QoS based multicast routing algorithms for real time applications

In recent years, there has been a lot of interest in providing real-time multimedia services like digital audio and video over packet-switched networks such as Internet and ATM. These services require certain quality of service (QoS) from the network. The routing algorithm should take QoS factor for an application into account while selecting the most suitable route for the application. In this paper, we introduce a new routing metric and use it with two different heuristics to compute the multicast tree for guaranteed QoS applications that need firm end-to-end delay bound. We then compare the performance of our algorithms with the other proposed QoS-based routing algorithms. Simulations were run over a number of random networks to measure the performance of different algorithms. We studied routing algorithms along with resource reservation and admission control to measure the call throughput over a number of random networks. Simulation results show that our algorithms give a much better performance in terms of call throughput over other proposed schemes.     

一种工作流环境下能耗感知的多路径服务组合方法

当前,服务组合方法只考虑组合服务QoS的优化而不考虑组合服务的能耗优化.针对这一问题,文中首先根据不同情况提出了两种服务能耗模型;其次在基于工作流的服务组合环境下,提出了一种能耗感知的多路径服务组合方法 EAMSC.该方法对服务组合的能耗优化问题进行了数学建模,并提出了一种基于启发式的多路径服务组合算法,该算法包括两个部分:一是组合服务的可行路径查找,即在满足端到端QoS约束的前提下找出若干条可行的服务组合路径;二是请求速率的分配,即在可行的服务组合路径上依据服务能耗模型分配请求流量以降低组合服务的总体能耗.最后,仿真实验结果表明:能耗感知的多路径服务组合方法与传统的服务组合方法相比,能够在保证端到端QoS约束的基础上有效地减少组合服务的总能耗.     

A bargaining-driven global QoS adjustment approach for optimizing service composition execution path

In service-based workflow area, service composition is emerging as a promising technology providing more flexible integration means across a variety of distributed heterogeneous applications. A critical research issue in service composition is how to achieve an optimized overall end-to-end quality of service (QoS) composition’s requirements by effectively coordinating QoS constraints of individual service. However, few of existing representative works have well considered economic strategies closely related to competitive market scenarios to address overall QoS issues in service composition. In this paper, we propose a novel global QoS adjustment approach by employing a recursive bargaining strategy to gradually remove QoS constraint violations for optimizing service composition execution path. Our approach mainly exploits the hidden marketplace competitive techniques from economics for developing a novel bargaining strategy. Based on this strategy, a user agent can simultaneously negotiate with more than one provider for driving them into competition. As a result, concessions may be made by service providers depending on their own economic equilibrium theories to offer better QoS values than their initial proposals and thus a user can win more profits. Through recursively using bargaining strategy, critical nodes of an initially built optimal service composition execution path by local optimization policy can be continuously updated to minimize QoS constraint violations by reselecting better service providers. Eventually, a near-optimal service composition execution path can be reconstructed meeting user’s overall QoS requirements. Meanwhile, an experiment and evaluation have been conducted for the sake of demonstrating the feasibility and effectiveness of our proposed approach.     

基于信任QoS增强的网格服务调度算法

针对目前服务网格资源管理中存在的信任机制与调度机制分离的缺陷,基于网格信任模型与信任效益函数,讨论了信任QoS增强的计算服务调度问题．分析传统调度算法的缺陷,提出了基于信任关系的网格服务调度算法．该算法在保证服务性能Qos要求的同时兼顾信任QoS的要求．对基于信任关系的网格服务调度算法性能进行了多角度分析和大规模仿真实验．结果表明:该算法较传统的基于性能QoS的调度启发式和另外两种基于信任Qos的调度启发式而言,不仅具有较优的平均信任效益、总信任效益和较小的最终服务期,而且在失效服务数和系统吞吐率等方面具有较好的综合性能．     

Hardware design and implementation of packet fair queuing algorithms for the quality of service support in the high-speed internet

The increasing amount of real-time traffic carried over the Internet requires end-to-end quality of service (QoS) support. To this end, the QoS Schedulers, that are implemented in routers, assign the available bandwidth resources to packet flows according to their respective allocated rates. Packet Fair Queuing (PFQ) schedulers can provide fair service and low end-to-end delay bound to the traffic flows. However, they have higher implementation complexity compared to other algorithms, because of the requirements of tracking the system state, and searching for the packet to get service among all flows, that are queued at the outgoing interface. QoS scheduling is a data plane functionality, which requires hardware implementation for high speed router interfaces. The previous works on hardware implementation of PFQ schedulers are specific to certain algorithms, and they do not provide any results on real hardware platforms. In this paper, we present a general hardware design framework for PFQ schedulers, and apply this framework to the WF²Q+ PFQ algorithm to demonstrate its properties. We carry out the entire implementation of the WF²Q+ algorithm on an FPGA, and evaluate its performance with real traffic flows. In addition, we implement WFQ as a second PFQ algorithm to demonstrate the generality of the framework.     

基于遗传算法的多性能目标网格服务调度算法

在分析状态图工作流模型的基础上，提出了一种网格环境下多QoS（服务质量）约束的组合服务模型，根据提出的模型归纳出了动态服务调度问题的形式化描述，并提出了一种基于遗传算法的动态服务调度算法进行求解.该算法采用基于服务区域及服务实例个数的编码方式，以组合方案的有效性和组合服务的综合QoS参数的效用值作为适应度函数，从而保证组合服务调度的全局QoS要求.与其它算法进行了比较.实验结果显示该算法是可行和有效的．     

一个快速的时延有界低代价多播路由算法

基于ＱｏＳ的多播路由算法需要在满足每个个体ＱｏＳ需求的同时,又能高效管理网络资源,提出了一种满足端端时延限制的低代价多播路由算法。算法使用一个修改的Ｓｔｅｉｎｅｒ树近似算法先构建时延有界的低代价多播树,再通过最小时延路径与其它尚不在多播树的且结点相连。     

Preferred link based delay-constrained least-cost routing in wide area networks

Multimedia applications involving digital audio and/or digital video transmissions require strict QoS constraints (end-to-end delay bound, bandwidth availability, packet loss rate, etc.) to be met by the network. To guarantee the real-time delivery of packets satisfying these constraints, a real-time channel (D. Ferrari and D.C. Verma, A scheme for real-time channel establishment in wide-area networks. IEEE JSAC, 8(3), 368–379, 1990) needs to be established before the transmission of packets of a connection can begin. The establishment of such channels requires the development of efficient route selection algorithms that are designed to take into account the QoS constraints.

The general problem of determining a least-cost delay-constrained route in a given communication network has been proved to be NP-hard (M.R. Garey and D.S. Johnson, Computers and Intractability: a guide to the theory of NP-completeness, W.H. Freeman, 1979). In this paper, we describe a preferred link approach to distributed delay-constrained least-cost routing in order to establish real-time channels. The approach attempts to combine the benefits of probing and backtracking based algorithms (better adaptiveness and wider search) with the advantages of distance-vector type algorithms (lower setup time). The scheme is flexible in that a variety of heuristics can be employed to order the neighbouring links of any given node. Three heuristics are proposed and their performance is studied through simulation experiments. The simulation results indicate that the proposed heuristics provide better performance than other preferred neighbour methods, in terms of increased call acceptance rate and lower average route cost. The heuristics are also shown to adapt much better to dynamic variations in network and link characteristics.     

基于渐进约束违背删除策略的服务合成执行路径优化

在面向服务计算领域,服务合成正成为分布异构资源整合、业务集成的一个新型计算模式。在开放互联的因特网环境下,由于存在多个功能相似但服务质量不同的服务提供商,从而导致一个抽象的服务合成方案存在多条可执行路径。然而,在满足用户综合QoS约束需求的情况下,如何有效协同单个服务的QoS从多条候选路径选出一条优化的执行路径,便成为服务合成QoS研究中的一个关键问题。遗憾的是,该问题还没有得到很好的解决。为此,我们提出一个采取渐进约束违背删除策略建立优化执行路径的方法。我们的方法能够避免探测多条执行路径,而是在靠本地最优化策略建立的执行路径上,通过全局QoS计算和QoS约束检查模型找出QoS约束违背,并进而采取渐进约束违背删除策略逐渐删除这些约束违规,并建立起一条近优的执行路径。实验表明,我们的方法很好地降低了建立优化执行路径的时间复杂性。     

异构网的QoS参数映射模型和映射方法

针对异构融合网络中不同网络的QoS和映射问题,解决分类和映射方法是提出QoS参数映射模型。在充分研究现有异构网QoS参数映射方法的基础上,提出了QoS参数映射三维模型（Three-dimensional Model for QoS Parameter Mapping,QoS-TMPM）,模型采用颗粒度适中、区分度较大的QoS分类关键参数进行搭建。同时,基于QoS参数映射三维模型的QoS参数映射方法能顺利地完成异构网络间的QoS参数映射,仿真结果表明QoS参数模型和参数映射方法能改善QoS保证服务,从而提高用户的业务体验质量。     

面向服务遥感图像处理平台中时间感知的服务质量预测

面向服务架构(SOA)通过将遥感图像处理算法抽象成组件化的服务，进一步通过服务选择及组合，满足遥感图像处理的复杂业务需求。为了得到满足用户服务质量(QoS)要求的服务组件进行组合，前提就是获得所有服务的QoS。然而，对于用户未调用过的服务，其QoS是缺失的，因此围绕缺失QoS的预测出现了很多研究工作。针对目前QoS预测没有考虑时效性，进而影响了QoS预测准确度的问题,通过考虑时效性提出基于时间片的QoS模型，进一步基于协同过滤提出时间感知的QoS预测方法。在WS-DREAM真实数据集中的实验结果表明，时间感知的QoS预测方法能够获得较小的均方误差(MAE)和均方根误差(RMSE)。此外，对于可能影响时间感知QoS预测的多个参数，通过设置不同的参数组合进行了多次实验和分析，为参数的选择提供了一定的参考。     

Design and performance analysis of an inductive QoS routing algorithm

Routing mechanism is key to the success of large-scale, distributed communication and heterogeneous networks. Consequently, computing constrained shortest paths is fundamental to some important network functions such as QoS routing and traffic engineering. The problem of QoS routing with multiple additive constraints is known to be NP-complete but researchers have been designing heuristics and approximation algorithms for multi-constrained paths algorithms to propose pseudo-polynomial time algorithms. This paper introduces a polynomial time approximation quality of service (QoS) routing algorithm and constructs dynamic state-dependent routing policies. The proposed algorithm uses an inductive approach based on trial/error paradigm combined with swarm adaptive approaches to optimize lexicographically various QoS criteria. The originality of our approach is based on the fact that our system is capable to take into account the dynamics of the network where no model of the network dynamics is assumed initially. Our approach samples, estimates, and builds the model of pertinent aspects of the environment which is very important in heterogeneous networks. The algorithm uses a model that combines both a stochastic planned pre-navigation for the exploration phase and a deterministic approach for the backward phase. Multiple paths are searched in parallel to find the K best qualified ones. To improve the overall network performance, a load adaptive balancing policy is defined and depends on a dynamic traffic path probability distribution function. We conducted a performance analysis of the proposed QoS routing algorithm using OPNET based on a platform simulated network. The obtained results demonstrate substantial performance improvements as well as the benefits of learning approaches over networks with dynamically changing traffic.     

QoS可信的服务组合技术研究*

现有基于QoS的服务组合方法通常假定QoS数据可信,然而这种假设很难保证,虚假的服务对于服务组合实现的影响常常是致命的。针对该问题,提出一种考虑QoS可信的服务组合方法。分析了QoS来源,建立了相应的系统结构,划分了信任级别,根据社会交往方式量化了直接经验和间接经验在可信判断中的作用,并以此构建QoS的计算模型,设计了相应的算法。最后,通过仿真实验,将考虑可信的服务组合方法与未考虑可信的传统组合方法进行了比较,前者体现出明显的优越性,从而表明提出的方法在性能和执行效率上是可行的。     

基于广义随机着色Petri网的Web服务组合模型

给出了广义随机着色Petri网(GSCPN)和基于GSCPN的Web服务模型,实现了Web服务QoS和数据的表示,并给出了基于GSCPN的服务组合运算方法。用模型的层次化方法来降低模型的复杂性,一定程度上减轻了模型状态爆炸问题。分析了组合运算的性质,探讨了模型的性质和QoS分析方法。     

A calculus for stochastic QoS analysis

The issue of Quality of Service (QoS) performance analysis in packet-switched networks has drawn a lot of attention in the networking community. There is a lot of work including an elegant theory under the name of network calculus, which focuses on analysis of deterministic worst case QoS performance bounds. In the meantime, researchers have studied stochastic QoS performance for specific schedulers. However, most previous works on deterministic QoS analysis or stochastic QoS analysis have only considered a server that provides deterministic service, i.e. deterministically bounded rate service. Few have considered the behavior of a stochastic server that provides input flows with variable rate service, for example wireless links. In this paper, we propose a stochastic network calculus to analyze the end-to-end stochastic QoS performance of a system with stochastically bounded input traffic over a series of deterministic and stochastic servers. We also prove that a server serving an aggregate of flows can be regarded as a stochastic server for individual flows within the aggregate. Based on this, the proposed framework is further applied to analyze per-flow stochastic QoS performance under aggregate scheduling.     

多版本中央数据库的移动数据库系统并发控制方案

首先介绍多版本中央数据库在移动数据库中的优势,然后分析由多版本中央数据库带来的同步并发控制问题产生的原因和由此造成的后果。就此问题提出了一个并发方案,并分析这个方案如何解决了并发问题,最后对这种并发方案的实质进行了简要的说明。     

Service selection algorithms for Web services with end-to-end QoS constraints

Web services are new forms of Internet software that can be universally deployed and invoked using standard protocols. Services from different providers can be integrated into a composite service regardless of their locations, platforms, and/or execution speeds to implement complex business processes and transactions. In this paper, we study the end-to-end QoS issues of composite services by utilizing a QoS broker that is responsible for selecting and coordinating the individual service component. We design the service selection algorithms used by QoS brokers to construct the optimal composite service. The objective of the algorithms is to maximize the user-defined utility function value while meeting the end-to-end delay constraint. We propose two solution approaches to the service selection problem: the combinatorial approach, by modeling the problem as the Multiple Choice Knapsack Problem (MCKP), and the graph approach, by modeling the problem as the constrained shortest path problem in the graph theory. We study efficient solutions for each approach.This research was supported in part by NSF CCR-9901697.     

Static heuristics for robust resource allocation of continuously executing applications

We investigate two distinct issues related to resource allocation heuristics: robustness and failure rate. The target system consists of a number of sensors feeding a set of heterogeneous applications continuously executing on a set of heterogeneous machines connected together by high-speed heterogeneous links. There are two quality of service (QoS) constraints that must be satisfied: the maximum end-to-end latency and minimum throughput. A failure occurs if no allocation is found that allows the system to meet its QoS constraints. The system is expected to operate in an uncertain environment where the workload, i.e., the load presented by the set of sensors, is likely to change unpredictably, possibly resulting in a QoS violation. The focus of this paper is the design of a static heuristic that: (a) determines a robust resource allocation, i.e., a resource allocation that maximizes the allowable increase in workload until a run-time reallocation of resources is required to avoid a QoS violation, and (b) has a very low failure rate (i.e., the percentage of instances a heuristic fails). Two such heuristics proposed in this study are a genetic algorithm and a simulated annealing heuristic. Both were “seeded” by the best solution found by using a set of fast greedy heuristics.     

QoS Requirement Generation and Algorithm Selection for Composite Service Based on Reference Vector

Under SOA (Service-Oriented Architecture), composite service is formed by aggregating multiple component services together in a given workflow. One key criterion of this research topic is QoS composition. Most work on service composition mainly focuses on the algorithms about how to compose services according to assumed QoS, without considering where the required QoS comes from and the selection of user preferred composition algorithm among those with different computational cost and di?erent selection resu...     

Multi-objective service composition model based on cost-effective optimization

The widespread application of cloud computing results in the exuberant growth of services with the same functionality. Quality of service (QoS) is mostly applied to represent nonfunctional properties of services, and has become an important basis for service selection. The object of most existing optimization methods is to maximize the QoS, which restricts the diversity of users’ requirements. In this paper, instead of optimization for the single object, we take maximization of QoS and minimization of cost as two objects, and a novel multi-objective service composition model based on cost-effective optimization is designed according to the complicated QoS requirements of users. Furthermore, to solve this complex optimization problem, the Elite-guided Multi-objective Artificial Bee Colony (EMOABC) algorithm is proposed from the addition of fast nondominated sorting method, population selection strategy, elite-guided discrete solution generation strategy and multi-objective fitness calculation method into the original ABC algorithm. The experiments on two datasets demonstrate that EMOABC has an advantage both on the quality of solution and efficiency as compared to other algorithms. Therefore, the proposed method can be better applicable to the cloud services selection and composition.