Hybrid Clouds brokering: Business opportunities,QoS and energy-saving issues

Hybrid Clouds couple the scalability offered by public Clouds with the greater control supplied by private ones. A (hybrid) Cloud broker acting as an intermediary between users and providers of public Cloud services, may support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality.The paper presents a Cloud brokering algorithm delivering services with different level of non-functional requirements, to the private or public resources, on the basis of different scheduling criteria. With the objective of maximize user satisfaction and broker’s revenues, the algorithm pursues profit increases by reducing energy costs, through the adoption of energy saving mechanisms. A simulation model is used to evaluate performance in terms of broker’s revenue, user satisfaction and energy behavior of various allocation policies. Simulation results show that differences among policies depend on system loads and that the use of turn on and off techniques greatly improves energy savings at low and medium load rates.     

Cloud federation in a layered service model

We show how a layered Cloud service model of software (SaaS), platform (PaaS), and infrastructure (IaaS) leverages multiple independent Clouds by creating a federation among the providers. The layered architecture leads naturally to a design in which inter-Cloud federation takes place at each service layer, mediated by a broker specific to the concerns of the parties at that layer. Federation increases consumer value for and facilitates providing IT services as a commodity. This business model for the Cloud is consistent with broker mediated supply and service delivery chains in other commodity sectors such as finance and manufacturing. Concreteness is added to the federated Cloud model by considering how it works in delivering the Weather Research and Forecasting service (WRF) as SaaS using PaaS and IaaS support. WRF is used to illustrate the concepts of delegation and federation, the translation of service requirements between service layers, and inter-Cloud broker functions needed to achieve federation.     

Mandi: a market exchange for trading utility and cloud computing services

The recent development in Cloud computing has enabled the realization of delivering computing as an utility. Many industries such as Amazon and Google have started offering Cloud services on a “pay as you go” basis. These advances have led to the evolution of the market infrastructure in the form of a Market Exchange (ME) that facilitates the trading between consumers and Cloud providers. Such market environment eases the trading process by aggregating IT services from a variety of sources, and allows consumers to easily select them. In this paper, we propose a light weight and platform independent ME framework called “Mandi”, which allows consumers and providers to trade computing resources according to their requirements. The novelty of Mandi is that it not only gives its users the flexibility in terms of negotiation protocol, but also allows the simultaneous coexistence of multiple trading negotiations. In this paper, we first present the requirements that motivated our design and discuss how these facilitate the trading of compute resources using multiple market models (also called negotiation protocols). Finally, we evaluate the performance of the first prototype of “Mandi” in terms of its scalability.     

Isolation im Cloud-Computing und Mechanismen zum Schutz der Privatsph?re

Cloud Computing lifts the borders between the access control domain of individuals?? and companies?? IT systems by processing their data within the application frameworks and virtualized runtime environments of Cloud service providers. A deployment of traditional security policies for enforcing confidentiality of Cloud users?? data would lead to a conflict with the availability of the Cloud??s software services: confidentiality of data would be assured but Cloud services would not be available for every user of a Cloud. This state-of-the-art contribution shows the analogy of the confidentiality of external data processing by Cloud services with mechanisms known and applied in privacy. Sustainability in Cloud is a matter of privacy, which in Cloud is called ??isolation??.     

An autonomic approach to manage elasticity of business processes in the Cloud

Cloud Computing is gaining more and more importance in the Information Technologies (IT) scope. One of the major assets of this paradigm is its economic model based on pay-as-you-go model. Cloud Computing gets more attention from IT users when it fits their required QoS and reduces their expenses. This task cannot be done without increasing the autonomy of the provisioned Cloud resources. In this paper, we propose a holistic approach that allows to dynamically adding autonomic management facilities to Cloud resources even if they were designed without these facilities. Based on the Open Cloud Computing Interface (OCCI) standard, we propose a generic model that allows describing the needed resources to render autonomic a given Cloud resource independently of the service level (Infrastructure, Platform or Software). Herein, we define new OCCI Resources, Links and Mixins that allow provisioning autonomic Cloud Resources. In order to illustrate our approach, we propose a use case that specializes our autonomic infrastructure to ensure the elasticity of Service-based Business Processes (SBPs). The elasticity approach that we are using is based on a formal model that features duplication/consolidation mechanisms and a generic Controller that defines and evaluates elasticity strategies. To validate our proposal, we present an end to end scenario of provisioning an elastic SBP on a public PaaS. Evaluation of our approach on a realistic situation shows its efficiency.     

Towards an autonomic performance management approach for a cloud broker environment using a decomposition–coordination based methodology

Efficient resource allocation of computational resources to services is one of the predominant challenges in a cloud computing environment. Furthermore, the advent of cloud brokerage and federated cloud computing systems increases the complexity of cloud resource management. Cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers. Cloud brokers rent different types of cloud resources from a number of cloud providers and sublet these resources to the requesting service providers. In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation capabilities for deploying a set of services over a federated cloud computing infrastructure by considering the availability as well as the demand of the cloud computing resources. A distributed control based approach is used for providing autonomic computing features to the proposed framework via a feedback-based control loop. This distributed control based approach is developed using one of the decomposition–coordination methodologies, named interaction balance, for interactive bidding of cloud computing resources. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for the service providers and the cloud broker. The application of interaction balance methodology and prioritization of profit maximization for the cloud broker and the service providers during resource allocation are novel contributions of the proposed approach.     

A secure re‐encryption scheme for data services in a cloud computing environment

Cloud computing as a promising technology and paradigm can provide various data services, such as data sharing and distribution, which allows users to derive benefits without the need for deep knowledge about them. However, the popular cloud data services also bring forth many new data security and privacy challenges. Cloud service provider untrusted, outsourced data security, hence collusion attacks from cloud service providers and data users become extremely challenging issues. To resolve these issues, we design the basic parts of secure re‐encryption scheme for data services in a cloud computing environment, and further propose an efficient and secure re‐encryption algorithm based on the EIGamal algorithm, to satisfy basic security requirements. The proposed scheme not only makes full use of the powerful processing ability of cloud computing but also can effectively ensure cloud data security. Extensive analysis shows that our proposed scheme is highly efficient and provably secure under existing security model. Copyright © 2015 John Wiley & Sons, Ltd.     

Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing

Cloud computing promises to provide high quality, on-demand services with service-oriented architecture. However, cloud service typically come with various levels of services and performance characteristics, which makes Quality of Cloud Service (QoCS) high variance. Hence, it is difficult for the users to evaluate these cloud services and select them to fit their QoCS requirements. In this paper, we propose an accurate evaluation approach of QoCS in service-oriented cloud computing. We first employ fuzzy synthetic decision to evaluate cloud service providers according to cloud users’ preferences and then adopt cloud model to computing the uncertainty of cloud services based on monitored QoCS data. Finally, we obtain the evaluation results of QoCS using fuzzy logic control. The simulation results demonstrate that our proposed approach can perform an accurate evaluation of QoCS in service-oriented cloud computing.     

Policing as a Service in the Cloud

ABSTRACT

Security and privacy are fundamental concerns in cloud computing both in terms of legal complications and user trust. Cloud computing is a new computing paradigm, aiming to provide reliable, customized, and guaranteed computing dynamic environment for end users. However, the existing security and privacy issues in the cloud still present a strong barrier for users to adopt cloud computing solutions. This paper investigates the security and privacy challenges in cloud computing in order to explore methods that improve the users’ trust in the adaptation of the cloud. Policing as a Service can be offered by the cloud providers with the intention of empowering users to monitor and guard their assets in the cloud. This service is beneficial both to the cloud providers and the users. However, at first, the cloud providers may only be able to offer basic auditing services due to undeveloped tools and applications. Similar to other services delivered in the cloud, users can purchase this service to gain some control over their data. The subservices of the proposed service can be Privacy as a Service and Forensics as a Service. These services give users a sense of transparency and control over their data in the cloud while better security and privacy safeguards are sought.     

Ontology-based access control model for security policy reasoning in cloud computing

There are many security issues in cloud computing service environments, including virtualization, distributed big-data processing, serviceability, traffic management, application security, access control, authentication, and cryptography, among others. In particular, data access using various resources requires an authentication and access control model for integrated management and control in cloud computing environments. Cloud computing services are differentiated according to security policies because of differences in the permitted access right between service providers and users. RBAC (Role-based access control) and C-RBAC (Context-aware RBAC) models do not suggest effective and practical solutions for managers and users based on dynamic access control methods, suggesting a need for a new model of dynamic access control that can address the limitations of cloud computing characteristics. This paper proposes Onto-ACM (ontology-based access control model), a semantic analysis model that can address the difference in the permitted access control between service providers and users. The proposed model is a model of intelligent context-aware access for proactively applying the access level of resource access based on ontology reasoning and semantic analysis method.     

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.     

云工作流中基于分时虚拟机的任务层调度算法

云计算是新的一种面向市场的商业计算模式,向用户按需提供服务,云计算的商业特性使其关注向用户提供服务的服务质量。任务调度和资源分配是云计算中两个关键的技术,所使用的虚拟化技术使得其资源分配和任务调度有别于以往的并行分布式计算。目前主要的调度算法是借鉴网格环境下的调度策略,研究基于QoS的调度算法,存在执行效率较低的问题。我们对云工作流任务层调度进行深入研究,分析由底层资源虚拟化形成的虚拟机的特性,结合工作流任务的各类QoS约束,提出了基于虚拟机分时特性的任务层ACS调度算法。经过试验,我们提出的算法相比于文献[1]中的算法在对于较多并行任务的执行上存在较大的优势,能够很好的利用虚拟的分时特性,优化任务到虚拟机的调度。     

Web services based scheduling in OpenCF

Cloud Computing is emerging as a new computing paradigm which aims to provide reliable, customized and QoS guaranteed dynamic computing environments for end users. The availability of these large, virtualized pools of computing resources raises the possibility of a new computing paradigm for scientific research with many advantages. For research groups, Cloud Computing provides convenient access to reliable, high-performance clusters and storage without the need to purchase and maintain sophisticated hardware. For developers, virtualization allows scientific codes to be optimized and pre-installed on machine images, facilitating control over the computational environment. In these large-scale, heterogeneous and dynamic systems, the efficient execution of parallel computations can require mappings of tasks to computing resources whose performance is both irregular (because of heterogeneity) and variable in time (because of dynamicity). This paper introduces our initial experience with Cloud Computing based on a Python implementation of our OpenCF framework. We propose to show the features provided by OpenCF using the Google Application Engine as a proof of concept.     

In Clouds Shall We Trust?

Cloud computing will become ubiquitous, but what we can do to improve our ability to provide users and providers of cloud computing with trust in the software services and infrastructure that make up the cloud. In this article we touch on issues of the transparency, changing expectations and uses, architecture, and amorphous nature of cloud computing.     

EMUSIM: an integrated emulation and simulation environment for modeling,evaluation, and validation of performance of Cloud computing applications

Cloud computing allows the deployment and delivery of application services for users worldwide. Software as a Service providers with limited upfront budget can take advantage of Cloud computing and lease the required capacity in a pay‐as‐you‐go basis, which also enables flexible and dynamic resource allocation according to service demand. One key challenge potential Cloud customers have before renting resources is to know how their services will behave in a set of resources and the costs involved when growing and shrinking their resource pool. Most of the studies in this area rely on simulation‐based experiments, which consider simplified modeling of applications and computing environment. In order to better predict service's behavior on Cloud platforms, we developed an integrated architecture that is based on both simulation and emulation. The proposed architecture, named EMUSIM, automatically extracts information from application behavior via emulation and then uses this information to generate the corresponding simulation model. We performed experiments using an image processing application as a case study and found that EMUSIM was able to accurately model such application via emulation and use the model to supply information about its potential performance in a Cloud provider. We also discuss our experience using EMUSIM for deploying applications in a real public Cloud provider. EMUSIM is based on an open source software stack and therefore it can be extended for analysis behavior of several other applications. Copyright © 2012 John Wiley & Sons, Ltd.     

Operation analysis of massively multiplayer online games on unreliable resources

We investigate the use of a new Massively Multiplayer Online Gaming (MMOG) ecosystem consisting of end-users, game providers, game operators, and Cloud resource providers, for autonomous, self-adaptive hosting and operation of MMOGs on unreliable resources. For this purpose, we developed an MMOG simulator compliant with our ecosystem, supported by a dynamic resource provisioning and load balancing algorithm. Using our simulator in which we inject traces collected from a real-world MMOG and resource characteristics from 16 Cloud providers, we study the impact on the involved actors by considering different resource availability levels. We highlight the advantages of dynamic resource allocation over the static overprovisioning with respect to two types of metrics: QoS offered to the clients and financial profit of game providers and operators.     

A resource elasticity framework for QoS-aware execution of cloud applications

Cloud infrastructures consisting of heterogeneous resources are increasingly being utilized for hosting large-scale distributed applications from diverse users with discrete needs. The multifarious cloud applications impose varied demands for computational resources along with multitude of performance implications. Successful hosting of cloud applications necessitates service providers to take into account the heterogeneity existing in the behavior of users, applications and system resources while respecting the user’s agreed Quality of Service (QoS) criteria. In this work, we propose a QoS-Aware Resource Elasticity (QRE) framework that allows service providers to make an assessment of the application behavior and develop mechanisms that enable dynamic scalability of cloud resources hosting the application components. Experimental results conducted on the Amazon EC2 cloud clearly demonstrate the effectiveness of our approach while complying with the agreed QoS attributes of users.     

Toward Cloud Computing QoS Architecture: Analysis of Cloud Systems and Cloud Services

Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service (QoS) by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue.      

个人云存储体验质量测量工具的设计与实现

随着用户日益增长的网络存储需求,涌现出了大量个人云存储（PCS）服务平台。个人云存储终端用户使用过程中体验质量（QoE）的测量是终端用户和服务提供商所共同关注的问题。通过从控制流与数据流之间的不同特性方面分析了影响个人云存储体验质量的因素,从终端用户的角度提出了能合理评估个人云存储体验质量的指标,设计了精确测量体验质量评估指标的方法。利用被动测量技术,实现了一个面向终端用户的个人云存储服务体验质量测量工具,同时给出了工具实现中的进程抓包、网络流分类等问题的解决方案。实验结果表明,测量工具运行健壮,测量数据准确,可以用于从用户终端测量个人云存储服务QoE。     

SLA-based admission control for a Software-as-a-Service provider in Cloud computing environments

Software as a Service (SaaS) provides access to applications to end users over the Internet without upfront investment in infrastructure and software. To serve their customers, SaaS providers utilise resources of internal data centres or rent resources from a public Infrastructure as a Service (IaaS) provider. In-house hosting can increase administration and maintenance costs whereas renting from an IaaS provider can impact the service quality due to its variable performance. To overcome these limitations, we propose innovative admission control and scheduling algorithms for SaaS providers to effectively utilise public Cloud resources to maximize profit by minimizing cost and improving customer satisfaction level. Furthermore, we conduct an extensive evaluation study to analyse which solution suits best in which scenario to maximize SaaS provider?s profit. Simulation results show that our proposed algorithms provide substantial improvement (up to 40% cost saving) over reference ones across all ranges of variation in QoS parameters.