QoS-aware service composition for distributed video surveillance

Quality of Service (QoS) is essential for the ubiquitous access of media services in real-time distributed video surveillance applications. To have ubiquitous access of desired media with emergency officials’ handheld devices, appropriate media transcoding services are required. Currently, it is challenging to select and compose these services for each of the devices to satisfy the desired QoS demand. To compose these media services so that video stream is available for target pervasive and smart devices, a composition algorithm is required. Thus, this paper presents a QoS-aware service composition algorithm to select the best composition for the target ubiquitous client so that it can optimally provide QoS to heterogeneous users. We have implemented a video surveillance prototype to demonstrate the performance of the proposed QoS-aware composition algorithm. Results from this prototype reveal that the approach is suitable for real-time video surveillance.     

Weighted principal component analysis-based service selection method for multimedia services in cloud

Cloud computing has rendered its ever-increasing advantages in flexible service provisions, which attracts the attentions from large-scale enterprise applications to small-scale smart uses. For example, more and more multimedia services are moving towards cloud to better accommodate people’s daily uses on various smart devices that support cloud, some of which are similar or equivalent in their functionality (e.g., more than 1,000 video services that share similar “video-play” functionality are present in APP Store). In this situation, it is necessary to discriminate these functional-equivalent multimedia services, based on their Quality of Service (QoS) information. However, due to the abundant information of multimedia content, dozens of QoS criteria are often needed to evaluate a multimedia service, which places a heavy burden on users’ multimedia service selection. Besides, the QoS criteria of multimedia services are usually not independent, but correlated, which cannot be accommodated very well by the traditional selection methods, e.g., traditional simple weighting methods. In view of these challenges, we put forward a multimedia service selection method based on weighted Principal Component Analysis (PCA), i.e., Weighted PCA-based Multimedia Service Selection Method (W_PCA_MSSM). The advantage of our proposal is two-fold. First, weighted PCA could reduce the number of QoS criteria for evaluation, by which the service selection process is simplified. Second, PCA could eliminate the correlations between different QoS criteria, which may bring a more accurate service selection result. Finally, the feasibility of W_PCA_MSSM is validated, by a set of experiments deployed on real-world service quality set QWS Dataset.     

基于改进DAG的Web服务组合优化

针对当前Web服务组合过程的复杂性,提出了一种基于改进DAG的Web服务组合方法.该方法通过构造有向无环图(DAG)表示服务之间可能的匹配,并对有向无环图进行优化,删除无效的组合路径及冗余服务,采取逆向搜索并结合服务的Qo S,减少处理的服务数量,对筛选得到的组合服务通过比较Qo S值选取最优的服务.为了验证以上方法的有效性,以智能用电互动化支撑平台的企业用能分析业务的生成设计实验,并针对不同规模的服务集进行了实验分析.结果证明本文提出的基于改进DAG的Web服务生成方法具有良好的可行性,能够生成满足用户需求且具有较高服务质量的优化服务,适用于智能用电互动化支撑平台的Web服务组合生成.     

智慧城市中QOE感知服务组合策略的研究

无线传感器的发展和物联网技术的应用,配有物联网的设备正以一种全新的“智能终端服务”的形式进入人类生活,要想在复杂冗余的物联网环境中寻得满足不同用户需求的服务,需要考虑服务组合的概念。为此,在对传统服务组合研究方法深入分析的基础上,提出了智慧城市的服务组合选择策略。首先根据物联网应用服务的多样性,通过改进蚁群算法获得用户对服务的偏好顺序,然后使用层次化QOE评价模型量化客户体验质量,进而选出最优服务组合提供给用户。根据提出的服务组合策略,给出服务选择具体的实施流程,通过仿真模拟表明基于改进蚁群算法的服务组合策略在服务选择问题上具有一定的推广价值。     

基于移动代理的蚂蚁算法在QoS路由选择中的应用研究

随着Internet上广泛的分布式多媒体应用对服务质量（QoS)需求的增长，各种服务应用对网络所能提供的QoS提出了不同的要求，所以高效率的QoS支持越来越显示出其重要性，而路由机制是实现QoS保证的关键之一。首先对一般性的QoS路由选择问题进行了讨论；在分析了自然界中蚂蚁卓越的选路行为后，提出了一种基于智能蚂蚁算法的QoS路由选择算法（QRAP），接着对QRAP算法进行了修正，使之更加符合应用的需求；然后讨论了该算法基于移动代理（Mobile Agent)的应用框架，并且给出了每个移动代理虚拟上应用模块的实现原理；最后通过实验仿真，验证了该算法应用的可行性和有效性。     

Joint optimisation of application QoS and energy conservation in grid environment

Mobile and wireless devices with limited energy are now of interest for the grid community. Reducing the related energy consumption is very important for wireless devices. Compared to conventional grid systems, energy aware grids need to support application quality of service (QoS) with limited energy. There is an inherent conflict in the design goals for high grid application QoS and low energy consumption. To address this challenging problem, this article considers joint optimisation of application QoS and energy conservation in grid environment. Joint optimisation of application QoS and energy conservation is targeted to maximise the system utility without exceeding the deadline and the total energy available, which can be provided by an exhaustible source such as a battery. This article formulates joint optimisation of application QoS and energy conservation as constrained maximisation problem; the constraints include energy budget and application QoS requirements. This article also presents a QoS and energy aware scheduling algorithm (QESA) which balances application QoS and energy conservation to improve system performance. In the simulation, the performance evaluation of QESA is conducted.     

Genetic algorithms for route discovery.

Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called "cognitive packet network" (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their "fitness." We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making.     

一种基于性能模型的中间件自配置框架

高动态的计算环境使得QoS(quality of service)保障对于基于组件的分布式系统越来越重要,软件系统需要具备自我调整的能力以适应外部环境的变化.给出一种自适应的中间件配置框架,能够动态感知负载变化,并自动调整系统参数配置以保持用户所要求的服务质量.该框架的核心是一个基于分层排队网络的性能预测模型,用于指导搜索最优的资源配置,使性能需求得到最大的满足.在OnceAS应用服务器上进行原型实现,并以StockOnline应用做实验,比较了在使用和不使用该框架时的性能需求的满足情况.结果显示,在负载增加时,通过自配置框架的调控,应用性能需求的保障程度得到了较大的提升.     

An adaptive quality of service aware middleware for replicated services

A dependable middleware should be able to adaptively share the distributed resources it manages in order to meet diverse application requirements, even when the quality of service (QoS) is degraded due to uncertain variations in load and unanticipated failures. We have addressed this issue in the context of a dependable middleware that adaptively manages replicated servers to deliver a timely and consistent response to time-sensitive client applications. These applications have specific temporal and consistency requirements, and can tolerate a certain degree of relaxed consistency in exchange for better response time. We propose a flexible QoS model that allows clients to specify their timeliness and consistency constraints. We also propose an adaptive framework that dynamically selects replicas to service a client's request based on the prediction made by probabilistic models. These models use the feedback from online performance monitoring of the replicas to provide probabilistic guarantees for meeting a client's QoS specification. The experimental results we have obtained demonstrate the role of feedback and the efficacy of simple analytical models for adaptively sharing the available replicas among the users under different workload scenarios.     

基于消息中间件的多维QoS管理模型的设计与实现

目前大多数中间件产品主要关注于应用的功能性需求,对应用的服务质量（QoS）需求只提供“尽力而为”的服务,难以满足分布式交互在时间、空间、可靠性等方面的QoS需求。首先对消息中间件和QoS的概念进行了介绍,然后设计了一种基于消息中间件的多维QoS管理模型,介绍了该模型的体系结构,并对该模型的核心模块进行了详细分析。该模型以消息中间件为平台,对分布式交互的QoS进行管理,以满足分布式交互的QoS需求。     

基于P2P的分布式VOD系统的研究

本文构建了基于P2P的分布式VOD系统的应用模型,并以JXTA和Windows Media作为技术支持,对系统中的相应关键问题提出了解决方案及其实现方法,力争用尽量小的资源消耗来提供保障QOS的分布式VOD服务。     

QoS guided Min-Min heuristic for grid task scheduling

Task scheduling is an integrated component of computing.With the emergence of Grid and ubiquitous computing,new challenges appear in task scheduling based on properties such as security,quality of service,and lack of central control within distributed administrative domains.A Grid task scheduling framework must be able to deal with these issues.One of the goals of Grid task scheduling is to achivev high system throughput while matching applications with the available computing resources.This matching of resources in a non-deterministically shared heterogeneous environment leads to concerns over Quality of Service (QoS).In this paper a novel QoS guided task scheduling algorithm for Grid computing is introduced.The proposed novel algorithm is based on a general adaptive scheduling heuristics that includes QoS guidance.The algorithm is evaluated within a simulated Grid environment.The experimental results show that the nwe QoS guided Min-Min heuristic can lead to significant performance gain for a variety of applications.The approach is compared with others based on the quality of the prediction formulated by inaccurate information.     

TSSF:基于QoS分解的可信服务调度框架

网络计算环境的松散化、独立性和异构性的特点不断给服务计算发展带来新的挑战,与此同时用户对软件系统实时性、安全性、可靠性、可用性等非功能属性要求又在不断提高,如何保障服务计算系统的可信性是平台设计者和服务应用开发者所面临的重要问题.设计了基于QoS分解的可信服务调度框架(Trustworthy Senrice Scheduling Framework,TSSF),通过对组合服务的可信QOS规划,并利用自适应的服务调度机制为服务提供可信QOS保障.提出了基于模糊集的可信QOS分解模型,将用户对组合服务的QoS需求分解为每个原子服务单独的可信QOS需求;根据分解后的额定QoS需求,以创建动态服务实体组(Dynamic Service Entity Group,DSEG)的方式为原子服务提供可信QoS保障,并以DSEG为基础详细叙述了变粒度、层次化基于反馈设计思想的自适应可信服务调度技术.最后扩展了JBoss中间件,实现了支持该服务调度框架的平台,并在此平台上进行了实验,结果表明TSSF能够为组合服务的QoS需求提供可信保障.     

Qos-aware mobile service optimization in multi-access mobile edge computing environments

With the rapid development of mobile Internet technologies and various new service services such as virtual reality (VR) and augmented reality (AR), users’ demand for network quality of service (QoS) is getting higher and higher. To solve the problems of high load and low latency in-network services, this paper proposes a data caching strategy based on a multi-access mobile edge computing environment. Based on the MEC collaborative caching framework, an SDN controller is introduced into the MEC collaborative caching framework, a joint cache optimization mechanism based on data caching and computational migration is constructed, and the user-perceived time-lengthening problem in the data caching strategy is solved by a joint optimization algorithm based on an improved heuristic genetic algorithm and simulated annealing. Meanwhile, this paper proposes a multi-base station collaboration-based service optimization strategy to solve the problem of collaboration of computation and storage resources due to multiple mobile terminals and multiple smart base stations. For the problem that the application service demand in MEC server changes due to time, space, requests and other privacy, an application service optimization algorithm based on the Markov chain of service popularity is constructed, and a deep deterministic strategy (DDP) based on deep reinforcement learning is also used to minimize the average delay of computation tasks in the cluster while ensuring the energy consumption of MEC server, which improves the accuracy of application service cache updates in the system as well as reducing the complexity of service updates. The experimental results show that the proposed data caching algorithm weighs the cache space of user devices, the average transfer latency of acquiring data resources is effectively reduced, and the proposed service optimization algorithm can improve the quality of user experience.     

A QoS-driven approach for service-oriented device anycasting in ubiquitous environments

The vision of pervasive computing is to let users enjoy ICT-enabled services in an “any time, anywhere, on any device” manner. It aims to supply ubiquitous services through communications among a set of devices deployed in a ubiquitous environment. Due to the diverse QoS needs of different kinds of ubiquitous services and users, it is a critical challenge to select an optimal set of devices with the objective of achieving service-specific QoS goals like low packet loss, short packet delay, and high energy efficiency. In this paper, the problem is first formulated as the service-oriented device anycasting problem (SDAP) and then proved as an NP-complete problem. By adopting a tree-based service representation model, Basu et al. proposed the dynamic task-embedding anycasting (DTA) approach. This approach effectively solves the SDAP in a distributed way. However, the service quality is likely sacrificed because the tree scheme does not sufficiently describe a ubiquitous service. In this paper, we propose a novel approach called the service-oriented device anycasting (SDA) approach that adopts a graph-based service representation model called the service profile (SP). By introducing a layered structure into the SP, the SDA approach can reach a compromise between service quality and computational complexity. In addition, the QoS-driven utility function is proposed to quantify service quality by matching the capabilities of heterogeneous devices to various QoS needs. Finally, the simulation results show that the SDA approach outperforms the DTA approach by saving roughly 20% of device energy and prolonging the network lifetime. Packet loss and packet delay are also improved by roughly 25% and 8%, respectively. The advantage of the SDA approach is more obvious in environments with highly mobile devices and multiple users.     

基于CORBA的可靠的负载均衡服务研究

随着基于CORBA的分布式应用系统对QoS要求的不断提高，需要采用负载均衡技术来提高系统的整体性能。该文分析了现有的基于CORBA的负载均衡技术，研究了一个基于CORBA的可靠的负载均衡服务，它具有高可靠性、自适应性、可移植性和对用户透明的特点。     

分布式多媒体系统服务质量管理机制的研究*

分布式多媒体系统的发展向服务质量QoS(quality of service)管理机制提出了新的挑战.本文从分析现有网络系统在QoS管理方面的不足出发,引出分布式多媒体系统中有关QoS管理的基本观点,讨论各层QoS参数和QoS管理机制的功能组成,并且给出一个实例,最后得出结论.     

基于智能配网业务的WRED算法改进及仿真

由于智能配网通信业务的复杂多样性,要求其通信网提供比传统服务质量(QoS)机制更具有针对性的灵活高效的QoS机制保证。为此,分析智能配网的业务特征,提出在主动队列管理中加权随机早检测(WRED)的改进算法WRED_gentle,通过在OPNET上建立集抄应用和配电自动化应用的业务模型,对NoWRED、WRED和WRED_gentle 3种算法进行仿真实验。结果证明WRED_gentle算法能够明显改善网络时延、吞吐量和链路利用率等性能,保证智能配电通信网的QoS要求。     

Dynamic end-to-end QoS management middleware for distributed multimedia systems

In this paper, we present a separable, reusable middleware solution that provides coordinated, end-to-end QoS management over any type of service component, and can use existing (legacy) QoS management solutions (by using wrappers) in a distributed multimedia system. Our middleware solution incorporates strategic and tactical QoS managers, and supports protocols and messages between tactical managers and managed application components, and between QoS managers in the management hierarchy. Strategic QoS managers take a global view of QoS provided by a set of application components within the manager's policy domain. Tactical QoS managers provide local control over application components. We introduce the concept of QoS policy domains to scope the authority of a strategic QoS manager. We describe how the management hierarchy is dynamically configured and reconfigured based on runtime needs of the application.