strategy is better than the adaptive probability forwarding strategy.

Service Caching Strategy based on Edge Computing and Reinforcement Learning

Service Caching Strategy based on Edge Computing and Reinforcement Learning

Intelligent Scheduling Method Supporting Stadium Sharing

At present, the fast-paced work and life make people under great pressure, and people’s enthusiasm for fitness is getting higher and higher, which is in contradiction with the shortage of existing stadiums. So it is considerably significant to open shared stadiums near where citizens live for booking. Therefore, how to allow citizens to book a suitable stadium according to their own needs through mobile phones or computers is an urgent problem to be solved. The booking of the shared stadium can be regarded as a mobile edge computing (MEC) scenario, and the problem can be transformed into task scheduling research under MEC through intelligent scheduling method. When using edge computing (EC) technology for service calculation, the mobile terminal needs to offload the service to the edge computing server. After the server completes the calculation, the calculation results will be sent back to the mobile terminal. Therefore, the calculation time and system energy consumption in the calculation process can be further reduced through task scheduling to improve user satisfaction. In this study, joint scheduling of service caching and task algorithm is proposed to reduce the latency of booking shared stadium request and improve user experience. The simulation results show that the proposed algorithm with edge cooperation idea can achieve lower average system latency at lower load level and can significantly reduce the cloud offloading ratio under low and middle pressure. In addition, the proposed algorithm uses the secondary transfer of more tasks to reduce the pressure of local task running. Finally, the quality of experience (QoE) satisfaction rate under low pressure is guaranteed.

Novel Architecture of Content Delivery Network Based on Big Data for Power Saving

This paper proposes an architecture of content delivery network (CDN) based on big data for power saving. There are two types of video content: hot content and cold content. When video content is accessed frequently, it is called hot content. Conversely, when video content is accessed infrequently, it is called cold content. In CDN, there is an origin server and a CDN cache server. A CDN cache server has a replicated content and provides its content to the end users nearby. Therefore, the user can receive the requested content from the closest proximity for fast content. The proposed architecture in this paper powers off the cold content server in CDN cache server when the number of cold content requests decreases. Hence, the proposed architecture for content delivery services based on power saving is expected to be useful for providing multimedia streaming services with low power consumption for content providers.

Service Prioritization in Information Centric Networking With Heterogeneous Content Providers

Service prioritization brings reasonable allocation of network resources and improves the overall quality of experience (QoE) of users, but it has not been thoroughly investigated in information centric networking (ICN). Existing works lack adaptability and they cannot ensure specific content provider (CP) get well caching service which is one of the most important functions in ICN. In this paper, we firstly propose a service prioritization scheme to flexibly provide different caching services for heterogeneous CPs to improve the overall network efficiency. The main idea is to allocate dedicated cache space for paying CPs and provide prioritized caching service for them, while normal CPs only enjoy the normal caching service. The scheme can be divided into two phases. First, we select a group of nodes with higher importance as core nodes based on network topology, and pair each edge node to a core node following the two-sided many-to-one matching algorithm. Second, we dynamically allocate and manage the dedicated cache space for core nodes. We model the allocation of dedicated cache space and convert it into a convex optimization problem to solve. After that, a practical caching strategy and system design are implemented in the ndnSIM simulator. Finally, we evaluate our scheme and conduct comparative experiments with the most representative work diff-caching, simulation results show that our scheme outperform it in terms of both delay and cache hit ratio.

Towards a Distributed Caching Service at the WiFi Edge Using Wi-Cache

Caching content close to the end users, e.g., at cellular base stations (BSs), WiFi access points (APs), and end user devices is known to improve efficiency and effectiveness of content delivery. This motivates the development of caching-as-a-service where edge networks and devices provide storage capacity to content providers, and enable them to strategically populate these caches to improve user experience in the targeted network. In this paper, we describe Wi-Cache, a prototype for providing caching-as-a-service at the WiFi edge. Wi-Cache is an SDN (Software Defined Networking) based distributed content caching system at the WiFi edge that uses storage at the APs for caching content. Wi-Cache caches content on wireless APs and delivers them to mobile clients when they are requested. It allows content providers to have fine-grained control over the AP-caches and also execute efficient content placement and delivery algorithms at the WiFi edge using a set of APIs that are provided by Wi-Cache. We also show the effectiveness of the Wi-Cache system using an extensive set of experiments.

Offloading Tasks With Dependency and Service Caching in Mobile Edge Computing

In Mobile Edge Computing (MEC), many tasks require specific service support for execution and in addition, have a dependent order of execution among the tasks. However, previous works often ignore the impact of having limited services cached at the edge nodes on (dependent) task offloading, thus may lead to an infeasible offloading decision or a longer completion time. To bridge the gap, this article studies how to efficiently offload dependent tasks to edge nodes with limited (and predetermined) service caching. We formally define the problem of offloading dependent tasks with service caching (ODT-SC), and prove that there exists no algorithm with constant approximation for this hard problem. Then, we design an efficient convex programming based algorithm (CP) to solve this problem. Moreover, we study a special case with a homogeneous MEC and propose a favorite successor based algorithm (FS) to solve this special case with a competitive ratio of O(1)O(1)<; inline-graphic xlink:href="zhao-ieq1-3076687.gif"/>. Extensive simulation results using Google data traces show that our proposed algorithms can significantly reduce applications' completion time by about 21-47 percent compared with other alternatives.

A formal pattern of information system design

A new design pattern intended for distributed cloud-based information systems is proposed. Pattern is based on the traditional client-server architecture. The server side is divided into three principal components: data storage, application server and cache server. Each component can be used to deploy parts of several independent information systems, thus realizing shared-resource approach. A strategy of separation of competencies between the client and the server is proposed. The strategy assumes that the client side is responsible for application logic and the server side is responsible for data storage consistency and data access control. Data protection is ensured by means of two particular approaches: at the entity level and at the transaction level. The application programming interface to access data is presented at the level of identified transaction descriptors.

Cooperative Caching Services on High-Speed Train by Reverse Auction

With the explosion of data traffic, the use of caching technology to offload data traffic is a very effective way to alleviate network pressure, especially in high-speed train (HST) scenarios. And when users participate in the caching game, the costs of data offloading are greatly reduced. However, current research on users’ participation in data traffic offloading is not much; and there are user security and data reliability issues in the cache of device-to-device (D2D) network. Therefore, in this paper, we propose a novel scheme that user terminals (UTs) cooperatively cache wireless services to the intelligent routing relay (IRR) side in HST scenarios; and we establish a reverse auction model to motivate UTs to collaborate and cache. In this model, IRR acts as the purchaser of the wireless services and UTs are suppliers and consumers. We propose a synchronous reverse auction strategy (S-RAS) to maximize the social incomes at first. However, UTs often have to wait for a long time before the auction begins. Then, we adopt an asynchronous reverse auction strategy (A-RAS) to minimize UTs’ waiting time. Ultimately, in order to minimize UTs’ waiting time while maximizing social incomes, we propose the popularity based hybrid reverse auction strategy (PB-H-RAS) considering both the advantages of S-RAS and A-RAS. Simulation and experiment results verify the effectiveness of the proposed PB-H-RAS strategy; and they also show that the performances of PB-H-RAS strategy are better than the other two strategies in terms of final incomes and completion rate.

Multicast-aware optimization for resource allocation with edge computing and caching

Mobile edge computing (MEC) is rising as a key technology for computation-intensive and delay-sensitive applications. Many works have focused on MEC, but most of them only consider unicast scenarios, and ignore multicast issues. The reason is that MEC aiming at personalized computing of users conflicts with multicast which demands the same data stream. This makes MEC and multicast seem inconsistent. However, in fact, there will be lots of services, whose computation process is different but the result may be same (e.g. media push), which can greatly benefit from multicast over MEC and make the combination of multicast and MEC meaningful. In this paper, we first consider challenges and propose multicast-aware resource allocation for MEC, which jointly optimizes computing and caching in multicast scenarios. We formulate the problem by considering user request, network communication, service caching and service computing. But this model is knotty because it is an optimization problem with mixed discrete and continuous variables. Besides its optimization objective is the average value over a long time. Considering the complexity, we first transform the problem into an online optimization, which jointly minimizes the average time delay and energy consumption, by stochastic optimization. Then we separate the discrete variables and continuous variable, and decompose the problem into two subproblems. By solving subproblems, an efficient online algorithm called MA-ECC is proposed. Finally, we compare it with other three baseline methods, and result shows that MA-ECC can effectively reduce service latency while still keeping energy consumption low.