面向云联网的云服务协商机制

随着云计算的快速发展,越来越多的用户开始使用云服务提供商提供的服务,而云联网作为云计算研究的新领域,可以实现跨云服务提供商的服务,当单个云服务提供商无法满足用户的服务需求时,云服务提供商之间以合作的方式为用户提供服务,以便更好地满足用户的服务需求。针对上述情况,提出了面向云联网的云服务协商机制,该机制利用云联网和改进的经典合同网模型来实现云服务提供商的交互协商。为了有效地选出合作伙伴以提高合作效率,还为每一个云服务提供商建立了一个熟人集。实验表明,本文设计的机制可以有效地提高云服务提供商之间的合作效率,并且可以更好地满足用户的服务需求。     

云环境下基于服务等级协议的信任评估模型

针对用户在交互过程中对云服务商缺乏信任的问题,提出了一个基于服务等级协议(SLA)的云计算信任模型。在该模型中,云服务商在向服务中心--可信的第三方平台注册时,首先提交自身的实力评估报告,说明其实力、运营、技术及提供的服务属性等,服务中心根据相关的评价标准对该云服务商进行评估,得到系统信任;其次把系统信任引入到传统的声誉机制中,把系统信任、直接信任和间接信任作为评估一个云服务提供商的三个重要因素,并计算出云服务商的综合信任度;最后用户根据云服务商提供的服务和综合信任度与其进行SLA协商,确定最终交互对象,屏蔽掉不诚实或信誉较低的云服务商。实验结果表明,在基于服务等级协议的信任模型中,由于引入了系统信任,云服务商综合信誉的获得更全面准确、有效地防止了云服务商的不诚信行为,提高了交互的成功率。     

A cloud service selection model using improved ranked voting method

Cloud computing is an upcoming and promising solution for utility computing that provides resources on demand. As it has grown into a business model, a large number of cloud service providers exist today in the cloud market, which further is expanding exponentially. Many cloud service providers, with almost similar functionality, pose a selection problem to the cloud users. To assist the users in the best service selection, as per its requirement, a framework has been developed in which users list their quality of service (QoS) expectation, while service providers express their offerings. Experience of the existing cloud users is also taken into account in order to select the best cloud service provider. This work identifies some new QoS metrics, besides few existing ones, and defines it in a way that eases both the user and the provider to express their expectations and offers, respectively, in a quantified manner. Further, a dynamic and flexible model, using a variant of ranked voting method, is proposed that considers users' requirement and suggests the best cloud service provider. Case studies affirm the correctness and the effectiveness of the proposed model. Copyright © 2016 John Wiley & Sons, Ltd.     

Formulating and managing viable SLAs in cloud computing from a small to medium service provider's viewpoint: A state-of-the-art review

In today's competitive world, service providers need to be customer-focused and proactive in their marketing strategies to create consumer awareness of their services. Cloud computing provides an open and ubiquitous computing feature in which a large random number of consumers can interact with providers and request services. In such an environment, there is a need for intelligent and efficient methods that increase confidence in the successful achievement of business requirements. One such method is the Service Level Agreement (SLA), which is comprised of service objectives, business terms, service relations, obligations and the possible action to be taken in the case of SLA violation. Most of the emphasis in the literature has, until now, been on the formation of meaningful SLAs by service consumers, through which their requirements will be met. However, in an increasingly competitive market based on the cloud environment, service providers too need a framework that will form a viable SLA, predict possible SLA violations before they occur, and generate early warning alarms that flag a potential lack of resources. This is because when a provider and a consumer commit to an SLA, the service provider is bound to reserve the agreed amount of resources for the entire period of that agreement – whether the consumer uses them or not. It is therefore very important for cloud providers to accurately predict the likely resource usage for a particular consumer and to formulate an appropriate SLA before finalizing an agreement. This problem is more important for a small to medium cloud service provider which has limited resources that must be utilized in the best possible way to generate maximum revenue. A viable SLA in cloud computing is one that intelligently helps the service provider to determine the amount of resources to offer to a requesting consumer, and there are number of studies on SLA management in the literature. The aim of this paper is two-fold. First, it presents a comprehensive overview of existing state-of-the-art SLA management approaches in cloud computing, and their features and shortcomings in creating viable SLAs from the service provider's viewpoint. From a thorough analysis, we observe that the lack of a viable SLA management framework renders a service provider unable to make wise decisions in forming an SLA, which could lead to service violations and violation penalties. To fill this gap, our second contribution is the proposal of the Optimized Personalized Viable SLA (OPV-SLA) framework which assists a service provider to form a viable SLA and start managing SLA violation before an SLA is formed and executed. The framework also assists a service provider to make an optimal decision in service formation and allocate the appropriate amount of marginal resources. We demonstrate the applicability of our framework in forming viable SLAs through experiments. From the evaluative results, we observe that our framework helps a service provider to form viable SLAs and later to manage them to effectively minimize possible service violation and penalties.     

Resource Management in Cloud Computing with Optimal Pricing Policies

As a new computing paradigm, cloud computing has received much attention from research and economics fields in recent years. Cloud resources can be priced according to several pricing options in cloud markets. Usage-based and reserved pricing schemes are commonly adopted by leading cloud service providers (CSPs) such as Amazon and Google. With more and more CSPs entering cloud computing markets, the pricing of cloud resources is an important issue that they need to consider. In this paper, we study how to segment cloud resources using hybrid pricing schemes in order to obtain the maximum revenue by means of optimal pricing schemes in what is a largely monopolized cloud market. We first study how the revenue of a cloud provider can be maximised using an on-demand pricing scheme. We then turn to the study of revenue maximization with a reserved pricing scheme and, finally, we compare the revenues obtained from the two pricing schemes.     

基于市场机制的云服务管理研究

云计算以其几乎无限的计算能力、存储能力和带宽,成为很多企业和组织研究和使用的最佳选择。为了实现云服务的商业化,云市场的出现是必要的。由于云市场中出现的服务类型和数量日益增加,如何帮助用户选择合适的提供商是个很大的挑战。文中提出一个以云市场为基础的机制,并给出一个推荐系统来管理云服务。该管理机制一定程度上节省了用户和供应商选择交易对象的时间,并且能够很好地满足用户的服务要求和预算成本,提高了整个云市场的效率。     

IaaS云服务定价机制的仿真研究

定价机制是影响云计算用户利益和云服务提供商收益的关键因素。本文以IaaS云服务定价机制为研究对象,首先将IaaS云服务定价机制分为固定定价和动态定价2大类,其中固定定价细分为即用即付费和预订定价2种,从理论上分析2类定价机制对IaaS云服务提供商收益的影响;然后,分别建立即用即付费和预订定价机制下IaaS云服务提供商的收益模型,并用启发式算法构建动态定价机制下IaaS云服务提供商的收益模型;最后,基于Repast Simphony建立多主体仿真模型,比较分析3种不同定价机制下IaaS云服务提供商的收益。研究结果表明,随着服务时长和顾客数量的增加,动态定价机制更能为IaaS云服务提供商带来更多收益,同时IaaS云服务提供商可以通过改变服务等级和资源价格等相关参数,改变顾客行为,使顾客数量增加,从而提高收益。     

An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider

The number of cloud service users has increased worldwide, and cloud service providers have been deploying and operating data centers to serve the globally distributed cloud users. The resource capacity of a data center is limited, so distributing the load to global data centers will be effective in providing stable services. Another issue in cloud computing is the need for providers to guarantee the service level agreements (SLAs) established with consumers. Whereas various load balancing algorithms have been developed, it is necessary to avoid SLA violations (e.g., service response time) when a cloud provider allocates the load to data centers geographically distributed across the world. Considering load balancing and guaranteed SLA, therefore, this paper proposes an SLA-based cloud computing framework to facilitate resource allocation that takes into account the workload and geographical location of distributed data centers. The contributions of this paper include: (1) the design of a cloud computing framework that includes an automated SLA negotiation mechanism and a workload- and location-aware resource allocation scheme (WLARA), and (2) the implementation of an agent-based cloud testbed of the proposed framework. Using the testbed, experiments were conducted to compare the proposed schemes with related approaches. Empirical results show that the proposed WLARA performs better than other related approaches (e.g., round robin, greedy, and manual allocation) in terms of SLA violations and the provider’s profits. We also show that using the automated SLA negotiation mechanism supports providers in earning higher profits.     

云外包服务提供商竞争力评价研究

云外包作为目前外包服务的趋势,已经得到了产业界的普遍关注。有效利用云计算技术提供外包服务是外包服务提供商抓住云计算发展契机,提高自身竞争力的关键,因此云外包服务提供商竞争力的评价研究是必要且重要的。在云计算技术背景下,分析了影响云外包服务提供商竞争力的因素,并以此为基础构建了基于灰色聚类的评价模型,旨在为客户企业合适的云外包服务提供商提供参考,也为云外包服务提供商提高自身竞争力提供一定的依据。     

A survey on the service interoperability in cloud computing: Client-centric and provider-centric perspectives

The vendor lock-in is a prominent issue in cloud computing. It is caused by cloud providers who offer proprietary services, which hinders the cloud interoperability. Client-centric interoperability enables the migration of the data and applications across clouds; it gives the clients control over their workloads and a wider range of service choices. Whereas, provider-centric interoperability allows the providers to collaborate. Thus, providers, who have spare resources, can lend them to other providers who lack computational or storage capabilities to overcome the limitations of their local resources. In this article, we conduct a survey to differentiate between client- and provider-centric interoperability solutions. We aim to provide an up-to-date analysis of the current tendencies and the neglected areas of the cloud interoperability field. Thus, we study the cloud service interoperability evolution through the years. Furthermore, we propose definitions for the intra-cloud and inter-cloud interoperability. Moreover, we propose a taxonomy to classify the cloud interoperability approaches into client-centric and provider-centric categories. Then, for each category, we classify the approaches based on their interoperability environment into single cloud or interconnected clouds. Finally, we analyze and compare the approaches based on multiple criteria. The study reveals the focus on the client-centric solutions and the interoperability in interconnected clouds. We notice more interest in the data and application levels interoperability, mainly, in infrastructure as a service model. We also find that client-centric solutions are, mostly, semantic technologies and brokers. However, provider-centric solutions are middleware, protocols, and standards. We conclude that a generic cloud service interoperability model is needed.     

公有云中针对服务提供商的用户隐私保护

用户隐私保护一直是影响云计算推广的重要问题。当前,针对云服务提供商的用户隐私保护研究还只局限于少数特定领域,没有一个较为通用的方案。这严重阻碍了用户对云服务提供商及云计算服务模式的信任。为解决这一问题,首先分析了云环境中信息泄露的特点,并根据云计算服务及模型的特点,引出了一个让云服务各层或各模块相互分离、相互制约的用户隐私保护思路。随后沿用该思路,提出了一套基于PaaS层和SaaS层分离的完整隐私保护方案,让PaaS层和SaaS层服务有不同的云服务商分别提供,并让云服务商在提供服务的同时,根据相应规范限制对方泄露用户隐私。最后,对该方案进行了详细的安全性分析论证,并采用一个实际例子说明了该方案在保护用户隐私中的作用。该方案能在一般的云计算架构中实行,可通用于各类SaaS服务中,具有较强的理论和应用价值。     

A price- and-time-slot-negotiation mechanism for Cloud service reservations

When making reservations for Cloud services, consumers and providers need to establish service-level agreements through negotiation. Whereas it is essential for both a consumer and a provider to reach an agreement on the price of a service and when to use the service, to date, there is little or no negotiation support for both price and time-slot negotiations (PTNs) for Cloud service reservations. This paper presents a multi-issue negotiation mechanism to facilitate the following: 1) PTNs between Cloud agents and 2) tradeoff between price and time-slot utilities. Unlike many existing negotiation mechanisms in which a negotiation agent can only make one proposal at a time, agents in this work are designed to concurrently make multiple proposals in a negotiation round that generate the same aggregated utility, differing only in terms of individual price and time-slot utilities. Another novelty of this work is formulating a novel time-slot utility function that characterizes preferences for different time slots. These ideas are implemented in an agent-based Cloud testbed. Using the testbed, experiments were carried out to compare this work with related approaches. Empirical results show that PTN agents reach faster agreements and achieve higher utilities than other related approaches. A case study was carried out to demonstrate the application of the PTN mechanism for pricing Cloud resources.     

基于虚拟机部署策略的云平台容错即服务方法

针对如何充分利用云基础架构层资源,满足上层云应用系统租户对应用系统容错的需求多样性和高可靠性要求的问题,提出一种面向租户和云服务提供商的、基于虚拟机部署策略的云平台容错即服务方法。该方法根据租户的特定容错需求适配适合的容错方法及容错级别,据此计算并最优化云服务提供商的收益和资源使用量,在此基础上对提供容错服务的虚拟机进行优化部署,充分利用底层虚拟机资源为租户的云应用系统提供更为可靠的容错服务。实验结果表明,所提方法能够在保障云服务提供商收益的基础上,为多租户云应用系统实现更灵活且可靠性更高的容错服务。     

Reliability and Incentive of Performance Assessment for Decentralized Clouds

Decentralized cloud platforms have emerged as a promising paradigm to exploit the idle computing resources across the Internet to catch up with the ever-increasing cloud computing demands. As any user or enterprise can be the cloud provider in the decentralized cloud, the performance assessment of the heterogeneous computing resources is of vital significance. However, with the consideration of the untrustworthiness of the participants and the lack of unified performance assessment metric, the performance monitoring reliability and the incentive for cloud providers to offer real and stable performance together constitute the computational performance assessment problem in the decentralized cloud. In this paper, we present a robust performance assessment solution RODE to solve this problem. RODE mainly consists of a performance monitoring mechanism and an assessment of the claimed performance (AoCP) mechanism. The performance monitoring mechanism first generates reliable and verifiable performance monitoring results for the workloads executed by untrusted cloud providers. Based on the performance monitoring results, the AoCP mechanism forms a unified performance assessment metric to incentivize cloud providers to offer performance as claimed. Via extensive experiments, we show RODE can accurately monitor the performance of cloud providers on the premise of reliability, and incentivize cloud providers to honestly present the performance information and maintain the performance stability.     

云计算下基于CRP算法的资源提供策略

云计算资源管理系统是用于接收来自云计算用户的资源请求,并且把特定的资源封装为服务提供给资源请求者。在云计算环境下,如何为资源请求者选择合适的资源是一个值得研究的课题。文中通过对云计算下现有的资源提供策略的分析,同时根据不同云提供者提供的计算资源的成本不同的特点,综合考虑资源的计算能力、可靠性和单位成本三点因素,提出了云计算下基于CRP算法的资源提供策略。这种资源提供策略既能提供满足用户资源请求的服务,也能降低云服务提供者的运营成本,从而获得更大收益。     

基于凸函数证据理论的关联感知云服务信任模型

针对大规模分布式云计算系统中的服务可信度辨别问题,提出一种基于凸函数证据理论的关联感知云服务信任模型。对云计算系统中云服务提供商、服务和用户之间的信任关系进行形式化描述,充分挖掘了同一服务商中的不同云服务之间的关联性,利用凸函数证据理论对有序命题的处理能力,构建了云计算系统中的可信服务推荐方法,根据用户需求为其提供合理可靠的云服务。与经典证据理论方法的对比结果表明,基于凸函数证据理论的关联感知云服务信任模型在保证有效性和健壮性的同时,充分利用了云计算系统中云服务之间的关联信息,能够根据用户的请求提供合理的云服务。     

一种成本相关的云提供商数据可靠性保证算法

数据可靠性保证和容灾成本控制对云提供商而言是一个相互矛盾的问题.在分析已有数据保障机制的基础上,设计了一个基于多个云平台的分布式富云容灾模型,利用富云容灾系统,私有云提供商可以借用其他云平台的虚拟资源对自身数据进行冗余备份.为了减少数据传输的响应时间,富云容灾模型通过设置多个地理位置隔离的富云代理实现云平台用户任务的分配,减少私有云平台的工作负荷.针对富云容灾系统的成本优化和数据可靠性保证问题,提出了一种成本相关的云计算服务数据可靠性保证算法CAHRPA.该算法根据数据传输带宽和容灾费用在多个云平台中动态选择数据副本的存放位置,从而以一种成本优化的方式为云提供商提供数据容灾方案.实验结果表明,CAHRPA 能够在保证数据可靠性的同时,实现一种成本更低的数据容灾策略.     

A cost‐efficient resource provisioning algorithm for DHT‐based cloud storage systems

Personal cloud storage provides users with convenient data access services. Service providers build distributed storage systems by utilizing cloud resources with distributed hash table (DHT), so as to enhance system scalability. Efficient resource provisioning could not only guarantee service performance, but help providers to save cost. However, the interactions among servers in a DHT‐based cloud storage system depend on the routing process, which makes its execution logic more complicated than traditional multi‐tier applications. In addition, production data centers often comprise heterogeneous machines with different capacities. Few studies have fully considered the heterogeneity of cloud resources, which brings new challenges to resource provisioning. To address these challenges, this paper presents a novel resource provisioning model for service providers. The model utilizes queuing network for analysis of both service performance and cost estimation. Then, the problem is defined as a cost optimization with performance constraints. We propose a cost‐efficient algorithm to decompose the original problem into a sub‐optimization one. Furthermore, we implement a prototype system on top of an infrastructure platform built with OpenStack. It has been deployed in our campus network. Based on real‐world traces collected from our system and Dropbox, we validate the efficiency of our proposed algorithms by extensive experiments. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimizing virtual machine allocation for parallel scientific workflows in federated clouds

Cloud computing has established itself as an interesting computational model that provides a wide range of resources such as storage, databases and computing power for several types of users. Recently, the concept of cloud computing was extended with the concept of federated clouds where several resources from different cloud providers are inter-connected to perform a common action (e.g. execute a scientific workflow). Users can benefit from both single-provider and federated cloud environment to execute their scientific workflows since they can get the necessary amount of resources on demand. In several of these workflows, there is a demand for high performance and parallelism techniques since many activities are data and computing intensive and can execute for hours, days or even weeks. There are some Scientific Workflow Management Systems (SWfMS) that already provide parallelism capabilities for scientific workflows in single-provider cloud. Most of them rely on creating a virtual cluster to execute the workflow in parallel. However, they also rely on the user to estimate the amount of virtual machines to be allocated to create this virtual cluster. Most SWfMS use this initial virtual cluster configuration made by the user for the entire workflow execution. Dimensioning the virtual cluster to execute the workflow in parallel is then a top priority task since if the virtual cluster is under or over dimensioned it can impact on the workflow performance or increase (unnecessarily) financial costs. This dimensioning is far from trivial in a single-provider cloud and specially in federated clouds due to the huge number of virtual machine types to choose in each location and provider. In this article, we propose an approach named GraspCC-fed to produce the optimal (or near-optimal) estimation of the amount of virtual machines to allocate for each workflow. GraspCC-fed extends a previously proposed heuristic based on GRASP for executing standalone applications to consider scientific workflows executed in both single-provider and federated clouds. For the experiments, GraspCC-fed was coupled to an adapted version of SciCumulus workflow engine for federated clouds. This way, we believe that GraspCC-fed can be an important decision support tool for users and it can help determining an optimal configuration for the virtual cluster for parallel cloud-based scientific workflows.