一个具有随机丢弃分组机制的GI/M/1/N排队系统

由于标准GI/M/1/N排队系统仅当等待队列中没有空闲位置时才丢弃到达的分组,不适合为QoS控制中的缓存队列管理建模。利用随机过程中输入流稀疏化的方法,在标准的GI/M/1/N排队系统中嵌入随机丢弃分组的机制,建立了一个具有随机丢弃分组机制的扩充的GI/M/1/N排队系统,以及该排队系统的分组丢失率、系统利用率、队列长度的均值/方差、平均等待时间等性能评价指标。     

一种新的排队系统在CDMA20001X数据业务吞吐量优化中的应用——M／M／m变速率自适应优先服务排队系统

文章通过对cDMA20001×数据吞吐量相关理论的研究,综合了基于用户数呼叫准入控制与基于干扰呼叫准入控制的优点,对M／M／m排队系统进行了修正,提出了新的M／M／m变速率自适应优先服务排队系统（M／M／m／VRAP）模型,并从模型的基本原理和系统性能两方面进行了详细分析。该模型可大大提高CDMA20001X数据业务物理层的吞吐量。     

基于M/M/1的无线mesh网络网关分析模型

根据排队论M/M/1模型的特性以及无线mesh网络中顾客请求服务的生成特点,构建了基于M/M/1模型的无线mesh网络网关队列模型,该模型将请求服务中原来的无序状态集转变为有序状态集,在网关节点前进行排队,网关作为单一的服务台服务于整个集合;利用数学方法对模型进行求解,即推导出网络系统的关键参量和目标分析函数.通过验证实验,该模型可以求得网络中非饱和资源分配状态下的一些关键参量值.理论分析和实验结果都说明了新模型是合理、有效的.     

基于OPNET的M/M/m队列仿真

M/M/m队列是排队论中的一个服务系统模型.OPNET是一个使用非常广泛的仿真工具.通过OPNET采集的两个统计量:数据包排队延时和队列长度平均时间,可对M/M/m队列模型进行仿真.通过仿真,得出稳态下数据包平均延时Ws和队列长度平均时间Ls随平均间隔时间的增加而降低;增加服务器m的数量,Ws和Ls都快速减少,并且Ws和Ls与平均数据包长度以及服务容量有关.     

基于多优先级探测的准入控制实现端到端服务质量保证的研究

本文针对传统的基于单优先级探测的准入控制存在的缺陷,提出了一种可扩展性好的基于多优先级探测的准入控制方法.它可以根据流的不同服务质量需求,为单个流提供多种不同优先级别的端到端的服务质量保证.本文详细论述了它的原理与实现.为了降低探测流冲突对准入控制的性能的影响,本文提出了在路由器中设定可变的竞争探测流数目上限的方法来降低探测流冲突,既提高了资源利用率又保证了较高的准入成功率.最后,本文通过NS模拟验证了多优先级准入控制的优越性.     

无线传感器网络中基于拥塞程度分级的速率调节机制

文中提出一种基于拥塞程度分级的速率调节算法.首先,对缓冲区进行多尺度排队分析,计算出缓冲区的溢出概率.其次,根据溢出概率的值,把节点拥塞程度分成三级.最后,针对每一级拥塞采取相应的速率调节方案来缓解拥塞.实验结果表明,该算法可以有效缓解拥塞,提高无线传感器网络的数据包投递率.     

M/M/C/m/m排队系统模型的飞机战伤抢修研究

抢修力量配置是飞机战伤抢修复杂系统的核心因素.损伤飞机在抢修系统中的停留时间是进行抢修力量配置的关键指标.首先用排队论的观点,从飞机战伤抢修系统的特性出发,结合对系统榆入过程的分析,得出抢修力量预测模型即M/M/C/m/m排队模型.然后通过对该排队系统状态转移方程求解,计算出损伤飞机在系统停留时间.最后通过实例分析,得出对战伤抢修力量配置的方法.     

清除阻塞的M/P/C/C 排队系统仿真

对清除阻塞型M／P／C／C排队系统进行了大量的仿真研究,结果表明在平均到达率及平均服务时间分别对应相同的情况下,其呼叫损失概率与爱尔兰-B公式符合得很好。这一结论对通信网络规划过程中考虑业务流的自相似性有重要意义。     

无线/移动网络中自适应的接纳控制算法及性能分析

无线／移动网络中重要的连接级QoS性能指标包括新连接请求阻塞率(CBP)、切换连接请求丢弃率(HDP)等。其中，更不希望因切换连接请求的丢弃而导致服务的终止。为降低HDP，通常采用资源预留方案。但这种方案导致CBP较高、资源利用率低。本文针对自适应的多媒体应用带宽可以动态调整的特点，研究无线／移动网络中多优先级服务自适应的接纳控制机制，提出一个自适应的接纳控制算法，对其QoS性能进行分析。     

网格环境中可靠性增强的资源预留策略

提出一种基于随机服务模型的可靠性量化计算方法,该策略采用随机服务系统描述网格资源的预留服务模型,通过分析预留请求的统计特征来评估接纳当前预留请求不发生违约的概率,然后依据备选资源集合中的违约概率来进行预留请求接纳和任务资源选择,从而提高资源预留时的可靠性。实验表明,与传统的预留策略相比,该策略在提高资源利用率、降低违约率以及对动态负载压力变化情况下的反应方面,表现出更好的有效性、适应性。     

A measurement-based prioritization scheme for handovers in mobilecellular networks

A method of improving the quality of service in mobile cellular systems based on prioritization of handover requests is presented. The objective is to improve perceived quality of cellular service by minimizing both the probability of forced termination of ongoing calls due to handover failures and the degradation in spectrum utilization. A model based on a multiple-priority nonpreemptive queuing discipline is developed. New calls are blocked if all channels are occupied. Handover requests are queued such that as soon as a channel is available, it is offered to the mobile subscriber with the measurement results closest to the minimum acceptable power level for communication. Service rate is given by channel occupancy time distribution and is assumed to be exponential. The performance of a cellular system employing the proposed handover policy is evaluated analytically and by simulation, and results are compared to those obtained when the cellular system employs nonprioritized call handling and first-in/first-out queuing discipline. This provides lower probability of forced termination and less call blocking, less reduction in traffic, and less delay     

基于开源的消息队列云服务研究

随着互联网应用软件规模的不断增长,分布式架构成为了应用开发的主流模式,而消息队列解决了分布式应用之间的通信难题,开源的推动使得消息队列成为了快速发展的热点技术.同时,云计算经过十余年的发展,逐渐从提供IT基础设施向应用开发渗透,国内主流的云服务商基于开源的消息队列技术推出了各自的消息队列云服务,如何评估这些云产品的功能和性能,给用户提供客观准确的参考,从而推动云计算市场积极发展,是当前亟待解决的重要问题.首先介绍了当前主流的开源消息队列产品及其特点,然后分析研究了云服务化的消息队列产品的特征,最后分析了消息队列云服务的性能测试指标,为企业及个人用户选型提供了参考意见.     

Modeling of Single-Step Power Control Scheme in Finite-State Markov Channel and Its Impact on Queuing Performance

In this paper, we analyze the queuing performance in the finite-state Markov channel (FSMC) when the single-step power control (SSPC) scheme is adopted. To start with, an SSPC model and its extended power control error (PCE) model are proposed and described in detail. Such models can emulate the behaviors of the interaction between the SSPC and an FSMC and the variation of the PCE with high accuracy. Furthermore, the packet error rate that has been recently analyzed by Lee and Chang is adopted to evaluate the service rate, the time average queuing length, and the probability mass function (PMF) of the queuing length variation. Based on these results, a queuing variation model is proposed to emulate the queuing variation in the FSMC. The flooring phenomenon of the queuing length is observed under a particular requirement of an overflow probability when the SNR value is greater than a certain value. This indicates that the tradeoff among the SNR, the buffer size of the queue, and the overflow probability exists. Hence, two different approaches to select the optimal target SNR of the SSPC and the maximum buffer size of the queue under the requested queuing buffer overflow probability are proposed. The improvement of the SSPC, its impact on the system, and the validity of the proposed models are shown in this paper as well.     

面向云计算的服务性能模型研究

为了衡量云计算的服务性能。分析了云计算服务的处理过程，提出了一个以排队论为基础的云计算服务性能模型。以Amazon SimpleDB的更新延迟为例，仿真验证了该模型，并以此为基础，分析比较了模型中的关键因子。实验表明，提升服务装置的性能可以有效提升云计算服务的性能，且在云计算部署后期，虚拟机实例的性能是服务性能提升的瓶颈。     

An Efficient Delay Tolerance and Cost-Effective Secure NFV Enabled Multi-casting in Mobile Edge Cloud Networks

The mobile edge cloud has developed as the main platform to offer low latency network services from the edge of networks for stringent delay necessities of mobile applications. In mobile edge cloud networks, network functions virtualization (NFV) creates the frameworks for building up a new dynamic resource management framework structure to effectively utilize network resources. Delay tolerance NFV-enabled multicast request admissions in a mobile edge-cloud network are explored in this paper to limit request admission delays or maximizing system performance for a group of requests arriving individually. At first, for the cost reduction issue of a single NFV-empowered multicast request admission, the admission cost of each multicast request is assessed, and the Support based graph is constructed. Here, the multicast requests are prioritized dependent on their admission cost. Subsequently, trust and the delay-based local gradient are assessed for the prioritized multicast requests. At long last, delay tolerance NFV multicasting is accomplished by successful (First Come First Serve) FCFS queuing reliant on the assessed local gradient of requests. When compared to existing approaches, the exploratory results show that the proposed methodology is superior in terms of throughput, admission cost, and running time.

     

Performance of MIMO-UWB for LOS and N-LOS Propagation

Mobile cloud computing (MCC) enables ubiquitous access to a diverse range of Internet multimedia services in a pay-as-you-go economic model. In an MCC environment with highly mobile users, the migration of service requests from one cloud server to another due to user movement may frequently occur. We note that when the load offered to the cloud server is increased beyond the capacity limit, particularly when migrated traffic due to user movement suddenly appears, the probability to disrupt existing services gets higher, consequently resulting in the degradation of user quality of experience (QoE). To keep the service disruption probability at an acceptable level so as to maintain a high user-perceived QoE for different classes of multimedia services, this paper proposes a QoE-aware service continuity strategy for the cloud server in an MCC environment. The strategy is based on the buffer-occupancy threshold policy that differentiates newly arriving service requests coming from the mobile users and offers effective protection for migrated service requests against traffic fluctuation in newly arriving service requests. With the proposed strategy, the cloud server can dynamically change the buffer thresholds for different classes of service requests based on the offered traffic load and the user mobility to improve resource utilization, and, most importantly, to keep the service disruption probability at an acceptable level. Besides, by taking the effect of migrated traffic into account, we develop an analytical model to study the performance of the cloud server using the proposed strategy. With the analytical model, we propose an iterative method to determine the optimal buffer thresholds that maximize resource utilization while keeping an acceptable user QoE for different classes of services.

     

Virtual machine scheduling strategy based on dual-speed and work vacation mode and its parameter optimization

Due to the increasingly strict environmental standards,high pollution and high energy consumption have become the significant factors restricting the development of cloud data centers (CDC).Under the premise of guaranteeing the quality of service (QoS) of CDC,dynamic power management (DPM) technology was applied,synchronous multiple working sleep mode was introduced,and a novel virtual machine (VM) scheduling strategy was proposed.By establishing a two-dimensional continuous-time Markov stochastic model with adaptive service rate and synchronous multiple work vacations,and using the method of a matrix geometric solution,the performance of the VM scheduling strategy was evaluated in terms of energy saving level and average delay of requests.Numerical results with analysis and simulation verify the energy saving effectiveness of the VM scheduling strategy.In order to achieve a reasonable balance between the response performance and the energy-saving effect,a system utility function was constructed from the perspective of economics and design a researching algorithm of the sleep parameter based on the firefly algorithm(FA).     

Admission control and buffer management of wireless communication systems with mobile stations and integrated voice and data services

This study presents models for management of voice and data traffic and new algorithms, which use call admission control as well as buffer management to optimise the performance of single channel systems such as wireless local area networks in the presence of mobile stations. Unlike existing studies, the new approach queues incoming voice packets as well as data packets, and uses a new pre-emption algorithm in order to keep the response time of voice requests at certain levels while the blocking of data requests is minimised. A new performance metric is introduced to provide uncorrelated handling of integrated services. Queueing related issues such as overall queue capacity, individual capacities for voice and data requests, the probability of blocking, and effects of waiting time on overall quality of service are considered in detail. Analytical models are presented and the results obtained from the analytical models were validated using discrete event simulations.     

An efficient clustering and load balancing of distributed cloud data centers using graph theory

The prime focus of the Cloud Service Providers is enhancing the service delivery performance of the distributed cloud data centers. The clustering and load balancing of distributed cloud data centers have significant impact on its service delivery performance. Hence, this paper models distributed cloud data center environment as a network graph and proposes a two‐phase cluster‐based load balancing (CLB) algorithm based on a graph model. The first phase proposes a Cloud Data Center Clustering algorithm to cluster the distributed cloud data centers based on their proximity. The second phase proposes a Client‐Cluster Assignment algorithm to perform uniform distribution of the client requests across the clusters to enable load balancing. To assess the performance, the proposed algorithms are compared with other K‐constrained graph‐based clustering algorithms namely, graph‐based K‐means and K‐spanning tree algorithms on a simulated distributed cloud data center environment. The experimental results reveal that the proposed CLB algorithm outperforms the compared algorithms in terms of the average clustering time, load distribution, and fairness index and hence improves the service delivery performance of the distributed cloud data centers.     

Survivable spectrum-shared ability in flexible bandwidth optical networks with distributed data centers

The survivable spectrum-shared ability problems are addressed by considering the shared-path protection in flexible bandwidth optical networks with distributed data centers. The overall objective of this paper is to investigate the effect of the spectrum-shared ability on the spectrum efficiency and the blocking probability of cloud service requests. We propose a survivable algorithm with the spectrum-shared ability (SA_SSA) to minimize blocking probability of cloud service requests and to improve the spectrum efficiency in shared-path protection. For comparison, an existing shared-path protection algorithm named Aggressive algorithm with spectrum-shared ability is also introduced. Simulation results show that, considering the different spectrum-shared abilities, our proposed SA_SSA has a better performance in terms of blocking probability, number of frequency slots/Erlang, spectrum occupation ratio, and spectrum redundancy ratio compared to Aggressive algorithm in flexible bandwidth optical networks with distributed data centers. Meanwhile, the spectrum efficiencies of the SA_SSA and Aggressive algorithms improve as the spectrum-shared abilities are strengthened.