Dynamic multiuser resource allocation and adaptation for wireless systems

Driven by the increasing popularity of wireless broadband services, future wireless systems will witness a rapid growth of high-data-rate applications with very diverse quality of service requirements. To support such applications under limited radio resources and harsh wireless channel conditions, dynamic resource allocation, which achieves both higher system spectral efficiency and better QoS, has been identified as one of the most promising techniques. In particular, jointly optimizing resource allocation across adjacent and even nonadjacent layers of the protocol stack leads to dramatic improvement in overall system performance. In this article we provide an overview of recent research on dynamic resource allocation, especially for MIMO and OFDM systems. Recent work and open issues on cross-layer resource allocation and adaptation are also discussed. Through this article, we wish to show that dynamic resource allocation will become a key feature in future wireless communications systems as the subscriber population and service demands continue to expand.     

A fuzzy modeling based dynamic resource allocation strategy in service grid

One of the major challenges in service grid is to guarantee the promised quality of service (QoS) for all the admitted users or applications, while maximizing the resource utilization through dynamic resource sharing. An efficient resource allocation method should ensure the service QoS and balance the load among service grid nodes which are often highly dynamic, heterogeneous and linked by wide-area network. In this paper, a new dynamic resource allocation method is presented and analyzed based on fuzzy modeling to solve the adaptation between heterogeneous applications with multiple QoS requirements and grid resource. Simulations in service grid with heterogeneous QoS requirements reveal that the proposed dynamic resource allocation method can distribute most suitable resource among the different service quickly and sensitively as the service QoS demand varies under the constraint of achieving maximum utilization of grid resources.     

The Optimizing QoS Miss Rate Scheme for the Data Centers

1IntroductionThe third-party services are hosted on the data cen-ters where the service owner pays rent for server re-sources,and in return,the service is provided guaran-tees on sufficient resources to meet service Quality ofService(QoS)requirement.In th…     

基于波分复用的智能光网节点管理模型

分析了基于波分复用(WDM)技术的下一代智能光网络中节点管理系统的功能模型和管理任务,给出了一种综合考虑业务分级和支持多径路由的算法,可在WDM光网中实现基于性能分析的资源动态配置管理。采用自行开发的资源动态配置软件进行了仿真实验,给出了仿真结果。     

车联网中网络切片资源分配方案

为了满足车联网中不同应用的服务质量(Quality of Service, QoS)要求,提出了一种基于网络切片技术的车联网频谱资源分配方案。该方案考虑数据接入控制,通过联合优化频谱资源块(Resource Block, RB)分配和车辆信号发射功率控制,在安全服务切片低时延高可靠性的约束下,最大化信息娱乐服务切片的平均和吞吐量。将车联网资源管理建模为一个混合整数随机优化问题,利用李雅普诺夫(Lyapunov)优化理论将该优化问题分解为接入控制和RB分配与功率控制两个子问题,并分别对其进行求解,得到每个时隙的接入控制和资源分配方案。仿真结果表明,所提出的资源分配方案能够有效提高信息娱乐服务切片的平均和吞吐量,并且可以通过调整引入的控制参数值来实现吞吐量和时延的动态平衡。同时,与已有方案相比,该方案具有更好的时延性能。     

超密集网络中的绿色预测资源分配

大数据分析兴起使得系统可以预测用户的移动轨迹和业务需求等信息，从而可以根据预测信息对资源进行预先分配，在满足用户需求的同时降低网络的资源消耗。相比于无干扰网络，在基站密集部署的网络中，干扰的存在使得用户数据率预测与资源分配耦合，增加了干扰网络中进行预测资源分配的复杂性。本文研究了在保证用户业务需求情况下如何最小化系统资源消耗的问题，提出了一种能够有效协调网络干扰的预测资源分配方法。仿真结果表明，本方法基于可预测的大尺度信道信息进行预测资源分配，能够在相同的用户需求下提高网络成功传输率，降低系统能量资源消耗，提高资源的频谱效率。      

Channel allocation for GPRS

Based on the GSM radio architecture, the general packet radio service (GPRS) provides users data connections with variable data rates and high bandwidth efficiency. In the GPRS service, allocation of physical channels is flexible, i.e., multiple channels can be allocated to a user. We propose four algorithms for the GPRS radio resource allocation: fixed resource allocation (FRA), dynamic resource allocation (DRA), fixed resource allocation with queue capability (FRAQ), and dynamic resource allocation with queue capability (DRAQ). We develop analytic and simulation models to evaluate the performance for these resource allocation algorithms in terms of the acceptance rate of both GPRS packet data and GSM voice calls. Our study indicates that DRAQ (queuing for both new and handoff calls) outperforms other algorithms     

Load-Balancing Based Cross-Layer Elastic Resource Allocation in Mobile Cloud

The paper proposes a hybrid mobile cloud computing system, in which mobile applications can use different resources or services in local cloud and remote public cloud such as computation, storage and bandwidth. The cross-layer load-balancing based mobile cloud resource allocation optimization is proposed. The proposed approach augments local cloud service pools with public cloud to increase the probability of meeting the service level agreements. Our problem is divided by public cloud service allocation and local cloud service allocation, which is achieved by public cloud supplier, local cloud agent and the mobile user. The system status information is used in the hybrid mobile cloud computing system such as the preferences of mobile applications, energy, server load in cloud datacenter to improve resource utilization and quality of experience of mobile user. Therefore, the system status of hybrid mobile cloud is monitored continuously. The mathematical model of the system and optimization problem is given. The system design of load-balancing based cross-layer mobile cloud resource allocation is also proposed. Through extensive experiments, this paper evaluates our algorithm and other approaches from the literature under different conditions. The results of the experiments show a performance improvement when compared to the approaches from the literature.

     

Distributed resource management in wireless sensor networks using reinforcement learning

In wireless sensor networks (WSNs), resource-constrained nodes are expected to operate in highly dynamic and often unattended environments. Hence, support for intelligent, autonomous, adaptive and distributed resource management is an essential ingredient of a middleware solution for developing scalable and dynamic WSN applications. In this article, we present a resource management framework based on a two-tier reinforcement learning scheme to enable autonomous self-learning and adaptive applications with inherent support for efficient resource management. Our design goal is to build a system with a bottom-up approach where each sensor node is responsible for its resource allocation and task selection. The first learning tier (micro-learning) allows individual sensor nodes to self-schedule their tasks by using only local information, thus enabling a timely adaptation. The second learning tier (macro-learning) governs the micro-learners by tuning their operating parameters so as to guide the system towards a global application-specific optimization goal (e.g., maximizing the network lifetime). The effectiveness of our framework is exemplified by means of a target tracking application built on top of it. Finally, the performance of our scheme is compared against other existing approaches by simulation. We show that our two-tier reinforcement learning scheme is significantly more efficient than traditional approaches to resource management while fulfilling the application requirements.     

基于无线公用窄带网络的综合远程支援系统设计与开发

介绍了一种基于无线公用窄带网络的IRSS（综合远程支援服务系统）。系统将前方外场采集的视频、语音等多媒体信息通过无线公用窄带网络传到后方中心服务器，使后方专家可以对外场提供同步支援。IRSS采用了多P2P服务单元体系结构，对数据进行了快速运动补偿和DCT（离散余弦变换），对视频编码与视频语音传输控制方式进行优化。IRSS在CDMA（码分多址）、GPRS（通用分组无线电业务）等网络上运行良好，使用便捷，数据安全，图像质量良好。     

中断概率在TD-LTE信道资源分配中的应用

TD-LTE(时分同步码分多址-长期演进)系统业务呈非对称状态,对下行数据率的要求要超过上行数据率,因而对系统信道上下行资源分配的要求更为严格。文章利用中断概率接纳控制算法对系统信道资源进行了智能动态分配,通过系统仿真证明了该策略能够灵活分配系统时隙资源。     

Quality-of-service control in GRAMS for ATM local area network

Gopher-style real-time ATM multimedia services (GRAMS) is a multimedia system designed for an ATM local area network with the server providing multimedia services to multiple users in real-time. The major goal of this system is to serve heterogeneous media requests so that every client served has satisfactory service while the network bandwidth and the host capacity are fully exploited. Therefore, a server that can determine the service rates and successfully multiplex media transmissions according to the quality-of-service (QoS) requirement of each request becomes the key component of this system. However, given the heterogeneity of QoS requirements from different types of media, it is usually very difficult to clearly specify and accurately measure the QoS for these media. Also, it is difficult to control the QoS in a distributed-multimedia environment. We propose in this paper a simple yet effective way to measure QoS in the GRAMS system. Based on the GRAMS system architecture, we use different kinds of starvation counters to measure system resource utilization and individual service quality. These counters are employed as major QoS parameters for the admission and rate-control algorithms in the GRAMS server that are under intensive evaluation and refinement. Experimental results are also presented, showing the feasibility of simultaneously providing tens of video and images transfer sessions using low-cost workstation technology. Bottlenecks for the video server are identified     

A LP-RR principle-based admission control for a mobile network

In mobile networks, the traffic fluctuation is unpredictable due to mobility and varying resource requirement of multimedia applications. Hence, it is essential to maintain traffic within the network capacity to provide service guarantees to running applications. This paper proposes an admission control (AC) scheme in a mobile cellular environment supporting hand-off and new application traffic. In the case of multimedia applications, each applications has its own distinct range of acceptable quality of service (QoS) requirements. The network provides the service by maintaining the application specified QoS range. We propose a linear programming resource reduction (LP-RR) principle for admission control by maintaining QoS guarantees to existing applications and to increase the percentage of admission to hand-off and new applications. Artificial neural networks are used to solve the linear programming problem, which facilitates in real time admission control decision in the practical systems. We present an analytical model and results for the proposed AC scheme with resource reduction principle and a simulation study of the AC for performance evaluation. The simulation results demonstrate that the proposed AC scheme performs well in terms of increasing the number of admitted applications and maintains higher percentage of resource utilization. The suggested principle also shown that it is appropriate for the fair resource allocation with improved resource utilization.     

基于在线拍卖的网络切片资源分配算法

为满足未来移动通信网络中多样化的业务需求,为用户提供定制化服务的同时提升网络经济效益,该文提出一种基于在线拍卖的网络切片资源分配算法。根据业务类型将用户的服务请求转化为相应投标信息,以最大化拍卖参与者的社会福利为目标,将切片资源分配问题建模为基于多业务的在线赢家确定问题。结合资源分配与价格更新策略,实现基于在线拍卖的资源优化配置。仿真结果表明,该算法能够在满足用户业务需求的同时,提升网络经济效益。     

Cross-layer resource allocation for real-time services in OFDM-based cognitive radio systems

The resource allocation problem on the downlink of a multiuser OFDM-based cognitive radio (CR) system is formulated using a cross-layer (MAC and PHY layers) approach with the aim of satisfying quality of service (QoS) requirements in real-time applications. The number of subchannels available to the CR system is time-varying as a result of the stochastic nature of the activities of the primary users (PUs). The MAC layer QoS requirements are dynamically converted to PHY layer rate requirements; the conversion depends on the delivery status of queued packets as well as the number of available subchannels. Simulation results show that the proposed cross-layer resource allocation algorithm can provide substantial transmit power reductions compared to existing PHY layer and MAC layer solutions designed for multiuser OFDM systems.     

Research on intelligent computing offloading model based on reputation value in mobile edge computing

Aiming at the problem of high-latency,high-energy-consumption,and low-reliability mobile caused by computing-intensive and delay-sensitive emerging mobile applications in the explosive growth of IoT smart mobile terminals in the mobile edge computing environment,an offload decision-making model where delay and energy consumption were comprehensively included,and a computing resource game allocation model based on reputation that took into account was proposed,then improved particle swarm algorithm and the method of Lagrange multipliers were used respectively to solve models.Simulation results show that the proposed method can meet the service requirements of emerging intelligent applications for low latency,low energy consumption and high reliability,and effectively implement the overall optimized allocation of computing offload resources.     

Integrated Downlink Resource Management for Multiservice WiMAX Networks

In this paper, we propose a novel downlink resource management framework for multiservice WiMAX (worldwide interoperability for microwave access) networks. Our framework consists of two major components: adaptive power allocation (APA) and call admission control (CAC). We formulate each of them as an optimization problem, where the demands of both WiMAX service providers and subscribers are taken into account. To solve the optimization problems, we develop a fairness-constrained greedy revenue algorithm for downlink APA optimization and a utility-constrained greedy approximation algorithm for downlink CAC optimization. Our simulation results show that, when combining the APA and CAC optimization methods together, the proposed resource management framework can meet the expectations of both service providers and subscribers     

Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

Cloud Computing (CC) environment presents a simplified, centralized platform or resources to usage while necessitated at minimum cost. In CC, the main processes in is the allocation of resources of web applications. However, with the increasing demands of Cloud User (CU), an efficient resource allocation technique for web applications is required. According to the request made by the user and response obtained, the cost of resources has also to be optimized. To overcome such limitations, Pearson service correlation‐based firefly resource cost optimization (PSC‐FRCO) technique is designed. Pearson service correlation‐based firefly resource cost optimization technique not only improves the performance of cost aware resource allocation but also achieves higher efficiency while rendering services in cloud computing environment for web applications. Pearson service correlation‐based firefly resource cost optimization technique initially uses Pearson service correlation in which the user‐required service is identified by correlating the available services provided by cloud owner. This helps in improving the Response Time (RT) of cloud service provisioning. Next, firefly resource cost optimization algorithm is applied to identify and allocate the cost‐optimized cloud resources to users to afford required service from the cloud server. Thus, PSC‐FRCO technique improves the Resource Utilization Efficiency (RUE) of web applications with minimal computational cost. This technique conducts experimental works on parameters such as RT, Bandwidth Utilization Rate (BUR) computational cost, Energy Consumption (EC), and RUE. Experimental analysis reveals that PSC‐FRCO technique enhances enhances RUE and lessens RT as compared to state‐of‐the‐art works.     

Resource allocation in cellular networks based on marketing preferences

As the usage of cellular phones increases wireless subscribers demand many advanced networking capabilities, especially multimedia applications with very high Quality of Service (QoS) requirements. The limited availability of radio spectrum enforces Mobile Network Operators (MNOs) to have efficient resource management strategies. The goal is to offer services that satisfy the QoS requirements of individual users while achieving an efficient utilization of network resources. This paper considers a resource allocation strategy for cellular networks to be applied during call initiation, handoff and allocation of mobile base stations. Long-term customer retention becomes a major challenge for MNOs due to severe competition in the telecommunications industry. Therefore the MNOs need to understand the customer demographics as well as the customer spending behavior in telecommunications market. Our proposed model combines the information about the customer demographics and usage behavior once the call is initiated. Our hypothesis is that using customer information together with call information yields an efficient customer-oriented resource management strategy. We have performed simulations with different real-life scenarios. Our results show that our proposed model performs better in terms of revenue increase when compared to the First-Come First-Serve based approach.     

Distributed congestion control strategy using network utility maximization theory in VANET

Cooperative vehicle safety system (CVSS) rely on periodical beacons to track neighboring vehicles.High traffic density often causes channel congestion,seriously damaging the performance of CVSS.Existing congestion control strategies aim to ensure the performance in network layer,without considering the service requirements of vehicles in different driving contexts.To solve the problem,a distributed congestion control strategy using network utility maximization (NUM) theory was proposed.First of all,the NUM model for channel resource allocation was introduced.A utility function reflecting vehicle’s safety requirements was proposed in the model.Then under the condition of fixed transmit powers,a optimization problem of channel resource allocation was proposed.Lastly,to solve the optimization problem,a distributed congestion control algorithm named utility-based rate congestion control (UBRCC) algorithm was designed,the algorithm worked out the optimal beaconing rate by updating vehicle’s congestion price,realizing the resource allocation according to vehicle’s safety requirements.Simulation results validate that UBRCC algorithm can efficiently control channel congestion,reduce transmission delay,ensure reliable data transmission and satisfies the requirements of safety applications.