基于云计算平台的组合服务质量信任度建模

随着云计算的发展,多种基于云计算的服务平台应用而生。平台为了更好地满足用户需求,进行平台服务组合,同时在开放的环境中,部分组合服务平台,为了获得为客户服务的机会,不惜造假欺骗客户,给正规组合服务平台以及客户带来了困扰,因此提出基于云计算平台的组合服务质量信任度建模。通过研究基于云计算平台的组合服务,设置平台的组合服务质量信任度评估机制。依托评估机制,进行平台的组合服务质量信任度建模,最后通过服务可信度获取以及处理,完成基于云计算平台的组合服务质量信任度建模研究。实验分析证明,基于云计算平台的组合服务质量信任度建模,相比于传统服务质量信任度评估方法,能够将云服务属性的信任度识别有效性提高18.5%,能够有效识别平台不可信的服务属性,具有切实可行性。

面向服务组合质量的物联网绿色能源管理

随着物联网技术的迅速发展,物联网环境中服务组合的能源消耗是目前有待研究的一个关键问题。当前物联网环境下服务组合的问题大多集中在基于服务质量(QoS)的评估研究,忽略了服务组合动态配置过程中的总体能源消耗。因此,提出面向物联网环境下服务组合的QoS评估模型和能源评估模型。考虑到物联网环境下的服务组合是NP难问题,将飞蛾算法(MFO)成功运用到QoS评估模型和能源评估模型。实验结果表明,MFO在上述模型中都呈现出较好的优化效果,从而实现服务组合质量在物联网环境下的绿色能源管理。

地理空间信息服务质量评价的概念与方法

本文在综合商业服务质量评价模型与软件质量评价模型的基础上,对地理空间信息服务质量评价的一般过程与方法进行了分析。首先,对地理空间信息服务的质量评价的基本概念与内涵进行了分析;其次,对服务执行质量评价与服务实体质量评价进行了比较;然后,对原子地理空间信息服务质量评价中的度量方法进行了探讨;最后,提出了5个地理空间信息服务的基本组合模型,并对几个关键质量要素的组合算法进行了分析。

Web services composition with QoS bound based on simulated annealing algorithm

In order to enable quality-aware web services selection in the process of service composition,this paper first describes the non-functional requirements of service consumers and the quality of elementary service or composite service as a quality vector,and then models the QoS（quality of service）-aware composition as a multiple criteria optimization problem in extending directed graph.A novel simulated annealing algorithm for QoS-aware web services composition is presented.A normalizing for composite service QoS values is made,and a secondary iterative optimization is used in the algorithm.Experimental results show that the simulated annealing algorithm can satisfy the multiple criteria and global QoS requirements of service consumers.The algorithm produces near optimum solution with much less computation cost.

QoS-oriented service composition based on mapping relation tree

Service composition is a hot and active research area in service-oriented computing which has gained great momentum. An quality of service (QoS) oriented and tree-based approach was proposed to implement service composition efficiently. Firstly, service descriptions were transformed to mapping relations which denote the association between input and output concepts. Then, the service composition problems were resolved by building mapping relation tree dynamically based on the divide and conquer method, and all mapping relation trees were combined without redundant branch to obtain the composition scheme. Finally, the optimal composition scheme was chosen based on quality of service attributes including the preference of service request. Experiment results illustrate that this method can improve the composition efficiency and reduce the searching time by increasing the number of services in repository.

Pre-filtering based automatic composition of multi-QoS semantic web services

To support the rapid automatic services composition and fulfill multi-quality of service （multi- QoS） demand, we propose a novel approach to realize services composition automatically by a prefihering process. Aimed at a set of web services with similar functionality and different quality of service （QoS） , a semantic services chain is given and a corresponding constructing algorithm is proposed to construct the data structure. A pre-filtering process is put forward to find whether a composition service before planning exists. It can avoid aborted planning. An optimal planning algorithm is proposed which can choose the most suitable service from a lot of similar candidate services based on semantic service chains and multi-QoS values. The algorithms can improve the correctness and automation performances of automated semantic web services composition. As an example, a concrete composite process is analyzed. Experimental results show the validity of the composite process.

Multi-Strategy Improvement of Sparrow Search Algorithm for Cloud Manufacturing Service Composition

In existing research,the optimization of algorithms applied to cloud manufacturing service composition based on the quality of service often suffers from decreased convergence rates and solution quality due to single-population searches in fixed spaces and insufficient information exchange.In this paper,we introduce an improved Sparrow Search Algorithm(ISSA)to address these issues.The fixed solution space is divided into multiple subspaces,allowing for parallel searches that expedite the discovery of target solutions.To enhance search efficiency within these subspaces and significantly improve population diversity,we employ multiple group evolution mechanisms and chaotic perturbation strategies.Furthermore,we incorporate adaptive weights and a global capture strategy based on the golden sine to guide individual discoverers more effectively.Finally,differential Cauchy mutation perturbation is utilized during sparrow position updates to strengthen the algorithm's global optimization capabilities.Simulation experiments on benchmark problems and service composition optimization problems show that the ISSA delivers superior optimization accuracy and convergence stability compared to other methods.These results demonstrate that our approach effectively balances global and local search abilities,leading to enhanced performance in cloud manufacturing service composition.