异构蜂窝网络中联合功率控制的终端直通通信资源分配

针对异构蜂窝网络中终端直通（D2D）用户和小蜂窝用户复用宏蜂窝用户资源产生的干扰问题,提出一种联合功率控制的资源分配方案。首先,在满足用户信号干扰噪声比（SINR）和发射功率约束条件的前提下,根据系统干扰模型推导出每个D2D用户和小蜂窝用户复用宏蜂窝用户信道资源时的最优发射功率;其次,将用户的信道选择规划成用户和信道之间的双边匹配问题,采用延迟接受（Gale-Shapley）算法得到一个稳定的匹配解;最后,以所得的匹配解为初始条件,通过交换搜索算法进一步优化分配方案。仿真结果表明,该方案的系统总容量和能量效率分别是最优解的93.62%和92.14%,与随机资源分配方案、无功率控制和交换搜索的分配方案,以及有功率控制无交换搜索的分配方案相比,系统容量平均增幅分别为48.29%、15.97%和4.8%,系统能量效率平均增幅分别为62.72%、44.48%和4.45%。该方案能够达到近似最优的系统总容量,有效提高频率利用率和能量效率。     

Coverage probability driven dynamic spectrum allocation in heterogeneous wireless networks

In this paper,we design a dynamic spectrum allocation(DSA) scheme for heterogeneous cellular wireless networks with special interest on guaranteeing the cell coverage probability.To this end,considering users spatial distribution,we propose a new interference control(IC) model,which guarantees SINR(signal to interference plus noise ratio) requirements of different services and ensures the coverage performance of base stations(BSs).Under such an IC model,we formulate the DSA scheme as a combinatorial optimization problem.Since the problem is computationally intractable,we design an algorithm for its solution based on graph coloring.Simulation results indicate that the proposed DSA scheme can increase the total spectrum utility while effectively controlling the interference among BSs and meeting SINR requirements of users.     

异构网络中基于鸽群优化算法的D2D资源分配机制

针对异构蜂窝网络中D2D(device-to-device)通信用户复用蜂窝用户上行信道产生的频谱资源分配优化问题,提出一种基于改进离散鸽群算法(PIO)的D2D通信资源分配机制.通过设置信干噪比(SINR)门限值保证用户的通信服务质量(QoS),使用基于改进地图-指南针算子和认知因子的离散鸽群算法(IMCBPIO)为D2D用户进行资源分配,并采用基于接收SINR的闭环功率控制算法动态调整用户的发送功率,以减少用户与基站以及用户与用户之间存在的干扰.仿真结果表明,所提出方案能够有效抑制异构网络中由于引入D2D用户后导致的干扰,降低通信用户的中断概率,大大提高频谱利用率和系统吞吐量.     

Multicell power allocation method based on game theory for inter-cell interference coordination

As a new technology, coordinated multipoint (CoMP) transmission is included in LTE-Advanced study item. Moreover, the network architecture in LTE-Advanced system is modified to take into account coordinated transmission. Under this background, a novel power allocation game model is established to mitigate inter-cell interference with cellular coordination. In the light of cellular cooperation relationship and centralized control in eNodeB, the power allocation in each served antenna unit aims to make signal to interference plus noise ratio (SINR) balanced among inter-cells. Through the proposed power allocation game algorithm, the users’ SINR can reach the Nash equilibrium, making it feasible to reduce the co-frequency interference by decreasing the transmitted power. Numerical results show that the proposed power allocation algorithm improves the throughput both in cell-center and cell-edge. Moreover, the blocking rate in cell-edge is reduced too.     

A new approach to dynamic bandwidth allocation in Quality of Service networks: Performance and bounds

Efficient dynamic resource provisioning algorithms are necessary to the development and automation of Quality of Service (QoS) networks. The main goal of these algorithms is to offer services that satisfy the QoS requirements of individual users while guaranteeing at the same time an efficient utilization of network resources.In this paper we introduce a new service model that provides per-flow bandwidth guarantees, where users subscribe for a guaranteed rate; moreover, the network periodically individuates unused bandwidth and proposes short-term contracts where extra-bandwidth is allocated and guaranteed exclusively to users who can exploit it to transmit at a rate higher than their subscribed rate.To implement this service model we propose a dynamic provisioning architecture for intra-domain Quality of Service networks. We develop a set of dynamic on-line bandwidth allocation algorithms that take explicitly into account traffic statistics and users’ utility functions to increase users’ benefit and network revenue.Further, we propose a mathematical formulation of the extra-bandwidth allocation problem that maximizes network revenue. The solution of this model allows to obtain an upper bound on the performance achievable by any on-line bandwidth allocation algorithm.We demonstrate through simulation in realistic network scenarios that the proposed dynamic allocation algorithms are superior to static provisioning in providing resource allocation both in terms of total accepted load and network revenue, and they approach, in several network scenarios, the ideal performance provided by the mathematical model.     

基于多对一Gale-Shapley算法的D2D通信资源分配

针对D2D通信复用异构蜂窝网络上行信道产生的干扰问题和频谱资源优化问题进行研究,提出一种基于多对一Gale-Shapley算法的D2D通信资源分配方案。本方案允许多个D2D用户共享一个蜂窝用户信道资源,通过设置信干噪比（SINR）门限保证用户的通信服务质量（QOS）。根据信道分配情况,构建D2D用户和信道的偏好列表,最大化系统总容量。仿真结果表明,该方案收敛较快,复杂度较低,能够有效保证用户的通信服务质量,系统总容量接近最优解。本研究为实现D2D用户和蜂窝用户的频谱资源共享,提高频谱利用率提供了一种有效方案。     

面向多租户网络资源分配的博弈优化策略

为了应对5G及未来网络中用户间差异化的服务需求,改善多租户网络切片资源利用率低和部署成本高的问题,提出一种基于多租户网络资源分配的博弈优化策略。在多租户网络中,网络切片租户（NSTs）租用基础设施提供商基站的无线频谱资源,将接入服务切片构建为网络切片即服务,为用户提供网络接入服务。将NSTs和用户的关系建模为一个多主多从的Stackelberg博弈,引入切片流行度和服务命中率指标,建立博弈双方的策略空间和收益函数,并证明NSTs的切片订购策略存在唯一的纳什均衡。通过逆向归纳法分析博弈模型,提出一种分布式迭代算法求得用户的最优吞吐量需求以及NSTs的最优切片定价。仿真结果表明,与传统考虑切片资源分配的优化策略对比,基于多租户网络资源分配的博弈优化策略能够有效提高资源利用率和用户满意度,并降低切片部署能耗,较好地实现频谱带宽资源的合理分配。     

多用户MIMO系统中机会波束截断的信道反演

针对下行链路多用户MIMO系统,提出了一种简单的基于机会波束截断的信道反演方法。机会波束形成可以用最小的反馈获得MIMO系统慢衰落信道中下行链路的多用户分集增益和复用增益。在机会波束形成进行自适应信道截断的基础上,使各通信用户的信干噪比（SINR）相同,改善了系统的误比特率性能。仿真结果表明,总的用户数为30时,系统误比特率性能提高3~4 dB。信道反演比注水功率分配简单,降低了系统复杂度。     

基于随机流模型的校园网格存储服务分析

校园网格存储服务是汇聚校园高性能计算中心、各个院系或研究所的服务器以及单个PC机贡献的存储资源,建立三级存储架构,为用户提供数据存储的注册、分配、调度、释放服务。由于节点的动态性,导致所提供的存储空间总数动态变化,为研究存储服务性能,采用随机流模型进行分析。它是一种宏观上对高速网络进行有效分析的模型,是基于排队论模型分析的一种替代方法。通过该模型分析校园网格存储服务性能,得出节点请求分配概率随节点数目变化的关系。     

D2D通信网络中一种基于时间反演的无线资源优化机制

针对D2D(Device-to-Device)异构无线通信网络中D2D用户和蜂窝用户之间的干扰问题,提出一种基于时间反演的无线资源优化机制。该机制包括两个步骤:1)在上行传输系统中结合时间反演镜技术实现干扰消除,即对每个用户执行信道签名,提取有用信号,剔除干扰,获取系统用户信干噪比;2)根据用户信干噪比,采用功率控制算法并结合凸优化理论来调整用户的发射功率,以最大系统吞吐量为优化目标。仿真结果表明,该机制有效地抑制了D2D异构无线通信网络中蜂窝用户和D2D用户的相互干扰,提升了系统容量,同时满足了用户对通信可靠性的需求,保证了用户能够获得更高的QoS(Quality of Service)。     

ROPAS: Cross-Layer Cognitive Architecture for Mobile UWB Networks

The allocation of bandwidth to unlicensed users, without significantly increasing the interference on the existing licensed users, is a challenge for Ultra Wideband （UWB） networks. Our research work presents a novel Rake Optimization and Power Aware Scheduling （ROPAS） architecture for UWB networks. Since UWB communication is rich in multipath effects, a Rake receiver is used for path diversity. Our idea of developing an optimized Rake receiver in our ROPAS architecture stems from the intention of reducing the computation complexity in terms of the number of multiplications and additions needed for the weight derivation attached to each finger of the Rake receiver. Our proposed work uses the Cognitive Radio （CR） for dynamic channel allocation among the requesting users while limiting the average power transmitted in each sub-band. In our proposed novel ROPAS architecture, dynamic channel allocation is achieved by a CR-based cross-layer design between the PHY and Medium Access Control （MAC） layers. Additionally, the maximum number of parallel transmissions within a frame interval is formulated as an optimization problem. This optimal decision is based on the distance parameter between a transmitter-receiver pair, bit error rate and frequency of request by a particular application. Moreover, the optimization problem improvises a differentiation technique among the requesting applications by incorporating priority levels among user applications. This provides fairness and higher throughput among services with varying power constraint and data rates required for a UWB network.     

基于和声算法异构Hadoop集群资源分配优化

异构Map-Reduce环境中资源分配策略直接影响其响应时间,如何利用有效的策略将计算任务分配到计算资源是亟待解决的问题。利用和声搜索算法对异构Hadoop集群中的计算资源分配问题进行优化。对问题进行建模时考虑了异构计算机集群中各节点的处理能力、带宽和线路质量和源数据位置等因素对计算资源分配的影响,利用和声搜索算法优化资源分配策略,以期在满足用户需求的前提下提高系统的响应时间。并用Gridsim对算法进行仿真实验,实验结果表明利用和声搜索算法可以达到减少系统响应时间的目的。     

基于异构服务的水资源遥感监测平台设计与实现

遥感监测是获取水资源信息的重要手段,但是水资源遥感监测数据的多源化及算法和服务异构化的特点,使得数据及应用共享不足等问题日渐突出.为解决数据和算法的多源集成管理问题,研究建立基于异构服务的水资源遥感监测平台,以充分利用各类水资源数据及服务.主要通过KVM实现底层资源虚拟化,为数据体量及异构等问题提供解决思路;通过分布式...     

基于云服务的计算化学社区

中国科学院超算环境是由中国科学院统筹规划建设的“院总中心-分中心-所级中心”三层结构的超级计算环境,环境的资源聚合使用网格中间件 SCE,并使用 API 接口对外提供服务。依托超算环境建设的科学计算学云服务社区采用软件即服务的模式,使用浏览器/服务器结构来提供专业交互式的计算化学科学计算服务,以“降低使用门槛、提高使用效率,助跑计算化学用户的科学研究工作”为目标,为广大科研用户提供“一站式”计算的 Web 服务模式和异构资源协同工作模式。本文同时介绍了社区的建设应用情况以及环境的运行、监控情况。     

面向大规模云资源调度的可扩展分布式调度方法

云数据中心异构物理服务器的能耗优化资源分配问题是NP难的组合优化问题,当资源分配问题规模较大时,求解的空间比较大,很难在合理时间内求得最优解。基于分而治之的思想,从调度模式方面提出可扩展分布式调度方法,即当云数据中心待调度的物理服务器的数量比较大时,将待调度的服务器划分为若干个服务器集群,然后在每个服务器集群建立能耗优化的资源分配模型,并利用约束编程框架Choco求解模型,获得能耗最优的资源分配方式。将提出的基于可扩展分布式调度方法的能耗优化云资源调度算法与非扩展调度算法进行实验比较,实验结果表明,提出的基于可扩展分布式调度方法的能耗优化云资源调度算法在大规模云资源分配上有明显的性能优势。     

异构云系统中基于智能优化算法的多维资源公平分配

资源分配策略的研究一直是云计算领域研究的热点和难点,针对异构云计算环境下多维资源的公平分配问题,结合基因算法（GA）和差分进化算法（DE）,分别给出了两种兼顾分配公平性和效率的资源分配策略,改进了解矩阵表达式使异构云系统中的主资源公平分配（DRFH）模型转化成为整数线性规划（ILP）模型,并提出了基于最大任务数匹配值（MTM）的初始解产生机制和使不可行解转化为可行解的修正操作,以此提高算法的收敛速度,使其能够快速有效地得到最优分配方案。实验结果表明,基于GA和DE算法的多维资源公平分配策略可以得到近似最优解,在最大化最小主资源份额目标值和资源利用率方面明显优于Best-Fit DRFH和Distributed-DRFH,而且针对不同任务类型的资源需求,具有较强的自适应能力。     

Optimization-based resource allocation for software as a service application in cloud computing

Software as a service (SaaS) is a software that is developed and hosted by the SaaS vendor. SaaS cloud provides software as services to the users through the internet. To provide good quality of service for the user, the SaaS relies on the resources leased from infrastructure as a service cloud providers. As the SaaS services rapidly expand their application scopes, it is important to optimize resource allocation in SaaS cloud. The paper presents optimization-based resource allocation approach for software as a service application in cloud. The paper uses optimization decomposition approach to solve cloud resource allocation for satisfying the cloud user’s needs and the profits of the cloud providers. The paper also proposes a SaaS cloud resource allocation algorithm. The experiments are designed to compare the performance of the proposed algorithm with other two related algorithms.     

Workflow performance improvement using model-based scheduling over multiple clusters and clouds

In recent years, a variety of computational sites and resources have emerged, and users often have access to multiple resources that are distributed. These sites are heterogeneous in nature and performance of different tasks in a workflow varies from one site to another. Additionally, users typically have a limited resource allocation at each site capped by administrative policies. In such cases, judicious scheduling strategy is required in order to map tasks in the workflow to resources so that the workload is balanced among sites and the overhead is minimized in data transfer. Most existing systems either run the entire workflow in a single site or use naïve approaches to distribute the tasks across sites or leave it to the user to optimize the allocation of tasks to distributed resources. This results in a significant loss in productivity. We propose a multi-site workflow scheduling technique that uses performance models to predict the execution time on resources and dynamic probes to identify the achievable network throughput between sites. We evaluate our approach using real world applications using the Swift parallel and distributed execution framework. We use two distinct computational environments-geographically distributed multiple clusters and multiple clouds. We show that our approach improves the resource utilization and reduces execution time when compared to the default schedule.     

基于多智能体Q学习的异构车载网络选择方法

异构车载网络环境下如何选择接入网络对于车载终端用户的服务体验而言至关重要,目前基于Q学习的网络选择方法利用智能体与环境的交互来迭代学习网络选择策略,从而实现较优的网络资源分配.然而该类方法通常存在状态空间过大引起迭代效率低下和收敛速度较慢的问题,同时由于Q值表更新产生的过高估计现象容易导致网络资源利用不均衡.针对上述问...     

一种全IP宽带移动P2P网络结构及性能分析

提出一种全IP宽带移动P2P网络结构,通过在数据网关上增加分布式用户服务器实现异构网络的互联,形成了虚拟的二维覆盖网络,采用IMS为核心控制网络,基于此结构可开展各种移动P2P业务。以资源共享流程为例,对比分析了该结构和基于IMS的P2P网络结构的差异,结果表明该网络结构在具有更小的系统开销。