DARGOS: A highly adaptable and scalable monitoring architecture for multi-tenant Clouds

One of the most important features in Cloud environments is to know the status and the availability of the physical resources and services present in the current infrastructure. A full knowledge and control of the current status of those resources enables Cloud administrators to design better Cloud provisioning strategies and to avoid SLA violations. However, it is not easy to manage such information in a reliable and scalable way, especially when we consider Cloud environments used and shared by several tenants and when we need to harmonize their different monitoring needs at different Cloud software stack layers. To cope with these issues, we propose Distributed Architecture for Resource manaGement and mOnitoring in cloudS (DARGOS), a completely distributed and highly efficient Cloud monitoring architecture to disseminate resource monitoring information. DARGOS ensures an accurate measurement of physical and virtual resources in the Cloud keeping at the same time a low overhead. In addition, DARGOS is flexible and adaptable and allows defining and monitoring new metrics easily. The proposed monitoring architecture and related tools have been integrated into a real Cloud deployment based on the OpenStack platform: they are openly available for the research community and include a Web-based customizable Cloud monitoring console. We report experimental results to assess our architecture and quantitatively compare it with a selection of other Cloud monitoring systems similar to ours showing that DARGOS introduces a very limited and scalable monitoring overhead.     

Integration of Cloud computing and Internet of Things: A survey

Cloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios.In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms–both proprietary and open source–and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet.     

A survey of Cloud monitoring tools: Taxonomy,capabilities and objectives

The efficient management of Cloud infrastructure and deployments is a topic that is currently attracting significant interest. Complex Cloud deployments can result in an intricate layered structure. Understanding the behaviour of these hierarchical systems and how to manage them optimally are challenging tasks that can be facilitated by pervasive monitoring. Monitoring tools and techniques have an important role to play in this area by gathering the information required to make informed decisions. A broad variety of monitoring tools are available, from general-purpose infrastructure monitoring tools that predate Cloud computing, to high-level application monitoring services that are themselves hosted in the Cloud. Surveying the capabilities of monitoring tools can identify the fitness of these tools in serving certain objectives. Monitoring tools are essential components to deal with various objectives of both Cloud providers and consumers in different Cloud operational areas. We have identified the practical capabilities that an ideal monitoring tool should possess to serve the objectives in these operational areas. Based on these identified capabilities, we present a taxonomy and analyse the monitoring tools to determine their strength and weaknesses. In conclusion, we present our reflections on the analysis, discuss challenges and identify future research trends in the area of Cloud monitoring.     

A performance comparison of container-based technologies for the Cloud

Cloud computing allows to utilize servers in efficient and scalable ways through exploitation of virtualization technology. In the Infrastructure-as-a-Server (IaaS) Cloud model, many virtualized servers (instances) can be created on a single physical machine. There are many such Cloud providers that are now in widespread use offering such capabilities. However, Cloud computing has overheads and can constrain the scalability and flexibility, especially when diverse users with different needs wish to use the Cloud resources. To accommodate such communities, an alternative to Cloud computing and virtualization of whole servers that is gaining widespread adoption is micro-hosting services and container-based solutions. Container-based technologies such as Docker allow hosting of micro-services on Cloud infrastructures. These enable bundling of applications and data in a manner that allows their easy deployment and subsequent utilization. Docker is just one of the many such solutions that have been put forward. The purpose of this paper is to compare and contrast a range of existing container-based technologies for the Cloud and evaluate their pros and cons and overall performances. The OpenStack-based Australia-wide National eResearch Collaboration Tools and Resources (NeCTAR) Research Cloud (www.nectar.org.au) was used for this purpose. We describe the design of the experiments and benchmarks that were chosen and relate these to literature review findings.     

GMonE: A complete approach to cloud monitoring

The inherent complexity of modern cloud infrastructures has created the need for innovative monitoring approaches, as state-of-the-art solutions used for other large-scale environments do not address specific cloud features. Although cloud monitoring is nowadays an active research field, a comprehensive study covering all its aspects has not been presented yet. This paper provides a deep insight into cloud monitoring. It proposes a unified cloud monitoring taxonomy, based on which it defines a layered cloud monitoring architecture. To illustrate it, we have implemented GMonE, a general-purpose cloud monitoring tool which covers all aspects of cloud monitoring by specifically addressing the needs of modern cloud infrastructures. Furthermore, we have evaluated the performance, scalability and overhead of GMonE with Yahoo Cloud Serving Benchmark (YCSB), by using the OpenNebula cloud middleware on the Grid’5000 experimental testbed. The results of this evaluation demonstrate the benefits of our approach, surpassing the monitoring performance and capabilities of cloud monitoring alternatives such as those present in state-of-the-art systems such as Amazon EC2 and OpenNebula.     

Virtual machine provisioning through satellite communications in federated Cloud environments

Cloud federation offers plenty of new services and business opportunities. However, many advanced services cannot be implemented in the real Cloud market due to several issues that have not been overcome yet. One of these concerns is the transfer of huge amount of data among federated Clouds. This paper aims to overcome such a limitation proposing an approach based on satellite communications. By comparing performance in data delivery on the Internet and satellite systems, it is evident that satellite technologies are enough ripe to be competitive against systems with a wired infrastructure. Thus, we propose to make use of satellite transmission to implement fast delivery of huge amount of data. Through the discussion of a use case, where a WEB TV company offers a streaming service, we show how to practically apply the proposed strategy in a real scenario, specifying the involvement of Cloud providers, Cloud users, satellite companies and end-user clients.     

云应用部署配置模型

云计算已经成为广泛使用的计算范型,越来越多的大规模分布式系统已经或正在向云平台部署和迁移.用户在部署和管理维护应用系统时通常需要管理底层基础设施资源细节,或者使用平台提供方的应用部署和管理服务,前者使得应用部署和运行时管理易于出错且费时费力,而后者则降低了系统管理的灵活性,很难满足用户的个性化需求.针对这一问题,本文提出了一种高层抽象模型来描述云应用的部署配置和管理需求.需求模型采用声明式机制定义期望的系统状态,而无需描述实现目标状态所需的执行步骤和细节.本文基于开源云计算平台OpenStack和自动化配置管理工具Puppet进行了原型实现,通过一个应用案例验证模型的有效性.     

基于PCA降维的云资源状态监控数据压缩技术

云计算资源状态监控作为保障云服务质量和可靠性的重要自动化手段,必须从海量的监控数据中分析出各类云资源的真实状态信息。为了减少资源监控任务自身对云计算资源的消耗,提出一种基于PCA(Principal Components Analysis)降维的监控数据的降维和筛选技术。监控数据转换利用PCA降维,将原始监控数据映射至若干主成分方向上,实现数据压缩。而监控数据筛选则着眼于在保留原始数据的前提下,筛选出关键监控指标以有效表征资源状态。基于VICCI云服务实验平台的实验结果证明,所提出的方法能够从多种监控数据中快速筛选出表征资源状态的核心数据,在保证状态监控效果的前提下,有效减少了监控任务所需处理的数据量。     

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.     

Cloud services selection: A systematic review and future research directions

Cloud computing has developed in popularity as a large-scale computing paradigm that offers a range of computing resources as a service through the internet on a pay-as-you-go basis. The expansion in demand and commercial availability of cloud services brings new challenges to cloud services selection. Several research studies have been conducted to develop enhanced methodologies to assist service consumers in selecting appropriate services. In this paper, 105 primary studies published during January, 2011 to May, 2022 has been selected using a multi-stage scrutinizing approach. The selected preliminary studies were further classified based on various variables to answer the research questions stated for this work. A systematic review of existing cloud service selection approaches is performed, which are analyzed along eight dimensions: decision-making methods, context, purposes, cloud service performance parameters, simulation/language tools, domain, datasets, and experiment/validation methods. After a thorough review and comparison of these approaches across the above-mentioned dimensions, several open research issues in the current literature have been identified. The contribution of this research is fourfold: focusing on state-of-the-art cloud services selection approaches, highlighting the benefits and drawbacks of various cloud services selection methodologies and their future directions, offering a taxonomy based on a thorough literature study, and identifying nine critical challenges in cloud services selection that require further investigation. This systematic review study is anticipated to benefit both academics and business experts.     

Comparison of a cost-effective virtual cloud cluster with an existing campus cluster

The Cloud provides impartial access to computer services on a pay-per-use basis, a fact that has encouraged many researchers to adopt the Cloud for the processing of large computational jobs and data storage. It has been used in the past for single research endeavours or as a mechanism for coping with excessive load on conventional computational resources (clusters). In this paper we investigate, through the use of simulation, the applicability of running an entire computer cluster on the Cloud. We investigate a number of policy decisions which can be applied to such a virtual cluster to reduce the running cost and the effect these policies have on the users of the cluster. We go further to compare the cost of running the same workload both on the Cloud and on an existing campus cluster of non-dedicated resources.     

Improving Scalability of Cloud Monitoring Through PCA-Based Clustering of Virtual Machines简

Cloud computing has recently emerged as a leading paradigm to allow customers to run their applications in virtualized large-scale data centers. Existing solutions for monitoring and management of these infrastructures consider virtual machines （VMs） as independent entities with their own characteristics. However, these approaches suffer from scalability issues due to the increasing number of VMs in modern cloud data centers. We claim that scalability issues can bc addressed by leveraging the similarity among VMs behavior in terms of resource usage patterns. In this paper we propose an automated methodology to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. The innovative contribution of the proposed methodology is the use of the statistical technique known as principal component analysis （PCA） to automatically select the most relevant information to cluster similar VMs. We apply the methodology to two case studies, a virtualized testbed and a real enterprise data center. In both case studies, the automatic data selection based on PCA allows us to achieve high performance, with a percentage of correctly clustered VMs between 80% and 100% even for short time series （1 day） of monitored data. Furthermore, we estimate the potential reduction in the amount of collected data to demonstrate how our proposal may address the scalability issues related to monitoring and management in cloud computing data centers.     

Taxonomy for Identification of Security Issues in Cloud Computing Environments

The emergence of Cloud Computing and consequential changes to infrastructure and work practices leaves little doubt that most computing platforms will be impacted by increased security concerns. Therefore, within the context of a security framework, issues surrounding the exploitation of hard or soft network elements in a Cloud architecture need to be acknowledged. To be applicable to any Cloud architecture, such a framework needs to play a role within the operational context of Cloud Computing. The framework is presented as a taxonomy of security threats specific to Cloud Computing environments.     

Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems

Cloud computing has recently emerged as a new paradigm to provide computing services through large-size data centers where customers may run their applications in a virtualized environment. The advantages of cloud in terms of flexibility and economy encourage many enterprises to migrate from local data centers to cloud platforms, thus contributing to the success of such infrastructures. However, as size and complexity of cloud infrastructures grow, scalability issues arise in monitoring and management processes. Scalability issues are exacerbated because available solutions typically consider each virtual machine (VM) as a black box with independent characteristics, which is monitored at a fine-grained granularity level for management purposes, thus generating huge amounts of data to handle. We claim that scalability issues can be addressed by leveraging the similarity between VMs in terms of resource usage patterns. In this paper, we propose an automated methodology to cluster similar VMs starting from their resource usage information, assuming no knowledge of the software executed on them. This is an innovative methodology that combines the Bhattacharyya distance and ensemble techniques to provide a stable evaluation of similarity between probability distributions of multiple VM resource usage, considering both system- and network-related data. We evaluate the methodology through a set of experiments on data coming from an enterprise data center. We show that our proposal achieves high and stable performance in automatic VMs clustering, with a significant reduction in the amount of data collected which allows to lighten the monitoring requirements of a cloud data center.     

An autonomic cloud environment for hosting ECG data analysis services

Advances in sensor technology, personal mobile devices, wireless broadband communications, and Cloud computing are enabling real-time collection and dissemination of personal health data to patients and health-care professionals anytime and from anywhere. Personal mobile devices, such as PDAs and mobile phones, are becoming more powerful in terms of processing capabilities and information management and play a major role in peoples daily lives. This technological advancement has led us to design a real-time health monitoring and analysis system that is Scalable and Economical for people who require frequent monitoring of their health. In this paper, we focus on the design aspects of an autonomic Cloud environment that collects peoples health data and disseminates them to a Cloud-based information repository and facilitates analysis on the data using software services hosted in the Cloud. To evaluate the software design we have developed a prototype system that we use as an experimental testbed on a specific use case, namely, the collection of electrocardiogram (ECG) data obtained at real-time from volunteers to perform basic ECG beat analysis.     

Quality of Experience in Cloud services: Survey and measurements

Cloud-based systems are gaining enormous popularity due to a number of promised benefits, including ease of use in terms of deployment, administration and maintenance, high scalability as well as flexibility to create new services. However, as more personal and business applications migrate to the Cloud, the service quality becomes an important differentiator between providers, specially in the case of mobile operators. Quality of Experience (QoE) as perceived by the end-user has therefore the potential to become the guiding paradigm for managing quality provisioning and applications’ design in the Cloud. This paper presents the results of several Cloud QoE studies performed for different Cloud-based services, ranging from services with low requirements in terms of latency and interactivity (e.g., Cloud storage systems), multimedia On-Demand services (e.g., YouTube video streaming), communication and telepresence (e.g., Lync Online videoconferencing) to highly interactive services (e.g., Virtual Cloud Desktop). The results of these studies provide a ground truth basis for developing future Cloud services with QoE requirements, as well as for dimensioning the underlying network provisioning infrastructures, particularly with regard to mobile access technologies.     

Manufacturing in the cloud: A human factors perspective

Cloud manufacturing adopts a cloud computing paradigm as the basis for delivering shared, on-demand manufacturing services. The result is customer-centric supply chains that can be configured for cost, quality, speed and customisation. While the technical capabilities required for cloud manufacturing are a current focus, there are many emerging questions relating to the impact, both positive and negative, on the people consuming or supporting cloud manufacturing services. Human factors can have a pivotal role in enabling the success and adoption of cloud manufacturing, while ensuring the safety, well-being and optimum user experience of those involved in a cloud manufacturing environment. This paper presents these issues, structured around groups of users (service providers, application providers and consumers). We also consider the issues of collaboration that are likely to arise from the manufacturing cloud. From this analysis we discuss the central role of human factors as an enabler of cloud manufacturing, and the opportunities that emerge.     

Behavioral modeling and automated verification of a Cloud-based framework to share the knowledge and skills of human resources

Expert Cloud as a new class of Cloud computing systems by employing the Internet infrastructures and Cloud computing concepts enables its users to request the skill, knowledge and expertise of human resources without any information about their location. It makes the communication between the HRs more efficient, reduces the cost of service, increases the variety of knowledge and information, facilitates employment of the HR in organizations, decreases customer response time and improves the service delivery methods. However, one facet that is still being less cared and that may introduce potential errors and faults regards the architectural problems and components analysis of Expert Cloud. Therefore, in this paper, we verify and check the specification, composition and architecture of the Expert Cloud via NuSMV model checker, Argo UML and Rebeca Verifier tools. The approach extracts the checking properties in the form of LTL and CTL formulas of control behaviors and automatically verifies the properties in operational behaviors. Also, experimental results indicate that the system is reachable, fair and deadlock-free.     

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end‐users under a usage‐based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter‐networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter‐networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy‐efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.     

Towards building a cloud for scientific applications

The Cloud computing becomes an innovative computing paradigm, which aims to provide reliable, customized and QoS guaranteed computing infrastructures for users. This paper presents our early experience of Cloud computing based on the Cumulus project for compute centers. In this paper, we give the Cloud computing definition and Cloud computing functionalities. This paper also introduces the Cumulus project with its various aspects, such as design pattern, infrastructure, and middleware. This paper delivers the state-of-the-art for Cloud computing with theoretical definition and practical experience.