运用备份服务位置和概率QoS模型的Web服务组合算法

针对工作流服务数的增加过程中最初规划的潜在成本较高以及很多服务组合算法可靠性不足等问题,提出了一种利用备份服务位置和概率服务质量（QoS）模型的服务组合算法。该算法计算服务集群的QoS优化选择,为每个服务包含足够数量的备份服务,且考虑了备份服务位置,以便在单个故障点上进行评估。由于这些备份服务分布均匀,防止了任务失败的发生。对于服务选择问题,采用一种改进的多目标优化（MOO）算法,利用聚类和QoS模型来计算可行解集合。仿真实验采用JMETAL 3.1框架,评估备份服务位置的收益以及算法的可靠性。结果表明,相比于其他MOO算法,提出的算法可靠性更高,从备份服务位置所获得的收益更高。     

Research on user-aware QoS based Web services composition

To improve user experience of composite Web services, a user-aware quality of service (QoS) based Web services composition model is proposed. Under such model, a Web services selection method based on quantum genetic algorithm is proposed. This algorithm uses quantum bit encoding, dynamic step-length quantum gate angle adjustment, neighborhood service search and dynamic punishment strategy to expand search scope and speed up convergence. Simulation experiment shows that this algorithm is more efficient than other existing algorithms in Web services selection.     

基于差异演化算法的QoS全局最优动态Web服务选择

QoS全局最优动态Web服务选择是服务组合中的一个难题。基于差异演化算法,设计一种用于解决该问题的DE-GODSS算法。算法的主要思想是将问题表示为一个带QoS约束的多目标服务组合优化问题,通过理想点的方法将多目标向单目标转化,然后利用差异演化算法的智能优化原理进行算法设计及求解,最终产生一组满足约束条件的优化服务组合流程集。理论分析证明DE-GODSS算法的时间复杂度优于已有的多目标遗传算法,且实验结果表明该算法的收敛速度优于已有的多目标遗传算法。     

A location-aware hybrid web service QoS prediction algorithm

Quality-of-service (QoS) describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determined by some type of QoS prediction method. However, with the dramatic increase in the number of Web services, the prediction failure caused by data sparseness has become a critical challenge. In this paper, a new hybrid user-location-aware prediction based on WAA (HUWAA) is proposed. The implicit neighbor search is optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem is validated in experiments on public real world datasets. The new algorithms outperform the existing IPCC, UPCC and WSRec algorithms.     

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Many sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP‐hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature‐inspired meta‐heuristic techniques are proposed for solving this problem. The nature‐inspired meta‐heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide‐ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature‐inspired meta‐heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature‐inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years.     

QoS-aware computational method for IoT composite service

This paper integrated the quality of service (QoS) requirements of Internet of things (IoT) composite services, and put forward effective decomposition and optimization methods of the QoS metrics. The authors break down the complex QoS calculation model into four basic models, and each model is given a computational method. Combined with the QoS technology of the composite service, the authors adopt the algorithm to find the sub-optimal service with an acceptable cost under the QoS constraints. The proposed algorithm can quickly provide QoS computing services than other two algorithms for large-scale IoT compose services.     

A heuristic algorithm for lightpath scheduling in next-generation WDM optical networks

We study the routing and wavelength assignment (RWA) problem of scheduled lightpath demands (SLDs) in all-optical wavelength division multiplexing networks with no wavelength conversion capability. We consider the deterministic lightpath scheduling problem in which the whole set of lightpath demands is completely known in advance. The objective is to maximize the number of established lightpaths for a given number of wavelengths. Since this problem has been shown to be NP complete, various heuristic algorithms have been developed to solve it suboptimally. In this paper, we propose a novel heuristic RWA algorithm for SLDs based on the bee colony optimization (BCO) metaheuristic. BCO is a newborn swarm intelligence metaheuristic approach recently proposed to solve complex combinatorial optimization problems. We compare the efficiency of the proposed algorithm with three simple greedy algorithms for the same problem. Numerical results obtained by numerous simulations performed on the widely used realistic European Optical Network topology indicate that the proposed algorithm produces better-quality solutions compared to those obtained by greedy algorithms. In addition, we compare the results of the BCO–RWA–SLD algorithm with four other heuristic/metaheuristic algorithms proposed in literature to solve the RWA problem in the case of permanent (static) traffic demands.     

云环境下服务信任感知的可信动态级调度方法

以社会学中的人际关系信任模型为基础,提出了一种基于服务消费者的服务满意度评价、推荐者的服务推荐和第三方服务性能反馈的可信度量模型。将用户对服务资源的信任需求和服务资源的可信度并入DLS算法得到可信动态级调度算法CTDLS,从而在计算调度级别时考虑服务资源的可信程度。模拟实验表明,该算法能有效满足任务在信任方面的服务质量需求,对提高任务调度的成功率具有实际意义。     

QoE/QoS driven simulated annealing-based genetic algorithm for Web services selection

In order to improve the efficiency and effect of Web service selection, an algorithm named quality of experience (QoE)/quality of service (QoS) driven simulated annealing-based genetic algorithm (QQDSGA) is given to achieve Web services selection efficiently with excellent QoE. QQDSGA includes two parts: QoE/QoS driven composite Web services evaluation model and simulated annealing-based (SA-based) genetic algorithm for Web services selection. Simulations show that when used in Web service selection, QQDSGA is better than genetic algorithm (GA) and SA in efficiency and is more satisfied in effect than no customer feedback.     

A new approach for service migration in cloud‐based e‐commerce using an optimization algorithm

Service‐oriented architecture (SOA) has a crucial role in backing productive cloud services. Also, the vast spread of the theoretical notion of diverse businesses (like e‐commerce) into the actual use has been recently applied by cloud computing. The service functionality could be affected by overfilling of the network traffic because of the broadly dispersed nature of e‐commerce in clouds—a key challenge for immediate jobs. Throughout the last decade, a vast range of applications or large‐scale operators has increasingly attracted to migrate the services in clouds. An effective method for accessing the applications throughout standard business hours is continually moving virtual machine containers from one data center to another. Now, with the commonness of cloud computing, many applications have been moved to the cloud fully/partly. It can be handled through the migration of cloud services to diverse platforms in a way that minimizes the communication cost of e‐commerce. As this issue has an NP‐hard nature, in the present article, we present an automatic smart service migration outline through the ant colony optimization (ACO) algorithm on cloud‐oriented e‐commerce. In the presented model, we use the ACO algorithm to take the finest (near‐optimal) service migration decisions. Based on the obtained results, the proposed technique has the optimal number of migrations compared to the existing models.     

Web service selection algorithm based on principal component analysis

Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractical for users to specify quantitative and exact preferences. Moreover, due to that Quality of Service (QoS) attributes are often interrelated, existing Web service selection approaches which employ weighted summation of QoS attribute values to compute the overall QoS of Web services may produce inaccurate results, since they do not take correlations among QoS attributes into account. To resolve these problems, a Web service selection framework considering user’s preference priority is proposed, which incorporates a searching mechanism with QoS range setting to identify services satisfying the user’s QoS constraints. With the identified service candidates, based on the idea of Principal Component Analysis (PCA), an algorithm of Web service selection named PCA-WSS (Web Service Selection based on PCA) is proposed, which can eliminate the correlations among QoS attributes and compute the overall QoS of Web services accurately. After computing the overall QoS for each service, the algorithm ranks the Web service candidates based on their overall QoS and recommends services with top QoS values to users. Finally, the effectiveness and feasibility of our approach are validated by experiments, i.e. the selected Web service by our approach is given high average evaluation than other ones by users and the time cost of PCA-WSS algorithm is not affected acutely by the number of service candidates.     

基于改进遗传算法的C-RAN网络动态无线资源分配

针对云无线网络(Cloud Radio Access Network,C-RAN)中传统静态资源分配效率低下以及动态无线资源分配中资源种类单一的问题,提出了一种基于用户服务质量(Qulity of Service,QoS)约束的动态无线资源分配方案,对无线资源从无线射频单元(Remote Radio Head,RRH)选择、子载波分配和RRH功率分配三个维度进行研究.首先,根据传统的C-RAN系统传输模型和QoS约束在时变业务环境下建立了以发射功率为变量,以吞吐量最大为优化目标的优化问题;然后,基于改进的遗传算法,将原优化方案转变为通过优化RRH选择、子载波分配和RRH功率分配来达到提高系统吞吐量的目的;最后,将改进的遗传算法与其他智能算法在种群规模变化下进行了时间复杂度对比.实验结果表明,所提算法具有较低时间复杂度,所提资源分配方案下的平均吞吐量增益为17％.     

QoS-Aware Service Selection Algorithms for Pervasive Service Composition in Mobile Wireless Environments

The successful application of pervasive services running in mobile wireless networks and devices relies on its ability to provide efficient and cost-effective QoS (Quality of Service) support. This paper proposes a comprehensive QoS model specifically for pervasive services. It considers not only user-perceived factors but also mobile wireless network characteristics. The corresponding formula to calculate each QoS criterion is also devised. In particular, this paper formulates the QoS-aware service selection problem for pervasive service composition and proposes some solutions to the problem, i.e., global-search-based LOSSA (local optimal service selection algorithm) and limited broadcast based LOSSA-k. The evaluation results of the algorithms have shown the effectiveness of the QoS model and the efficiency of the proposed LOSSAs.     

Virtual network function–forwarding graph embedding: A genetic algorithm approach

Network function virtualization (NFV) provides a simple and effective mean to deploy and manage network and telecommunications' services. A typical service can be expressed in the form of a virtual network function–forwarding graph (VNF‐FG). Allocating a VNF‐FG is equivalent to place VNFs and virtual links onto a given substrate network considering resources and quality‐of‐service (QoS) constraints. The deployment of VNF‐FGs in large‐scale networks, such that QoS measures and deployment cost are optimized, is an emerging challenge. Single‐objective VNF‐FGs allocation has been addressed in existing literature; however, there is still a lack of studies considering multiobjective VNF‐FGs allocation. In addition, it is not trivial to obtain optimal VNF‐FGs allocation due to its high computational complexity even in case of single‐objective VNF‐FGs allocation. Genetic algorithms (GAs) have been proved its ability in coping with multiobjective optimization problems; thus, we propose a GA‐based scheme to solve multiobjective VNF‐FGs allocation problem in this paper. The numerical results confirm that the proposed scheme can provide near Pareto‐optimal solutions within a short execution time.     

基于优先级的IEEE802.16系统接纳控制算法研究

鉴于IEEE802.16标准中未对接纳控制机制提出定义,为了提高系统带宽利用率,在分析现有无线网络接纳控制技术的基础上,结合IEEE802.16的具体机制提出了一种基于业务优先级的接纳控制算法。相比于先到先服务的接纳机制,该算法严格区分业务优先级,为不同优先级业务预留带宽,以保证实时业务的服务质量(QoS)要求。利用NS2网络模拟软件对算法进行了仿真和性能评估。结果表明,本接纳控制算法可以较好地保障高优先级业务的实时性,同时在重负载情况下系统带宽利用率有了明显提高。     

WLAN/WPAN环境中模拟退火动态频率选择算法的研究

动态频率选择(DFS)是解决频道实时优化的有力手段，本文针对无线局域网，无线个域网(WLAN／WPAN)等小区域无线多媒体通信系统，首次把模拟退火算法应用到DFS中，针对WLAN／WPAN的情况，给出其处理过程的完整描述，并以HiperLAN／2系统为例进行了仿真。仿真结果表明，与目前已有的算法相比较，新算法实现了整个系统的组合优化，克服了现有算法可能存在的跳转较频繁、性能不够稳定等缺点，能在复杂传播环境下提高WLAN／WPAN的服务质量(QOS)。     

Cluster based wireless sensor network routing using artificial bee colony algorithm

Due to recent advances in wireless communication technologies, there has been a rapid growth in wireless sensor networks research during the past few decades. Many novel architectures, protocols, algorithms, and applications have been proposed and implemented. The efficiency of these networks is highly dependent on routing protocols directly affecting the network life-time. Clustering is one of the most popular techniques preferred in routing operations. In this paper, a novel energy efficient clustering mechanism, based on artificial bee colony algorithm, is presented to prolong the network life-time. Artificial bee colony algorithm, simulating the intelligent foraging behavior of honey bee swarms, has been successfully used in clustering techniques. The performance of the proposed approach is compared with protocols based on LEACH and particle swarm optimization, which are studied in several routing applications. The results of the experiments show that the artificial bee colony algorithm based clustering can successfully be applied to WSN routing protocols.     

基于环境感知的可信QoS评价与服务选取策略

面向服务计算网络中,迫切需要对服务实体的QoS与可信性做出客观的评价.针对服务实体QoS的评价通常只是基于对用户感知到的QoS一种加权,不能客观反映实际网络环境下服务实体所能够提供的QoS问题,论文结合服务实体实际服务组合环境来表征服务实体的QoS,提出了一种基于环境感知的可信QoS评价与服务选取策略:(1)采用不同负载下的QoS特征向量来表征服务实体的QoS;(2)建立一种受限于可信实体的信任推理与演化机制,有效避免信任演化机制中信任泛化问题;(3)基于可信实体演化推导服务实体的可信度与QoS的特征向量;(4)结合当前的服务负载情况,较为客观地评价服务实体实际所能够提供的QoS.论文最后给出了综合的可信QoS评价与服务选取算法,实验结果表明了策略的有效性.     

A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing

The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.     

A novel membrane-inspired multiverse optimizer algorithm for quality of service-aware cloud web service composition with service level agreements

Cloud computing is an emerging technology in computing that provides different services over the Internet. It needs composite services to perform a complex task. Optimal selection of services that provides both functionality and nonfunctionality requirements is an NP-hard problem. This study uses nondeterministic parallel and distributed structures of membrane systems for the recently improved multiverse optimization algorithm to improve the quality of solutions. In the previous membrane-inspired algorithm, the population was divided into subpopulations that evolve different dynamic membranes. This study not only uses a conventional membrane-inspired approach to introduce a conventional membrane-inspired multiverse optimizer (CMIMVO) for the first time but also proposes an algorithm that divides the variables (dimension) into subgroups for different membranes called proposed membrane-inspired multiverse optimizer (PMIMVO). Thus, in PMIMVO, each membrane works on a subgroup to gain global information, which considers the best values obtained by other membranes for other variables. The PMIMVO shows promising results on benchmark function problems. Furthermore, simulation results show that the PMIMVO approach could achieve up to 38% improvement in integrated quality of service (QoS) with attributes including response time, price, availability, and reliability in comparison with the previous approaches, including genetics algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), moth–flame optimization (MFO) improved multiverse optimizer (MVO), and CMIMVO.