云计算弹性测试研究

弹性作为云计算的关键特征,能够支持资源的快速扩展、灵活配置、动态增加和释放,使得资源充分利用,同时降低云服务提供商和用户的成本。为了检测弹性是否满足用户的服务等级协议和资源的合理配置,需要对其进行评估和测试。在分析弹性定义、实现方法和评测指标的基础上,提出弹性测试的概念,总结了目前弹性测试的相关研究,分析了弹性测试的关键技术和架构,指出弹性测试面临的挑战和问题,并给出了弹性测试的进一步研究方向。     

Language and Runtime Support for Automatic Configuration and Deployment of Scientific Computing Software over Cloud Fabrics

In this paper, we present the design and implementation of Neptune, a simple, domain-specific language based on the Ruby programming language. Neptune automates the configuration and deployment of scientific software frameworks over disparate cloud computing systems. Neptune integrates support for MPI, MapReduce, UPC, X10, StochKit, and others. We implement Neptune as a software overlay for the AppScale cloud platform and extend AppScale with support for elasticity and hybrid execution for scientific computing applications. Neptune imposes no overhead on application execution, yet significantly simplifies the application deployment process, enables portability across cloud systems, and promotes lock-in avoidance by specific cloud vendors.     

Implementation and performance evaluation of a scheduling algorithm for divisible load parallel applications in a cloud computing environment

Cloud computing is an emerging technology in which information technology resources are virtualized to users in a set of computing resources on a pay‐per‐use basis. It is seen as an effective infrastructure for high performance applications. Divisible load applications occur in many scientific and engineering applications. However, dividing an application and deploying it in a cloud computing environment face challenges to obtain an optimal performance due to the overheads introduced by the cloud virtualization and the supporting cloud middleware. Therefore, we provide results of series of extensive experiments in scheduling divisible load application in a Cloud environment to decrease the overall application execution time considering the cloud networking and computing capacities presented to the application's user. We experiment with real applications within the Amazon cloud computing environment. Our extensive experiments analyze the reasons of the discrepancies between a theoretical model and the reality and propose adequate solutions. These discrepancies are due to three factors: the network behavior, the application behavior and the cloud computing virtualization. Our results show that applying the algorithm result in a maximum ratio of 1.41 of the measured normalized makespan versus the ideal makespan for application in which the communication to computation ratio is big. They show that the algorithm is effective for those applications in a heterogeneous setting reaching a ratio of 1.28 for large data sets. For application following the ensemble clustering model in which the computation to communication ratio is big and variable, we obtained a maximum ratio of 4.7 for large data set and a ratio of 2.11 for small data set. Applying the algorithm also results in an important speedup. These results are revealing for the type of applications we consider under experiments. The experiments also reveal the impact of the choice of the platforms provided by Amazon on the performance of the applications under study. Considering the emergence of cloud computing for high performance applications, the results in this paper can be widely adopted by cloud computing developers. Copyright © 2014 John Wiley & Sons, Ltd.     

Expanded cloud plumes hiding Big Data ecosystem

Today, a paradigm shift is being observed in science, where the focus is gradually shifting away from operation to data, which is greatly influencing the decision making also. The data is being inundated proactively from several sources in various forms; especially social media and in modern data science vocabulary is being recognized as Big Data. Today, Big Data is permeating through the bigger aspect of human life for scientific and commercial dependencies, especially for massive scale data analytics of beyond the exabyte magnitude. As the footprint of Big Data applications is continuously expanding, the reliability on cloud environments is also increasing to obtain appropriate, robust and affordable services to deal with Big Data challenges. Cloud computing avoids any need to locally maintain the overly scaled computing infrastructure that include not only dedicated space, but the expensive hardware and software also. Several data models to process Big Data are already developed and a number of such models are still emerging, potentially relying on heterogeneous underlying storage technologies, including cloud computing. In this paper, we investigate the growing role of cloud computing in Big Data ecosystem. Also, we propose a novel XCLOUDX {XCloudX, X…X}classification to zoom in to gauge the intuitiveness of the scientific name of the cloud-assisted NoSQL Big Data models and analyze whether XCloudX always uses cloud computing underneath or vice versa. XCloudX symbolizes those NoSQL Big Data models that embody the term “cloud” in their name, where X is any alphanumeric variable. The discussion is strengthen by a set of important case studies. Furthermore, we study the emergence of as-a-Service era, motivated by cloud computing drive and explore the new members beyond traditional cloud computing stack, developed in the past couple of years.     

IaaSMon: Monitoring Architecture for Public Cloud Computing Data Centers

Monitoring of cloud computing infrastructures is an imperative necessity for cloud providers and administrators to analyze, optimize and discover what is happening in their own infrastructures. Current monitoring solutions do not fit well for this purpose mainly due to the incredible set of new requirements imposed by the particular requirements associated to cloud infrastructures. This paper describes in detail the main reasons why current monitoring solutions do not work well. Also, it provides an innovative monitoring architecture that enables the monitoring of the physical and virtual machines available within a cloud infrastructure in a non-invasive and transparent way making it suitable not only for private cloud computing but also for public cloud computing infrastructures. This architecture has been validated by means of a prototype integrating an existing enterprise-class monitoring solution, Nagios, with the control and data planes of OpenStack, a well-known stack for cloud infrastructures. As a result, our new monitoring architecture is able to extend the exiting Nagios functionalities to fit in the monitoring of cloud infrastructures. The proposed architecture has been designed, implemented and released as open source to the scientific community. The proposal has also been empirically validated in a production-level cloud computing infrastructure running a test bed with up to 128 VMs where overhead and responsiveness has been carefully analyzed.     

A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds

In the last years, scientific workflows have emerged as a fundamental abstraction for structuring and executing scientific experiments in computational environments. Scientific workflows are becoming increasingly complex and more demanding in terms of computational resources, thus requiring the usage of parallel techniques and high performance computing (HPC) environments. Meanwhile, clouds have emerged as a new paradigm where resources are virtualized and provided on demand. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines. Although the initial focus of clouds was to provide high throughput computing, clouds are already being used to provide an HPC environment where elastic resources can be instantiated on demand during the course of a scientific workflow. However, this model also raises many open, yet important, challenges such as scheduling workflow activities. Scheduling parallel scientific workflows in the cloud is a very complex task since we have to take into account many different criteria and to explore the elasticity characteristic for optimizing workflow execution. In this paper, we introduce an adaptive scheduling heuristic for parallel execution of scientific workflows in the cloud that is based on three criteria: total execution time (makespan), reliability and financial cost. Besides scheduling workflow activities based on a 3-objective cost model, this approach also scales resources up and down according to the restrictions imposed by scientists before workflow execution. This tuning is based on provenance data captured and queried at runtime. We conducted a thorough validation of our approach using a real bioinformatics workflow. The experiments were performed in SciCumulus, a cloud workflow engine for managing scientific workflow execution.     

Empirical evaluation of a cloud computing information security governance framework

ContextCloud computing is a thriving paradigm that supports an efficient way to provide IT services by introducing on-demand services and flexible computing resources. However, significant adoption of cloud services is being hindered by security issues that are inherent to this new paradigm. In previous work, we have proposed ISGcloud, a security governance framework to tackle cloud security matters in a comprehensive manner whilst being aligned with an enterprise’s strategy.ObjectiveAlthough a significant body of literature has started to build up related to security aspects of cloud computing, the literature fails to report on evidence and real applications of security governance frameworks designed for cloud computing environments. This paper introduces a detailed application of ISGCloud into a real life case study of a Spanish public organisation, which utilises a cloud storage service in a critical security deployment.MethodThe empirical evaluation has followed a formal process, which includes the definition of research questions previously to the framework’s application. We describe ISGcloud process and attempt to answer these questions gathering results through direct observation and from interviews with related personnel.ResultsThe novelty of the paper is twofold: on the one hand, it presents one of the first applications, in the literature, of a cloud security governance framework to a real-life case study along with an empirical evaluation of the framework that proves its validity; on the other hand, it demonstrates the usefulness of the framework and its impact to the organisation.ConclusionAs discussed on the paper, the application of ISGCloud has resulted in the organisation in question achieving its security governance objectives, minimising the security risks of its storage service and increasing security awareness among its users.     

Joint‐analysis of performance and energy consumption when enabling cloud elasticity for synchronous HPC applications

A key characteristic of cloud computing is elasticity, automatically adjusting system resources to an application's workload. Both reactive and horizontal approaches represent traditional means to offer this capability, in which rule‐condition‐action statements and upper and lower thresholds occur to instantiate or consolidate compute nodes and virtual machines. Although elasticity can be beneficial for many HPC (high‐performance computing) scenarios, it also imposes significant challenges in the development of applications. In addition to issues related to how we can incorporate this new feature in such applications, there is a problem associated with the performance and resource pair and, consequently, with energy consumption. Further exploring this last difficulty, we must be capable of analyzing elasticity effectiveness as a function of employed thresholds with clear metrics to compare elastic and non‐elastic executions properly. In this context, this article explores elasticity metrics in two ways: (i) the use of a cost function that combines application time with different energy models; (ii) the extension of speedup and efficiency metrics, commonly used to evaluate parallel systems, to cover cloud elasticity. To accomplish (i) and (ii), we developed an elasticity model known as AutoElastic, which reorganizes resources automatically across synchronous parallel applications. The results, obtained with the AutoElastic prototype using the OpenNebula middleware, are encouraging. Considering a CPU‐bound application, an upper threshold close to 70% was the best option for obtaining good performance with a non‐prohibitive elasticity cost. In addition, the value of 90% for this threshold was the best option when we plan an efficiency‐driven execution. Copyright © 2015 John Wiley & Sons, Ltd.     

Implementation and performance evaluation of a distributed conjugate gradient method in a cloud computing environment

Cloud computing is an emerging technology where information technology resources are provisioned to users in a set of a unified computing resources on a pay per use basis. The resources are dynamically chosen to satisfy a user service level agreement and a required level of performance. A cloud is seen as a computing platform for heavy load applications. Conjugate gradient (CG) method is an iterative linear solver that is used by many scientific and engineering applications to solve a linear system of algebraic equations. CG generates a heavy load of computation, and therefore, it slows the performance of the applications using it. Distributing CG is considered as a way to increase its performance. However, running a distributed CG, based on a standard API, such as Message Passing Interface, in a cloud face many challenges, such as the cloud processing and networking capabilities. In this work, we present an in‐depth analysis of the CG algorithm and its complexity to develop adequate distributed algorithms. The implementation of these algorithms and their evaluation in our cloud environment reveal the gains and losses achieved by distributing the CG. The performance results show that despite the complexity of the CG processing and communication, a speedup gain of at least 1157.7 is obtained using 128 cores compared with National Aeronautics and Space Administration Advanced Supercomputing sequential execution. Given the emergence of clouds, the results in this paper analyzes performance issues when a generic public cloud, along with a standard development library, such as Message Passing Interface, is used for high‐performance applications, without the need of some specialized hardware and software. Copyright © 2012 John Wiley & Sons, Ltd.     

Cloud Computing: Will Commodity Services Benefit Users Long Term?

The attention the IT community has given cloud computing recently rivals that given to American Idol judges by the public. This magazine alone dedicated its March/April 2009 issue and significant other space throughout the year to the topic. The introduction to IT Professional's March/April issue asked whether cloud computing offers anything different; after a review of both the basics and the challenges, the authors respond to that question.     

Adapting scientific computing problems to clouds using MapReduce

Cloud computing, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. To study this, we established a scientific computing cloud (SciCloud) project and environment on our internal clusters. The main goal of the project is to study the scope of establishing private clouds at the universities. With these clouds, students and researchers can efficiently use the already existing resources of university computer networks, in solving computationally intensive scientific, mathematical, and academic problems. However, to be able to run the scientific computing applications on the cloud infrastructure, the applications must be reduced to frameworks that can successfully exploit the cloud resources, like the MapReduce framework. This paper summarizes the challenges associated with reducing iterative algorithms to the MapReduce model. Algorithms used by scientific computing are divided into different classes by how they can be adapted to the MapReduce model; examples from each such class are reduced to the MapReduce model and their performance is measured and analyzed. The study mainly focuses on the Hadoop MapReduce framework but also compares it to an alternative MapReduce framework called Twister, which is specifically designed for iterative algorithms. The analysis shows that Hadoop MapReduce has significant trouble with iterative problems while it suits well for embarrassingly parallel problems, and that Twister can handle iterative problems much more efficiently. This work shows how to adapt algorithms from each class into the MapReduce model, what affects the efficiency and scalability of algorithms in each class and allows us to judge which framework is more efficient for each of them, by mapping the advantages and disadvantages of the two frameworks. This study is of significant importance for scientific computing as it often uses complex iterative methods to solve critical problems and adapting such methods to cloud computing frameworks is not a trivial task.     

Enabling cost-aware and adaptive elasticity of multi-tier cloud applications

Elasticity (on-demand scaling) of applications is one of the most important features of cloud computing. This elasticity is the ability to adaptively scale resources up and down in order to meet varying application demands. To date, most existing scaling techniques can maintain applications’ Quality of Service (QoS) but do not adequately address issues relating to minimizing the costs of using the service. In this paper, we propose an elastic scaling approach that makes use of cost-aware criteria to detect and analyse the bottlenecks within multi-tier cloud-based applications. We present an adaptive scaling algorithm that reduces the costs incurred by users of cloud infrastructure services, allowing them to scale their applications only at bottleneck tiers, and present the design of an intelligent platform that automates the scaling process. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness against other approaches by studying the behaviour of an example e-commerce application using a standard workload benchmark.     

Architecting cloud‐enabled systems: a systematic survey of challenges and solutions

The literature on the challenges of and potential solutions to architecting cloud‐based systems is rapidly growing but is scattered. It is important to systematically analyze and synthesize the existing research on architecting cloud‐based software systems in order to build a cohesive body of knowledge of the reported challenges and solutions. We have systematically identified and reviewed 133 papers that report architecture‐related challenges and solutions for cloud‐based software systems. This paper reports the methodological details, findings, and implications of a systematic review that has enabled us to identify 44 unique categories of challenges and associated solutions for architecting cloud‐based software systems. We assert that the identified challenges and solutions classified into the categories form a body of knowledge that can be leveraged for designing or evaluating software architectures for cloud‐based systems. Our key conclusions are that a large number of primary studies focus on middleware services aimed at achieving scalability, performance, response time, and efficient resource optimization. Architecting cloud‐based systems presents unique challenges as the systems to be designed range from pervasive embedded systems and enterprise applications to smart devices with Internet of Things. We also conclude that there is a huge potential of research on architecting cloud‐based systems in areas related to green computing, energy efficient systems, mobile cloud computing, and Internet of Things. Copyright © 2016 John Wiley & Sons, Ltd.     

A high performance scientific cloud computing environment for materials simulations

We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.     

A survey on security challenges in cloud computing: issues,threats, and solutions

Cloud computing has gained huge attention over the past decades because of continuously increasing demands. There are several advantages to organizations moving toward cloud-based data storage solutions. These include simplified IT infrastructure and management, remote access from effectively anywhere in the world with a stable Internet connection and the cost efficiencies that cloud computing can bring. The associated security and privacy challenges in cloud require further exploration. Researchers from academia, industry, and standards organizations have provided potential solutions to these challenges in the previously published studies. The narrative review presented in this survey provides cloud security issues and requirements, identified threats, and known vulnerabilities. In fact, this work aims to analyze the different components of cloud computing as well as present security and privacy problems that these systems face. Moreover, this work presents new classification of recent security solutions that exist in this area. Additionally, this survey introduced various types of security threats which are threatening cloud computing services and also discussed open issues and propose future directions. This paper will focus and explore a detailed knowledge about the security challenges that are faced by cloud entities such as cloud service provider, the data owner, and cloud user.

     

Locking the sky: a survey on IaaS cloud security

Cloud computing is expected to become a common solution for deploying applications thanks to its capacity to leverage developers from infrastructure management tasks, thus reducing the overall costs and services’ time to market. Several concerns prevent players’ entry in the cloud; security is arguably the most relevant one. Many factors have an impact on cloud security, but it is its multitenant nature that brings the newest and more challenging problems to cloud settings. Here, we analyze the security risks that multitenancy induces to the most established clouds, Infrastructure as a service clouds, and review the literature available to present the most relevant threats, state of the art of solutions that address some of the associated risks. A major conclusion of our analysis is that most reported systems employ access control and encryption techniques to secure the different elements present in a virtualized (multitenant) datacenter. Also, we analyze which are the open issues and challenges to be addressed by cloud systems in the security field.     

Model-based services convergence and multi-clouds integration

With the development of cloud computing, IT users (individuals, enterprises and even public services providers) are transferring their jobs or businesses to public online services provided by professional information service companies. These information service companies provide applications as public resources to support the business operation of their customers. However, no cloud computing service vendor (CCSV) can satisfy the full functional information system requirements of its customers. As a result, its customers often have to simultaneously use services distributed in different clouds and do some connectivity jobs manually. Services convergence and multi-clouds integration will lead to new business models and trigger new integration technologies that provide solutions to satisfy IT users’ complicated requirements. This paper firstly reviews the development of cloud computing from business and technical viewpoints and then discusses requirements and challenges of services convergence and multi-clouds integrations. Thirdly, a model based architecture of multi-clouds integration is provided. Business logic modelling for cross-organizational collaboration, service modelling and operation modelling methods with relative model mapping technology are discussed in detail. Some key enabling technologies are also developed. At last, case studies are presented to illustrate the implementation of the technologies developed in the paper.     

云计算体系结构及应用实例分析

云计算包含两个方面的基本内容：一、描述用于构造应用程序的基础架构;二、描述建立在这种基础架构之上的应用和扩展服务;针对云计算的体系结构及应用实例,剖析其背后的技术含义以及当前云计算平台所采用的实现方法,进而评析当前云计算的发展状况,探讨实现云计算的技术方案。     

Performance optimization for cloud computing systems in the microservice era: state-of-the-art and research opportunities

With the demand of agile development and management, cloud applications today are moving towards a more fine-grained microservice paradigm, where smaller and simpler functioning parts are combined for providing end-to-end services. In recent years, we have witnessed many research efforts that strive to optimize the performance of cloud computing system in this new era. This paper provides an overview of existing works on recent system performance optimization techniques and classify them based on their design focuses. We also identify open issues and challenges in this important research direction.