Reference deployment models for eliminating user concerns on cloud security

Cloud computing has become a hot topic both in research and in industry, and when making decisions on deploying/adopting cloud computing related solutions, security has always been a major concern. This article summarizes security related issues in cloud computing and proposes five service deployment models to address these issues. The proposed models provide different security related features to address different requirements and scenarios and can serve as reference models for deployment.     

面向云服务信息安全评估的度量模型研究

针对云服务信息安全缺乏度量准则和度量不准的问题,介绍了国内外相关度量标准的研究现状。针对可信云的信息安全度量需求,总结了基于可信根的度量模型的优缺点,指出未来的方向是一个兼顾可拓展性、实时性、隐私保护好、易移植且开销更小的云服务信息安全度量模型。     

Leveraging social media networks for classification

Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors?? latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes.     

Chargeback for cloud services

With pay-per-use pricing models, elastic scaling of resources, and the use of shared virtualized infrastructures, cloud computing offers more efficient use of capital and agility. To leverage the advantages of cloud computing, organizations have to introduce cloud-specific chargeback practices. Organizations have to allocate IT service costs to business users in a way that reflects service consumption. To help organizations become effective users of cloud services, this article provides an overview of the factors that influence chargeback in the cloud services. This is an initial work that determines the factors influencing the chargeback in the cloud services. The findings of this research facilitate organizations to realize the implications of the cloud for their chargeback.     

云计算环境下虚拟机部署策略的优化

针对虚拟机群在云计算平台动态部署过程中现有的部署机制所存在的局限性,提出一种优化的部署模型。首先建立预分块存储的分布式预定制虚拟机全镜像模板库;然后由调度中心依据调度策略选择适当的宿主机,并通过多源并行传输的方式提高部署效率;最后由对应的虚拟机监视器完成实例化任务。实验结果显示,在不同的虚拟机群部署规模下,单台虚拟机的平均部署时间较以往缩短3%～45%。实验结果验证了该模型的有效性。     

智能社区云服务平台及服务模式研究

为满足人们对家居系统智能化的要求,提出将云服务应用于智能家居系统,以实现智能家居物联网系统和外部云计算平台的对接,从而形成高度智能化的云枉区,最终达到一站式服务于用户的目的。文中结合云计算技术给出了一种云服务平台的构建方法,该方法可提供不同用户的多种云服务模式,达到用最少的资源提供最多的服务这一目标。     

Trust-based authentication scheme with user rating for low-resource devices in smart environments

In smart environments, pervasive computing contributes in improving daily life activities for dependent people by providing personalized services. Nevertheless, those environments do not guarantee a satisfactory level for protecting the user privacy and ensuring the trust between communicating entities. In this study, we propose a trust evaluation model based on user past and present behavior. This model is associated with a lightweight authentication key agreement protocol (Elliptic Curve-based Simple Authentication Key Agreement). The aim is to enable the communicating entities to establish a level of trust and then succeed in a mutual authentication using a scheme suitable for low-resource devices in smart environments. An innovation in our trust model is that it uses an accurate approach to calculate trust in different situations and includes a human-based feature for trust feedback, which is user rating. Finally, we tested and implemented our scheme on Android mobile phones in a smart environment dedicated for handicapped people.     

SLA guarantees for cloud services

Quality-of-service and SLA guarantees are among the major challenges of cloud-based services. In this paper we first present a new cloud model called SLAaaS — SLA aware Service. SLAaaS considers QoS levels and SLA as first class citizens of cloud-based services. This model is orthogonal to other SaaS, PaaS, and IaaS cloud models, and may apply to any of them. More specifically we make three contributions: (i) we provide a novel domain specific language that allows to describe QoS-oriented SLA associated with cloud services; (ii) we present a general control-theoretic approach for managing cloud service SLA; (iii) we apply the proposed language and control approach to guarantee SLA in various case studies, ranging from cloud-based MapReduce service, to locking service, and higher-level e-commerce service; these case studies successfully illustrate SLA management with different QoS aspects of cloud services such as performance, dependability, financial energetic costs.     

A study on virtual machine deployment for application outsourcing in mobile cloud computing

In mobile cloud computing, application offloading is implemented as a software level solution for augmenting computing potentials of smart mobile devices. VM is one of the prominent approaches for offloading computational load to cloud server nodes. A challenging aspect of such frameworks is the additional computing resources utilization in the deployment and management of VM on Smartphone. The deployment of Virtual Machine (VM) requires computing resources for VM creation and configuration. The management of VM includes computing resources utilization in the monitoring of VM in entire lifecycle and physical resources management for VM on Smartphone. The objective of this work is to ensure that VM deployment and management requires additional computing resources on mobile device for application offloading. This paper analyzes the impact of VM deployment and management on the execution time of application in different experiments. We investigate VM deployment and management for application processing in simulation environment by using CloudSim, which is a simulation toolkit that provides an extensible simulation framework to model the simulation of VM deployment and management for application processing in cloud-computing infrastructure. VM deployment and management in application processing is evaluated by analyzing VM deployment, the execution time of applications and total execution time of the simulation. The analysis concludes that VM deployment and management require additional resources on the computing host. Therefore, VM deployment is a heavyweight approach for process offloading on smart mobile devices.     

Leveraging data aggregation algorithm in LoRa networks

The Journal of Supercomputing - Long Range (LoRa) is an interference-free, single-hop, low-power wide area network (LPWAN) technology. LoRa offers customization of its physical layer parameters...     

Optimal business process deployment cost in cloud resources

Cloud computing is the fastest emerging technology that proposes several resources under various pricing strategies that are specified based on temporal constraints. The main aim of cloud computing is to enhance the performance level and minimize operating costs. Thus, organizations looking towards optimizing their spending on IT infrastructure find such pricing strategies very attractive, especially, to deploy their business process models. However, discovering the optimal deployment cost of a business process in cloud resources proposed under various pricing strategies becomes a highly challenging problem. So, the objective of the present paper is to present an approach that assists business process designers in finding an optimal assignment or scheduling based on the variety of pricing strategies. We use linear programming models with an objective function under a set of constraints. Besides, we propose an extension of the famous cloud simulator provided in the market, CloudSim, to simulate the cloud resources consumed to deploy a business process model. The experimental results show the feasibility, effectiveness, and performance of our approach.

     

Memory allocation algorithm for cloud services

Memory allocation has a major influence on multiuser systems, cloud-based services, virtual machines, and other computer systems. Memory allocation is a process that assigns physical or virtual memory space to programs and services as efficiently and quickly as possible. Economical memory allocation management needs allocation strategies with minimum wastage. In this paper, we introduce a new memory allocation algorithm based on sequential fits and zoning for on-demand (online) cloud services. The memory is divided into multiple zones, where a subgroup of relative request sizes compete in reverse order. We use simulation to compare our new mechanism with existing memory allocation methods that have been deployed using Amazon Elastic Compute Cloud as a test bed. The proposed algorithm is more efficient, and the average saving for the normalized revenue loss is about 7% better than best-fit and 15% better than first-fit memory allocation. In addition, we show that proposed algorithm is robust and faster and has a fairness index that is superior to that of existing techniques.     

云环境下实现容器部署的加速粒子群优化算法

基于容器的微服务部署是一个具有挑战性的问题,为获得更好的用户体验并给云供应商带来更多的利润,需要在降低微服务的故障率和减少响应时间的同时提高资源利用率。提出了一种改进的加速粒子群优化算法,用于解决集群中微服务容器部署的多目标优化问题。该算法通过考虑微服务之间的调用关系,使得容器聚集在一起,从而降低服务的数据传输成本、减少故障率,并提高集群资源利用率。与现有部署算法相比,实验结果表明,所提出的优化算法在服务间的数据传输开销、故障率和资源利用率等性能指标上有明显改善。具体表现在：容器聚集度的提升达到40%以上,数据传输消耗平均有提升4%以上,故障率减少10%～20%,利用率提升3%左右。     

基于云基础设施的服务部署优化问题建模

目前典型的云基础设施提供商所提供的虚拟机资源都是有限的规格类型,当同一个服务部署在不同配置的虚拟机上时,会体现出不同的性能。通过对基于云基础设施的SaaS层服务部署情况进行研究和分析,提出对服务部署策略进行优化的必要性,并将该服务部署优化问题抽象为多目标组合优化问题,对其进行数学建模;然后利用排队论,得出优化指标的计算方法,并进行实验验证。     

Neural networks in quality function deployment

Quality Function Deployment (QFD) is a method of product planning in the early phases of the development of new products (pre-CAD phase). A major drawback of its application is the need to input a large amount of data and the necessity to estimate values on a rather subjective basis in order to complete the House of Quality. This data is plentiful and often designers lack the knowledge with satisfying accuracy. This paper suggests a machine learning approach in which a neural network automatically determines the data by learning from examples. Unlike conventional neural networks which are treated as black boxes, the topology and the weight values are not random but represent real circumstances and can directly be interpreted in the terms of the application. A final section discusses problems arising from the small number of training sets which is usually available in the field of product design.