OFDM中继系统的传输策略选择与资源分配

提出传输策略选择结合资源分配的迭代算法,证明其收敛性。进一步将算法简化为顺序进行的三步：子信道指配,传输策略选择和中继的发射功率分配,源的发射功率分配。仿真结果表明,所提资源分配迭代方案和简化方案的频谱效率性能均优于已有的两种资源分配方案,简化方案更适合实际应用。     

计算网格资源分配研究

计算网格是一种新的技术,有很多内容都刚开始研究发展,还不成熟。由于网格资源的异构与时变,网格资源用户的不同需求,使网格资源分配成为一个重要问题。因而,网格资源分配技术也成为一种关键技术。给出了基于代理的网格资源分配策略和资源代理的迭代算法,从而实现了资源的协调分配。     

一种基于均衡理论的网格资源分配模型

针对网格资源特点提出一种基于市场经济均衡理论的网格资源分配模型,将整个网络资源系统看成是一个价格随供求关系浮动的竞争市场,网络用户购买网络资源以满足个人的服务质量(QoS),利用流量控制技术优化网络资源分配,使之公平有效。借助经济学中的均衡理论对网格参与者之间的交互行为进行分析,并结合迭代算法给出初步仿真试验的分析结果。     

LTE系统自适应软频率复用技术研究

提高小区边缘用户性能是蜂窝移动通信系统的经典难题.下一代蜂窝移动通信系统3GPP LTE通过有效的小区间干扰协调以及频率资源分配来改善小区边缘用户性能.提出一种自适应软频率复用算法,根据系统负载情况自适应地为各小区分配主、副载波及其发射功率,在实现系统吞吐量优化的同时保证小区中心和边缘区域速率需求.算法首先通过穷尽搜索和贪婪递减策略,获得单小区最优资源分配,然后在不同小区间迭代执行单小区算法直到系统吞吐量不变为止.仿真结果表明,算法通过多次迭代后,系统吞吐量保持不变并输出一种优化的资源分配方案.与同类频率分配算法相比,可以有效提升小区边缘用户的吞吐量,同时获得更高的系统容量,更适用于高速率的LTE系统.     

虚拟机资源概率配置的云计算SEFFD算法

摘要：为增强虚拟机资源分配过程性能,有效解决云计算环境下虚拟资源分配的NP hard问题,利用模拟进化算法结合首次下降算法构建虚拟资源分配优化过程（SEFFD）。首先,构建全新的虚拟资源分配的评估方式,并结合模拟进化过程较强的算法寻优爬坡效果,采用迭代方式实现虚拟资源分配过程的个体选择、评估以及排序进化;其次,以模拟进化（SE）过程所获得资源分配结果为基础,结合首次下降（FFD）算法准则,实现物理主机及虚拟机资源的二次分配,从而获得资源分配效果和效率的同步提升;最后,利用CloundSim及Gridbus云计算仿真平台对算法性能进行对比测试,实验结果表明所提策略的内存利用率高于60%,处理器利用率大于55%,可有效减少所需物理主机数量,从而降低能耗。     

动态主用户流量模型的跨层优化设计

为适应主用户流量变化较快的场景,在不完美频谱感知的情况下最大化认知用户的吞吐量,提出了一种基于集中式Overlay认知无线网络中感知时间与资源分配跨层优化算法。将优化目标分解为信道分配和检测时间同功率分配联合优化两个子问题,通过子算法迭代,最终得到感知时间与资源分配的联合最优解。仿真结果表明,相对于仅考虑频谱感知或资源分配的单层优化算法,该算法可在兼顾公平的前提下使次用户吞吐量得到有效提升。     

蜂窝与D2D混合网络中干扰协调算法研究

在蜂窝与D2D混合网络中,D2D用户复用蜂窝用户的资源会给混合网络带来不能避免的干扰,这些干扰严重影响了系统性能。文章在资源分配与功率控制研究的基础上,提出一种基于联合资源分配与功率控制的干扰协调方案。资源分配时,首先对用户进行分簇,分簇的结果是使簇与簇间的总干扰最大,然后以最大化系统容量为目标,采用一种迭代分配方案,对已经分好的簇进行资源块的划分。功率控制时,根据预设的干扰门限值,以及D2D用户与蜂窝用户、基站之间的信道增益估计值,对D2D用户进行动态的功率调整。通过仿真验证该算法能够提高混合网络的总吞吐量。     

基于传播属性的社交网络重要节点发现

节点重要性排序在复杂网络领域中有着广泛的应用。基于节点传播属性的迭代资源分配改进算法（improved iterative resource allocation,IIRA）通过引入节点传播属性,提升了节点重要性排序的准确性,但该算法并未考虑节点相似性对节点资源分配的影响,存在局限性。针对其不足,提出了一种以节点相似性为输入指标的资源分配算法（similarity-based resource allocation,SBRA）,使得资源分配策略更加符合真实的社交网络;在SBRA算法的基础上借鉴LeaderRank算法中背景节点的思想,引入高阶邻居节点间的资源流动,提出了一种基于节点相似度和高阶流动资源分配算法（LeaderRank similarity-based resource allocation,L-SBRA）;基于传播动力学的SIR模型,通过各算法之间的对比实验,验证了相似性作为资源分配依据以及引入背景节点的合理性,并且证明了改进算法的有效性和优越性。     

一种基于市场机制的计算网格资源分配方法

针对计算网格提出了一种基于市场机制的资源分配方法，以一般均衡理论为基础，依靠市场机制，实现计算网格资源的优化分配，首先，描述了基于代理的资源分配框架，它包括3个 层次：资源层、代理层和用户层；接着，给出了计算网络资源分配的市场模型，其中效用函数用于刻画用户对给定资源的满意程度；然后定义了市场模型的均衡状态并证明了均衡状态撮优性，这意味着在均衡状态下资源分配不仅有效而且公平，最后引入了资源代理的迭代算法。     

D2D通信中联合资源分配与功率控制算法

终端直通(D2D)技术引入移动蜂窝网络虽然能够提高蜂窝系统性能,但却带来了很大的干扰和能量消耗.为了降低干扰,提升频谱效率,同时又兼顾能量效率,提出了一种联合资源分配和功率控制的方法,用以实现高能效的D2D通信.仿真结果表明:本文提出的迭代资源分配和功率控制方案,相比已有方案能量效率有了明显提高.     

面向运行时间预测和容错感知的网格资源分配

提出与描述了一种面向任务运行时间预测和容错感知（Fault-Aware）的网格资源分配策略,采用主动容错的方式,在资源出错之前尽量提前避免它出错或异常的情况发生。该策略把网格中任务的运行时间（runtime）预测和资源的在线时间（uptime）预测结合起来,相对于普通的调度策略具有比较高的资源利用率。在具体的CoBRA网格中间件中实现了该容错感知调度,描述了实现该容错感知调度策略模块的功能。测试过程中选择了睡眠任务技术,划分四种不同的场景进行实验,把该容错感知资源分配与普通的FCFS调度策略进行比较,结果证明在可变化的资源可用性的情况下系统可以加快应用的整体执行时间,具有很小的偏差。     

基于禁忌搜索的流式计算平台负载均衡策略

针对大数据流式计算平台原生的调度机制存在计算负载分配不均衡、资源利用率低的问题,提出异构环境下基于禁忌搜索算法的负载均衡策略,并将其应用于Apache Flink平台。首先,通过构建作业拓扑模型将流式计算作业的拓扑结构抽象为有向无环图（directed acyclic graph,DAG）,并将每个任务槽（task slot）抽象为节点,为计算节点的性能评估奠定基础;其次,通过建立性能评估模型将有向无环图中带性能权值的节点导入性能评估模型,进行归一化处理得到节点性能的优劣;再将评估参数传入禁忌调度算法（tabu search for schedule,TBS）进行作业路径优化,从而得出最优作业路径;最后,使用Flink平台提供的CustomPatitionerWrapper接口将数据分配到最优作业路径包含的节点中,完成计算负载的均衡分配,从而提升Flink平台的整体性能。实验结果表明：通过禁忌调度算法优化后的负载均衡策略与原生的Flink平台相比,平均计算延迟降低了10~20 ms,资源利用率显著提高,平均吞吐量提升约15%,有效证明了负载均衡策略的有效性和优化效果。     

A Game Theory-Based Pricing Strategy to Support Single/Multiclass Job Allocation Schemes for Bandwidth-Constrained Distributed Computing Systems

Today's distributed computing systems incorporate different types of nodes with varied bandwidth constraints which should be considered while designing cost-optimal job allocation schemes for better system performance. In this paper, we propose a fair pricing strategy for job allocation in bandwidth-constrained distributed systems. The strategy formulates an incomplete information, alternating-offers bargaining game on two variables, such as price per unit resource and percentage of bandwidth allocated, for both single and multiclass jobs at each node. We present a cost-optimal job allocation scheme for single-class jobs that involve communication delay and, hence, the link bandwidth. For fast and adaptive allocation of multiclass jobs, we describe three efficient heuristics and compare them under different network scenarios. The results show that the proposed algorithms are comparable to existing job allocation schemes in terms of the expected system response time over all jobs     

QAAS: quick accurate auto-scaling for streaming processing

In recent years, the demand for real-time data processing has been increasing, and various stream processing systems have emerged. When the amount of data input to the stream processing system fluctuates, the computing resources required by the stream processing job will also change. The resources used by stream processing jobs need to be adjusted according to load changes, avoiding the waste of computing resources. At present, existing works adjust stream processing jobs based on the assumption that there is a linear relationship between the operator parallelism and operator resource consumption (e.g., throughput), which makes a significant deviation when the operator parallelism increases. This paper proposes a nonlinear model to represent operator performance. We divide the operator performance into three stages, the Non-competition stage, the Non-full competition stage, and the Full competition stage. Using our proposed performance model, given the parallelism of the operator, we can accurately predict the CPU utilization and operator throughput. Evaluated with actual experiments, the prediction error of our model is below 5%. We also propose a quick accurate auto-scaling (QAAS) method that uses the operator performance model to implement the auto-scaling of the operator parallelism of the Flink job. Compared to previous work, QAAS is able to maintain stable job performance under load changes, minimizing the number of job adjustments and reducing data backlogs by 50%.     

Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directions

Nowadays, high-performance computing (HPC) clusters are increasingly popular. Large volumes of job logs recording many years of operation traces have been accumulated. In the same time, the HPC cloud makes it possible to access HPC services remotely. For executing applications, both HPC end-users and cloud users need to request specific resources for different workloads by themselves. As users are usually not familiar with the hardware details and software layers, as well as the performance behavior of the underlying HPC systems. It is hard for them to select optimal resource configurations in terms of performance, cost, and energy efficiency. Hence, how to provide on-demand services with intelligent resource allocation is a critical issue in the HPC community. Prediction of job characteristics plays a key role for intelligent resource allocation. This paper presents a survey of the existing work and future directions for prediction of job characteristics for intelligent resource allocation in HPC systems. We first review the existing techniques in obtaining performance and energy consumption data of jobs. Then we survey the techniques for single-objective oriented predictions on runtime, queue time, power and energy consumption, cost and optimal resource configuration for input jobs, as well as multi-objective oriented predictions. We conclude after discussing future trends, research challenges and possible solutions towards intelligent resource allocation in HPC systems.     

Experiences from integrating algorithmic and systemic load balancing strategies

Load balancing increases the efficient use of existing resources for parallel and distributed applications. At a coarse level of granularity, advances in runtime systems for parallel programs have been proposed in order to control available resources as efficiently as possible by utilizing idle resources and using task migration. Simultaneously, at a finer granularity level, advances in algorithmic strategies for dynamically balancing computational loads by data redistribution have been proposed in order to respond to variations in processor performance during the execution of a given parallel application. Combining strategies from each level of granularity can result in a system which delivers advantages of both. The resulting integration is systemic in nature and transfers the responsibility of efficient resource utilization from the application programmer to the runtime system. This paper presents the design and implementation of a system that combines an algorithmic fine-grained data parallel load balancing strategy with a systemic coarse-grained task-parallel load balancing strategy, and reports on recent experimental results of running a computationally intensive scientific application under this integrated system. The experimental results indicate that a distributed runtime environment which combines both task and data migration can provide performance advantages with little overhead. It also presents proposals for performance enhancements of the implementation, as well as future explorations for effective resource management. Copyright © 2001 John Wiley & Sons, Ltd.     

On construction of a well-balanced allocation strategy for heterogeneous multi-cluster computing environments

With the rapid increment of the heterogeneity of hardware devices, cluster computing has to encounter the problem of handling heterogeneous resources for exploiting the utilization of system resources. This paper introduces a new job allocation strategy based on multi-clusters in diskless environments. By adopting Ganglia as the resource monitor and Condor as the queue system, a heterogeneous multi-cluster system is also constructed with and without storage devices for evaluating the system performance. The proposed algorithm is called the Well-Balanced Allocation Strategy (WBAS) in which the scheduler dispatches MPI-based jobs to appropriate resources across multi-clusters. The strategy focuses on dispatching jobs to nodes with similar performance, thus equalizing execution times among all the required nodes. The WBAS is implemented on the constructed heterogeneous multi-cluster system to evaluate the performance of the scheduling strategy. The experimental results show that the proposed strategy performs well and could efficiently improve the system performance.     

用爬山法实现无中心式网格调度

为方便网格资源的扩展,网格调度应当是无中心的.为在尽可能多的计算资源中为单地点作业优化资源选择,这里采用了爬山算法.当一个网格调度器收到一个单地点作业,爬山法被激活,根据网格调度器之间的相邻关系为作业找出最适合的计算系统,这里每个计算系统的适合度用预测的作业响应时间表示.实验模拟了无中心式网格调度与计算系统之间的性能差别,每个计算系统的本地调度采用保守式装填法,网格工作负荷由模型得到,并用一段工作负荷的平均响应时间衡量调度性能.实验结果表明,即使在作业提交点分布不均匀且运行时间估计不准确情况下,爬山法仍可有效改善单地点作业的调度.     

基于异构Flink集群的节点优先级调度策略

Flink流处理系统默认的任务调度策略在一定程度上忽略了集群异构和节点可用资源,导致集群整体负载不均衡。研究分布式节点的实时性能和集群作业环境,根据实际作业环境的异构分布情况,设计结合异构Flink集群的节点优先级调整方法,以基于Ganglia可扩展分布式集群资源监控系统的集群信息为依据,动态调整适应当前作业环境的节点优先级指数。基于此提出Flink节点动态自适应调度策略,通过实时监测节点的异构状况,并在任务执行过程中根据实时作业环境更新节点优先级指数,为系统任务找到最佳的执行节点完成任务分配。实验结果表明,相比于Flink默认的任务调度策略,基于节点优先级调整方法的自适应调度策略在WorldCount基准测试中的运行时间约平均减少6%,可使异构Flink集群在保持集群低延迟的同时,节点资源利用率和任务执行效率更高。     

一种支持服务网格的动态负载平衡系统

为了实现服务网格系统内负载的均衡分布,提高资源利用率和系统的吞吐率,设计并实现了一种基于服务网格环境的动态负载平衡系统。提出了层次式负载平衡调度模式,给出了本系统结构形式,设计并实现了一种综合考虑各局部代理作业数和各个局部代理性能以及当前的负载情况的动态双阈值作业分配算法。实验结果表明,此算法能有效地基于负载分派作业,达到了提高网格内分布资源的利用率和减少作业调度时间的目的。