A degree-constrained QoS-aware routing algorithm for application layer multicast

Application layer multicast (ALM) provides a low-cost solution for multicast over the Internet. It overcomes the deployment hurdle of IP multicast by moving all multicast related functions from network routers to end-hosts. However, since packet replication is performed on end-hosts, the system performance of an ALM is limited by the bandwidth of end-hosts. Therefore, degree-constrained QoS-aware multicast routing becomes one of the key concerns for implementing realtime multicast services, such as continuous streaming applications. In this paper, we claim that the QoS gained by most users will be better evaluated using the overall latency, and we explore the optimization of Degree-Constrained Minimum Overall Latency Spanning Tree (DCMOLST). The process for optimizing the overall latency is divided into two phases, i.e., the initialization phase and the dynamic adjustment phase. In the former phase, we present a heuristic DCMOLST algorithm which negotiates both transmission delay and node bandwidth simultaneously, so as to avoid QoS degradation caused by any single metrics. In the later phase, we define a set of distributed iterative optimizing operations to swap the position between nearby end-hosts for further optimization. Experimental results show that the proposed degree-constrained QoS-aware routing algorithm could improve the overall performance of application layer multicast services.     

Runtime home mapping for effective memory resource usage

In tiled Chip Multiprocessors (CMPs) last-level cache (LLC) banks are usually shared but distributed among the tiles. A static mapping of cache blocks to the LLC banks leads to poor efficiency since a block may be mapped away from the tiles actually accessing it. Dynamic policies either rely on the static mapping of blocks to a set of banks (D-NUCA) or rely on the OS to dynamically load pages to statically mapped addresses (first-touch).     

QoS-aware middleware for Web services composition

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.     

QoS-aware replica placement for content distribution

The rapid growth of new information services and business-oriented applications entails the consideration of quality of service (QoS) in content distribution. This paper investigates the QoS-aware replica placement problems for responsiveness QoS requirements. We consider two classes of service models: replica-aware services and replica-blind services. In replica-aware services, the servers are aware of the locations of replicas and can therefore optimize request routing to improve responsiveness. We show that the QoS-aware placement problem for replica-aware services is NP-complete. Several heuristic algorithms for fast computation of good solutions are proposed and experimentally evaluated. In replica-blind services, the servers are not aware of the locations of replicas or even their existence. As a result, each replica only serves the requests flowing through it under some given routing strategy. We show that there exist polynomial optimal solutions to the QoS-aware placement problem for replica-blind services. Efficient algorithms are proposed to compute the optimal locations of replicas under different cost models.     

QoS-aware downlink packet scheduling for LTE networks

Scheduling for flows has been studied before. However, applying the previous schemes directly for LTE networks may not achieve good performance. To have good performance, both frequency domain allocations and time domain allocations for LTE resource blocks are suggested. Our method is suitable for real-time services and it consists of three phases. In frequency domain we design our method to utilize the RBs effectively. In time domain we first manage queues for different applications and propose a mechanism for predicting the packet delays. We introduce the concept of virtual queue to predict the behavior of future incoming packets based on the packets in the current queue. Then based on the calculated results, we introduce a cut-in process to rearrange the transmission order and discard those packets which cannot meet their delay requirements. We compare our scheduling mechanism with maximum throughput, proportional fair, modified largest delay first and exponential proportional fair. Simulation results show our scheduling method can achieve better performance than other schemes.     

QoS感知的语义服务组合搜索算法

针对如何在海量Web服务中进行快速的服务自动组合、以满足客户复杂需求的问题,提出一种基于规划图思想的搜索组合算法,该算法在正向扩展阶段除了依据所选取服务的功能与非功能属性进行有效剪枝外,还实时级联更新已驱动服务的QoS信息并有效记录所操作服务的前驱服务,通过前驱索引集合,确保逆向求解阶段的效率并满足了QoS最优的约束。在实验环节证明了注册服务数量与求解效率的线性关系,以及与其他算法相比较所体现出来的性能优势。     

Cluster-based application mapping method for Network-on-Chip

Network-on-Chip (NoC) is a newly introduced paradigm to overcome the communication problems of System-on-Chip architectures. Mapping applications onto mesh-based NoC architecture is an NP-hard problem and several heuristic methods have been presented to solve it so far. Scalability is the main problem of the heuristic methods and it is very difficult to conclude that one heuristic is better than the others. Integer Linear Programming (ILP) based methods determine the optimum mappings. However, they take very long execution times. In this paper, we propose a clustering based relaxation for ILP formulations. Our experiments conducted on several multimedia benchmarks and custom graphs show that the proposed method obtains optimal or close to optimal results within tolerable time limits.     

一种高效的服务组合优化算法

随着功能性属性相同而非功能性属性各异的Web服务的大量涌现,如何在服务组合业务流程中为各个任务选择相应的组件服务以达到组合服务的QoS(quality of service)最大化,并在此基础上满足不同用户的需求,已成为了国内外研究的热点.由于该问题的复杂性(NP-hard),目前存在的大多数方法都并不十分适合需要相对精确、实时决策的Web服务组合系统.因此,本文提出了一种基于凸包构建的组合服务优化算法(CM-HEU)用以解决QoS感知的服务组合优化问题.CM-HEU首先通过对组合服务中的每组任务进行凸包构建,以减少搜索空间.然后通过对初始解向量的多次升级和一次降级操作以达到全局优化的目标.实验表明:相对于现阶段存在的一些主流方法,CM-HEU不仅能得到一个比较理想的结果,并且具有良好的效率.     

一种多QoS约束的可靠移动组播路由协议

针对复杂多变的移动组播环境,从整体上提出了一种基于优先级的报文管理机制(PDMM)。对不同类型的报文赋以不同的优先级别,提出了一种基于层次结构的自适应移动组播路由协议(AMMPH),在子网内采用隧道机制、子网间采用远程签署和隧道机制相结合的组播路由协议,保证了报文的可靠高效传输,并在寻求最短转发路径和减少组播树重构开销间取得了较好的平衡。     

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.     

A QoS-aware VoD resource sharing scheme for heterogeneous networks

In network-aware concept, applications are aware of network conditions and are adaptable to the varying environment to achieve acceptable and predictable performance. Two basic aspects of network-aware applications, network-awareness and network adaptation, have been widely addressed in the literature. In this work, a solution for video on demand service that integrates wireless and wired networks by using the network-aware concepts is proposed to reduce the blocking probability and dropping probability of mobile requests. Fuzzy logic inference system is employed to select appropriate cache relay nodes to cache published video streams and distribute them to different peers through service oriented architecture (SOA). SIP-based control protocol and IMS standard are adopted in this work to ensure the possibility of heterogeneous communication and provide a framework for delivering real-time multimedia services over an IP-based network to ensure interoperability, roaming, and end-to-end session management. The experimental results demonstrate that effectiveness and practicability of the proposed work.     

An adaptive QoS-aware fault tolerance strategy for web services

Service-Oriented Architecture (SOA) is widely adopted for building mission-critical systems, ranging from on-line stores to complex airline management systems. How to build reliable SOA systems becomes a big challenge due to the compositional nature of Web services. This paper proposes an adaptive QoS-aware fault tolerance strategy for Web services. Based on a user-collaborated QoS-aware middleware, SOA systems can dynamically adjust their optimal fault tolerance configurations to achieve optimal service reliability as well as good overall performance. Both the subjective user requirements and the objective system performance of the Web services are considered in our adaptive fault tolerance strategy. Experiments are conducted to illustrate the advantages of the proposed adaptive fault tolerance strategy. Performance and effectiveness comparisons of the proposed adaptive fault tolerance strategy and various traditional fault tolerance strategies are also provided.     

QoS-aware path switching for VoIP traffic using SCTP

Voice over Internet protocol (VoIP) has been a prevalent multimedia service nowadays. It allows us to transmit voice data over IP networks. However, quality of service (QoS) is a major challenge to VoIP services. It must provide similar quality to traditional public switched telephone network or cellular phone services. Therefore, QoS related protocols have become important for real-time applications. Multi-protocol label switch (MPLS) is one of the important techniques to improve the network performance from QoS point of view. It employs label swapping to speed up packet forwarding. However, when a large number of users utilize VoIP services, the network congestion issue still exists. It causes delay, jitter and packet loss that affect VoIP QoS. In this paper, we propose a QoS-aware path switching strategy by using stream control transmission protocol (SCTP) in MPLS network to improve the VoIP traffic. This was done by employing SCTP selective acknowledgment mechanism to report the transmission parameters of primary path and to determine the criteria to switch to backup path. Simulation results show significant improvement in VoIP QoS.     

On optimal decision for QoS-aware composite service selection

The increasing popularity of employing web services for distributed systems contributes to the significance of service discovery. However, duplicated and similar functional features existing among services require service consumers to include additional aspects to evaluate the services. Generally, the service consumers would have different view on the quality of service (QoS) of service attributes. How to select the best composite service in theory among available service (WS) candidates for consumers is an interesting practical issue. This work proposes a QoS-aware service selection model based on fuzzy linear programming (FLP) technologies, in order to identify their dissimilarity on service alternatives, assist service consumers in selecting most suitable services with consideration of their expectations and preferences. This approach can obtain the optimal solution of consensual weight of QoS attribute and fuzzy positive ideal solution (FPIS) by extending LINMAP method, developed by Srinivasan and Shocker. Finally, two numerical examples are given to demonstrate the process of QoS-aware web service selection. The experimental results demonstrated that it is a feasible and supplementary manner in selecting the of web services.     

SALSA: QoS-aware load balancing for autonomous service brokering

The evolution towards “Software as a Service”, facilitated by various web service technologies, has led to applications composed of a number of service building blocks. These applications are dynamically composed by web service brokers, but rely critically on proper functioning of each of the composing subparts which is not entirely under control of the applications themselves. The problem at hand for the provider of the service is to guarantee non-functional requirements such as service access and performance to each customer. To this end, the service provider typically divides the load of incoming service requests across the available server infrastructure. In this paper we describe an adaptive load balancing strategy called SALSA (Simulated Annealing Load Spreading Algorithm), which is able to guarantee for different customer priorities, such as default and premium customers, that the services are handled in a given time and this without the need to adapt the servers executing the service logic themselves. It will be shown that by using SALSA, web service brokers are able to autonomously meet SLAs, without a priori over-dimensioning resources. This will be done by taking into account a real time view of the requests by measuring the Poisson arrival rates at that moment and selectively drop some requests from default customers. This way the web servers’ load is reduced in order to guarantee the service time for premium customers and provide best effort to default customers. We compared the results of SALSA with weighted round-robin (WRR), nowadays the most used load balancing strategy, and it was shown that the SALSA algorithm requires slightly more processing than WRR but is able to offer guarantees - contrary to WRR - by dynamically adapting its load balancing strategy.     

基于合作中继的OFDM蜂窝网络的QoS感知子载波分配算法*

针对基于中继的OFDM蜂窝网络,提出了一种基于合作中继的QoS感知子载波分配算法,即C-QSA(cooperative QoS-ware subcarrier allocation)算法。C-QSA算法利用基站与中继节点之间的合作传输机制来保证QoS业务的速率要求,允许中继节点进行比特重分配,充分利用无线系统的时变及多用户分集特性,提高无线资源的利用效率。C-QSA算法将子载波分配问题抽象为非线性整数规划,以最大化系统效用为目标,同时满足QoS业务的速率要求。仿真结果表明,C-QSA算法在用户效用及吞吐量等性能方面都有明显优势,系统实际效用接近理论最优值。     

QoS-aware distributed spectrum sharing for heterogeneous wireless cognitive networks

Ubiquitous wireless networking calls for efficient dynamic spectrum allocation (DSA) among heterogeneous users with diverse transmission types and bandwidth demands. To meet user-specific quality-of-service (QoS) requirements, the power and spectrum allocated to each user should lie inside a bounded region in order to be meaningful for the intended application. Most existing DSA methods aim at enhancing the total system utility. As such, spectrum wastage may arise when the system-wise optimal allocation falls outside individual users’ desired regions for QoS provisioning. The goal of this paper is to develop QoS-aware distributed DSA schemes using game-theoretic approach. We derive DSA solutions that respect QoS and avoid naively boosting or sacrificing some users’ utilities to maximize the network spectrum utilization. Specifically, we propose two game-based DSA algorithms: one resorts to proper scaling of the transmission power according to each user’s useful utility range, and the other embeds the QoS factor into the utility function used during gaming. To evaluate DSA schemes from a practical QoS perspective, we introduce two new metrics, namely “system useful utility” and “fraction of QoS-satisfied users”. Simulations confirm that the proposed DSA techniques outperform existing QoS-blind game models in terms of the spectrum sharing efficiency in heterogeneous networks. Convergence analysis of the proposed QoS-aware DSA algorithms is also provided.     

Genetic programming for QoS-aware web service composition and selection

Web services, which can be described as functionality modules invoked over a network as part of a larger application are often used in software development. Instead of occasionally incorporating some of these services in an application, they can be thought of as fundamental building blocks that are combined in a process known as Web service composition. Manually creating compositions from a large number of candidate services is very time consuming, and developing techniques for achieving this objective in an automated manner becomes an active research field. One promising group of techniques encompasses evolutionary computing, which can effectively tackle the large search spaces characteristic of the composition problem. Therefore, this paper proposes the use of genetic programming for Web service composition, investigating three variations to ensure the creation of functionally correct solutions that are also optimised according to their quality of service. A variety of comparisons are carried out between these variations and two particle swarm optimisation approaches, with results showing that there is likely a trade-off between execution time and the quality of solutions when employing genetic programming and particle swarm optimisation. Even though genetic programming has a higher execution time for most datasets, the results indicate that it scales better than particle swarm optimisation.     

A novel heuristic algorithm for QoS-aware end-to-end service composition

Many works have been carried out to find the efficient algorithms for QoS-aware service composition in recent years. Nevertheless, on one hand, some of these works only consider the local QoS attributes in Web services composition; on the other hand, some ideas derived from QoS selection algorithms for network routing are directly applied in service composition without any adaption. A service composition model with end-to-end QoS constraints has been presented in this paper. An improved heuristics HCE based on the observation of characteristic of end-to-end service composition is proposed as a novel solution. Simulation results reveal the better performance of proposed heuristic compared to the other two heuristics, HMCOP and generic CE algorithm.     

Probabilistic approach for QoS-aware recommender system for trustworthy web service selection

We present a QoS-aware recommender approach based on probabilistic models to assist the selection of web services in open, distributed, and service-oriented environments. This approach allows consumers to maintain a trust model for each service provider they interact with, leading to the prediction of the most trustworthy service a consumer can interact with among a plethora of similar services. In this paper, we associate the trust in a service to its performance denoted by QoS ratings instigated by the amalgamation of various QoS metrics. Since the quality of a service is contingent, which renders its trustworthiness uncertain, we adopt a probabilistic approach for the prediction of the quality of a service based on the evaluation of past experiences (ratings) of each of its consumers. We represent the QoS ratings of services using different statistical distributions, namely multinomial Dirichlet, multinomial generalized Dirichlet, and multinomial Beta-Liouville. We leverage various machine learning techniques to compute the probabilities of each web service to belong to different quality classes. For instance, we use the Bayesian inference method to estimate the parameters of the aforementioned distributions, which presents a multidimensional probabilistic embodiment of the quality of the corresponding web services. We also employ a Bayesian network classifier with a Beta-Liouville prior to enable the classification of the QoS of composite services given the QoS of its constituents. We extend our approach to function in an online setting using the Voting EM algorithm that enables the estimation of the probabilities of the QoS after each interaction with a web service. Our experimental results demonstrate the effectiveness of the proposed approaches in modeling, classifying and incrementally learning the QoS ratings.