TOPVISOR: Two‐level controller‐based approach for service advertisement and discovery in vehicular cloud network

Vehicular cloud is a kind of mobile cloud in which vehicles share their resources and provide services for each other. The first step in establishing a vehicular cloud network is the service advertisement and discovery. Due to the dynamic nature of vehicular cloud networks, services are not continuous and the service location may vary at any time depending on the vehicle location. Therefore, higher network traffic is generated to access the consistent and up‐to‐date information. In this paper, a two‐level hierarchical approach is proposed for service advertisement and discovery in vehicular cloud networks. To register the services' specification in this approach, the distributed directories in RSUs and central controllers are used. Moreover, a method is used to avoid extra update packets by localizing the updates. The simulation results show improvement in the packet delivery rate as well as a reduction in transmission bandwidth preventing the network congestion.     

Enhanced security‐aware technique and ontology data access control in cloud computing

Nowadays, security and data access control are some of the major concerns in the cloud storage unit, especially in the medical field. Therefore, a security‐aware mechanism and ontology‐based data access control (SA‐ODAC) has been developed to improve security and access control in cloud computing. The model proposed in this research work is based on two operational methods, namely, secure awareness technique (SAT) and ontology‐based data access control (ODAC), to improve security and data access control in cloud computing. The SAT technique is developed to provide security for medical data in cloud computing, based on encryption, splitting and adding files, and decryption. The ODAC ontology is launched to control unauthorized persons accessing data from storage and create owner and administrator rules to allow access to data and is proposed to improve security and restrict access to data. To manage the key of the SAT technique, the secret sharing scheme is introduced in the proposed framework. The implementation of the algorithm is performed by MATLAB, and its performance is verified in terms of delay, encryption time, encryption time, and ontology processing time and is compared with role‐based access control (RBAC), context‐aware RBAC and context‐aware task RBAC, and security analysis of advanced encryption standard and data encryption standard. Ultimately, the proposed data access control and security scheme in SA‐ODAC have achieved better performance and outperform the conventional technique.     

An efficient authentication system of smart device using multi factors in mobile cloud service architecture

Mobile cloud computing environments have overcome the performance limitation of mobile devices and provide use environments not restricted by places. However, user information protection mechanisms are required because of both the security vulnerability of mobile devices and the security vulnerability of cloud computing. In this paper, a multifactor mobile device authentication system is proposed to provide safety, efficiency, and user convenience for mobile device use in cloud service architectures. This system improves security by reinforcing the user authentication required before using cloud computing services. Furthermore, to reinforce user convenience, the system proposed increases the strength of authentication keys by establishing multiple factors for authentication. For efficient entries in mobile device use environments, this system combines mobile device identification number entries, basic ID/password type authentication methods, and the authentication of diverse user bio‐information. This system also enhances authentication efficiency by processing the authentication factors of a user's authentication attempt in a lump instead of one by one in the cloud computing service environment. These authentication factors can be continuously added, and this authentication system provides authentication efficiency even when authentication factors are added. The main contribution is to improve high security level by through authentication of mobile devices with multifactors simultaneously and to use the mobile cloud service architecture for its efficient processing with respect to execution time of it. Copyright © 2013 John Wiley & Sons, Ltd.     

OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

Cloud is a multitenant architecture that allows the cloud users to share the resources via servers and is used in various applications, including data classification. Data classification is a widely used data mining technique for big data analysis. It helps the learners to discover hidden data patterns by training massive data collected from the real world. Because this trained model is the private asset of an entity, it should be protected from all other noncollaborative entities. Therefore, it is essential to take effective measures to preserve the confidential data. The objective of this paper is to preserve the privacy of the confidential data in the cloud environment by introducing the medical data classification method. In view of that, this paper presents a method for medical data classification using a novel ontology and whale optimization‐based support vector machine (OW‐SVM) approach. Initially, privacy‐preserved data are developed adopting Kronecker product bat approach, and then, ontology is built for the feature selection process. Ontology and whale optimization‐based support vector machine is then proposed by integrating ontology and whale optimization algorithm into SVM, in which ontology and whale optimization algorithm is used for the feasible selection of kernel parameters. The experiment is done using 3 heart disease datasets, such as Cleveland, Switzerland, and Hungarian. In a comparative analysis, the performance of the OW‐SVM approach is compared with that of K‐nearest neighbor, Naive Bayes, decision tree, SVM, and OW‐SVM, using accuracy, sensitivity, specificity, and fitness, as the evaluation metrics. The OW‐SVM approach could achieve maximum performance with accuracy of 83.21%, the sensitivity of 91.49%, specificity of 73%, and fitness of 81.955, outperforming existing comparative techniques.     

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

One of the most critical issues in using service‐oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP‐hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust‐based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest‐guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large‐scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.     

云计算环境下基于信任演化及集合的服务选择

针对云计算环境中服务节点的可信度参差不齐常导致用户很难获得高质量组合服务的问题,提出了一种基于信任生成树的云服务组织方法,将服务提供者与请求者的交互行为经演化后形成信任关系,使主体间可信程度达到相应级别,形成对外提供相似服务功能的云服务集合,将恶意、虚假的服务排除在信任生成树之外,使服务组合在可信场景中进行;在此基础上,采用了基于信息熵的度量策略来对服务间的信任关系进行评估,解决了现有研究中仅对可信参数进行简单加权分析的不足。实验分析表明,该方法能有效抑制云计算环境下恶意节点的欺诈行为并保护真实节点的合法利益,具有较好的服务选择质量。     

基于云环境下的高性能服务平台研究与应用

云计算的核心部分是平台即服务(Platfromasa Service,Paa S),Paa S是以服务的方式提供计算平台和软件组合。文中提出一种高性能服务平台,介绍了高性能服务平台的体系结构、服务接口、用户与服务管理,重点分析了云调度服务、云统一授权服务、云消息服务的工作方法。     

基于云计算的综合信息化管理系统架构

综合信息化管理是现阶段信息化发展的趋势,云计算是目前合理利用资源加快信息化进程技术的研究热点.针对现阶段信息化发展的需求,在分析和研究云计算和信息化管理系统特点的基础上,提出了基于云计算的综合信息化管理系统架构,为信息化管理提供一种有效的建设方案.     

Penalty‐aware and cost‐efficient resource management in cloud data centers

Today, data centers are the main source of providing cloud services through a service level agreement (SLA). Most research papers for cloud resource management concentrate on how to reduce host energy consumption and SLA violation (SLAV) to minimize operational cost. However, they do not consider the amount of penalty that cloud provider should pay to users because of SLAV. In this paper, we propose a new penalty‐aware and cost‐efficient method that considers cloud resource management as a cost problem. In this method parameters such as user budget, penalty, and host energy consumption cost play an important role in minimizing operational cost which leads to higher profit for cloud provider. The simulation results with CloudSim show that our proposed method minimizes operational cost compared to the prior resource managements. Copyright © 2016 John Wiley & Sons, Ltd.     

An architecture of knowledge management system based on agent and ontology

To solve the problems in knowledge management system （KMS）, such as information sharing, the ability to extend and re-engineer, and the reusable ability of legacy systems in distributed and heterogeneous environments. This article presents a method based on agent and ontology of designing KMS. This method consists of two agencies. One is knowledge agency with three agents supporting knowledge management process. The other is application agency with three agents supporting knowledge application. In this method, ontology is used to represent the knowledge in knowledge base and the content in the message exchanged among agents. To demonstrate the advantages of this method, experiments have been carried out and the results imply that this method is efficient and effective for small and medium-size enterprises to design KMS.     

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

With the rapid development of cloud computing, the number of cloud users is growing exponentially. Data centers have come under great pressure, and the problem of power consumption has become increasingly prominent. However, many idle resources that are geographically distributed in the network can be used as resource providers for cloud tasks. These distributed resources may not be able to support the resource‐intensive applications alone because of their limited capacity; however, the capacity will be considerably increased if they can cooperate with each other and share resources. Therefore, in this paper, a new resource‐providing model called “crowd‐funding” is proposed. In the crowd‐funding model, idle resources can be collected to form a virtual resource pool for providing cloud services. Based on this model, a new task scheduling algorithm is proposed, RC‐GA (genetic algorithm for task scheduling based on a resource crowd‐funding model). For crowd‐funding, the resources come from different heterogeneous devices, so the resource stability should be considered different. The scheduling targets of the RC‐GA are designed to increase the stability of task execution and reduce power consumption at the same time. In addition, to reduce random errors in the evolution process, the roulette wheel selection operator of the genetic algorithm is improved. The experiment shows that the RC‐GA can achieve good results.     

基于云计算的高校信息化资源服务区域经济体系框架构建

本文阐述了云计算的定义，分析了基于云计算的高校信息化资源在服务区域经济中的优势。同时，深入探讨了基于云计算的高校信息化资源服务区域经济框架及相关硬件配置，具有一定的参考价值和实际应用意义。     

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.     

Quality‐of‐service‐aware cluster‐based service discovery approach for mobile ad hoc networks

This paper presents a quality‐of‐service (QoS) distributed service discovery approach for mobile ad hoc network environments. The approach builds upon a clustered topology, where the clusterhead (CH) is assigned additional roles having to do with maintaining a directory of services in the network and aggregating and computing QoS scores about service providers (SPs) from requesting nodes (RNs) and the providers themselves. To reduce the amount of overhead traffic, the design makes extensive use of piggybacking for relaying and updating the CHs with QoS scores. A mobile device that is interested in a certain service submits a request to its CH, which uses cached QoS data to return a ranked list of SPs that offer the type of requested service. On the basis of its interaction with the SP, the device sends the CH next time it makes a request a score reflecting its perception of the SP's QoS. Over time, the CH develops robust QoS data that it uses to help devices obtain the best available service. Theoretical analysis and experimental evaluation based on simulations prove the advantages of the proposed system and the effectiveness of its operations. Copyright © 2013 John Wiley & Sons, Ltd.     

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA). Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well‐known NP‐hard problem. To resolve this problem, this paper proposes an energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization.     

LARPA: A learning automata‐based resource provisioning approach for massively multiplayer online games in cloud environments

Nowadays, with the development of communication systems, massively multiplayer online games (MMOGs) have become very popular. In these games, the players all over the world dynamically interact with each other by sending play actions such as shootings, movements, or chatting in the form of MMOG sessions in real time through a large‐scale distributed environment. Leveraging affordable cloud computing to host such services is a widely investigated issue. It is because the arrival rate of players to the game environment has to make fluctuations, and the players expect services to be always available with an acceptable quality of service (QoS), especially in terms of the response time. Therefore, the dynamic provisioning of resources in order to deal with fluctuating demands due to variability in the arrival rate of players of the MMOG services is highly recommended. In this paper, we propose a learning‐based resource provisioning approach for MMOG services that is based on the combination of the autonomic computing paradigm and learning automata (LA). The remarkable performance of the proposed approach in terms of response time, cost, and allocated virtual machines (VMs) is assessed through simulation and comparison with the existing approaches.     

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.     

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 location‐based service advertisement algorithm for pervasive service discovery in wireless mobile networks

The practical success of pervasive services running in mobile wireless networks relies largely on its flexibility in providing adaptive and cost‐effective services. Service discovery is an essential mechanism to achieve this goal. As an enhancement to our previous work for service discovery, that is, model‐based service discovery (MBSD), this paper proposes a location‐based service advertisement (SA) algorithm named as MBSD‐sa. MBSD‐sa advocates the importance of service location to the service availability and integrates the service location information together with the service semantic information into service information for advertisement. MBSD‐sa utilizes prediction to estimate the service location so as to reduce the number of SA messages (SAMs). Two complementary types of SA mechanisms (Types 1 and 2) are employed by MBSD‐sa to strike the balance between the SAM overhead and the accuracy of service information. The performance of MBSD‐sa is analyzed both numerically and using simulations. Copyright © 2008 John Wiley & Sons, Ltd.     

An energy‐saving algorithm for cloud resource management using a Kalman filter

Cloud computing has emerged as a promising technique to provide storage and computing component on‐demand services over a network. In this paper, we present an energy‐saving algorithm using the Kalman filter for cloud resource management to predict the workload and to further achieve high resource availability with low service level agreement. Using the proposed algorithm, one can estimate the potential future workload trend then predict the computing component workload utilizations and further retrench energy consumption and achieve load balancing in a cloud system. Experimental results show that the proposed algorithm achieves more than 92.22% accuracy in the computing component workload prediction, improves 55.11% energy in energy consumption, and has 3.71% in power prediction error rate, respectively. Copyright © 2013 John Wiley & Sons, Ltd.