Cloud Middleware Research Articles

INFN and the evolution of distributed scientific computing in Italy

INFN and the evolution of distributed scientific computing in Italy

Internet of Things Middleware: Evaluating the Costs of Service-Based Solutions Under Different Application Scenarios

The rapid evolution of technology has paved the way for the many applications of the Internet of Things (IoT). As IoT systems become increasingly complex, new technologies that simplify the development of the IoT are emerging, for example, IoT cloud platforms have gained popularity as tools that reduce development time and cut costs. Recent surveys indicate that only a few IoT projects have had successful outcomes. Among the main reasons for this, there is the lack of knowledge regarding IoT design and architecture. Numerous research studies have tried to address the problem of choosing the right cloud platform based on specific requirements, but none of the reviewed studies has proposed a cost-benefit analysis of the current available cloud solutions for the IoT. Here, the IoT has been studied from a decision-maker’s perspective, giving a brief but concise introduction to modern IoT architecture and IoT middleware and evaluating the costs of four popular IoT cloud middleware solutions under different project requirements and design scenarios.

Cloud middleware and services-a systematic mapping review

Cloud computing currently plays a crucial role in the delivery of vital information technology services. A unique aspect of cloud computing is the cloud middleware and other related entities that support applications and networks. A specific field of research may be considered, particularly as regards cloud middleware and services at all levels, and thus needs analysis and paper surveys to elucidate possible study limitations. The purpose of this paper is to perform a systematic mapping for studies that capture cloud computing middleware, stacks, tools and services. The methodology adopted for this study is a systematic mapping review. The results showed that more papers on the contribution facet were published with tool, model, method and process having 18.10%, 13.79%, 6.03% and 8.62% respectively. In addition, in terms of tool, evaluation and solution research had the largest number of articles with 14.17% and 26.77% respectively. A striking feature of the systemic map is the high number of articles in solution research with respect to all aspects of the features applied in the studies. This study showed clearly that there are gaps in cloud computing middleware and delivery services that would interest researchers and industry professionals desirous of research in this area.

DSEM-SW: a data-service enhanced scientific workflow middleware for cloud computing

As more and more scientific workflows have been deployed and executed in cloud environments, how to optimise the execution efficiency and quality of workflow applications has become an important issue that needs to be addressed. Although many workflow management systems have been proposed in recent years, the performance bottleneck when running scientific workflows is still the low-efficiency of data-related services. In this paper, we present a workflow-oriented optimisation plug-in system, namely data-service enhanced middleware for scientific workflow (DSEM-SW), which provides a set of mechanisms to improve the efficiency and quality of data processing when running scientific workflows. Extensive experiments are conducted to evaluate the performance of the proposed middleware in a real-world cloud test-bed. The experimental results indicate that our DSEM-SW outperforms many existing systems in terms of different performance metrics, and can significantly improve the execution efficiency of data-intensive workflows.

DSEM-SW: a data-service enhanced scientific workflow middleware for cloud computing

DSEM-SW: a data-service enhanced scientific workflow middleware for cloud computing

Cloud-based Enabling Mechanisms for Container Deployment and Migration at the Network Edge

In recent years, a new trend of advanced applications with huge demands in terms of Quality of Service (QoS) is gaining ground. Even though Cloud computing provides mature management facilities with ubiquitous capabilities, novel requirements and workloads, foisted by new services, start to expose its weaknesses. In this context, a new Information and Communication Technologies (ICT) trend aims at pushing computation from the Cloud to be much close as possible to data sources, raising in the evolution of new paradigms namely Fog and Mist computing. Specifically, the Fog computing paradigm exploits powerful nodes such as servers, routers, and cloudlets that are coupled with the end devices or their access networks accordingly; they are ”relatively” close by the data sources. Whereas Mist computing, which is a lightweight form of Fog computing, pushes the resources even closer. Precisely, Mist computing uses particular nodes that could reside within the same network (e.g., Local Area Network (LAN)) as the end-devices. Considering the advancement that the hardware is knowing nowadays, Fog and Mist nodes are seen suitable to provide resources such as processing, storage, and networking in the proximity of data sources; thereby, the requirements of the new services could be met. Together with the Cloud, the Fog and Mist paradigms introduce a stacked architecture for data processing where a data pre-processing could be performed at the Mist level, then offloaded vertically to the upper layers (i.e., Fog nodes or the Cloud). In these circumstances, it is fundamental to build a management system able to provision efficiently the Fog/Mist-based applications. For this purpose, the Operating System (OS)-level virtualization using containerization technologies, considering its light footprint, fits as a suitable solution to provide Fog/Mist services. The industrial-grade Cloud middlewares, such as OpenStack, which is a reference architecture for Infrastructure-as-a-Service solutions, are still far away from incorporating this new trend. This article proposes an OpenStack-based middleware platform through which containers can be deployed/managed at the Fog/Mist levels.

Impact of Cloud Database in Medical Healthcare Records Based On Secure Access

In the future generation would development of new technologies, such as cloud computing, Internet of things (IOT), used in the medical field from concept to practice. Health care medical field is the major import role in cloud computing. The E-medical record contains the Healthcare providers HIS, but its deployment process slow due to high cost and high maintenance cost. In this paper study, electronic medic records with cloud-based security and access rights. In the cloud data ware house contained for all hospital patients details. Cloud middleware gives a platform for electronic healthcare system. It is provides to secure access the medical health record only authorized users and avoid unauthorized user. In this paper, proposed cloud computing design, which enables the secure and collaborative E-medical records among the all the hospitals with case of access rights.

Amazon Elastic Compute Cloud (EC2) versus In-House HPC Platform: A Cost Analysis

While High Performance Computing (HPC) centers continuously evolve to provide more computing power to their users, we observe a wish for the convergence between Cloud Computing (CC) and High Performance Computing (HPC) platforms, with the commercial hope to see Cloud Computing (CC) infrastructures to eventually replace in-house facilities. If we exclude the performance point of view where many previous studies highlight a non-negligible overhead induced by the virtualization layer at the heart of every Cloud middleware when running a HPC workload, the question of the real cost-effectiveness is often left aside with the intuition that, most probably, the instances offered by the Cloud providers are competitive from a cost point of view. In this article, we wanted to assert (or infirm) this intuition by analyzing what composes the Total Cost of Ownership (TCO) of an in-house HPC facility operated internally since 2007. This Total Cost of Ownership (TCO) model is then used to compare with the induced cost that would have been required to run the same platform (and the same workload) over a competitive Cloud IaaS offer. Our approach to address this price comparison is three-fold. First we propose a theoretical price-performance model based on the study of the actual Cloud instances proposed by one of the major Cloud IaaS actors: Amazon Elastic Compute Cloud (EC2). Then, based on the HPC facility TCO analysis we propose a hourly price comparison between our in-house cluster and the equivalent EC2 instances. Finally, based on the experimental benchmarking on the local cluster and on the Cloud instances we propose an update of the former theoretical price model to reflect the real system performance. The results obtained advocate in general for the acquisition of an in-house HPC facility, which balances the common intuition in favor of Cloud Computing platforms, would they be provided by the reference Cloud provider worldwide.

Umd-verification: Automation of Software Validation for the EGI Federated e-Infrastructure

Supporting e-Science in the EGI e-Infrastructure requires extensive and reliable software, for advanced computing use, deployed across over approximately 300 European and worldwide data centers. The Unified Middleware Distribution (UMD) and Cloud Middleware Distribution (CMD) are the channels to deliver the software for the EGI e-Infrastructure consumption. The software is compiled, validated and distributed following the Software Provisioning Process (SWPP), where the Quality Criteria (QC) definition sets the minimum quality requirements for EGI acceptance. The growing number of software components currently existing within UMD and CMD distributions hinders the application of the traditional, manual-based validation mechanisms, thus driving the adoption of automated solutions. This paper presents umd-verification, an open-source tool that enforces the fulfillment of the QC requirements in an automated way for the continuous validation of the software products for scientific disposal. The umd-verification tool has been successfully integrated within the SWPP pipeline and is progressively supporting the full validation of the products in the UMD and CMD repositories. While the cost of supporting new products is dependant on the availability of Infrastructure as Code solutions to take over the deployment and high test coverage, the results obtained for the already integrated products are promising, as the time invested in the validation of products has been drastically reduced. Furthermore, automation adoption has brought along benefits for the reliability of the process, such as the removal of human-associated errors or the risk of regression of previously tested functionalities.

Virtual sensor as a service: a new multicriteria QoS-aware cloud service composition for IoT applications

A large number of connected devices made Internet of Things (IoT). IoT devices may provide the same service but with different quality parameters such as high availability, cost and delay. Nowadays cloud infrastructures provide an entry point for discovery, selection, fusion and consuming such distributed IoT services. Hence, a new kind of middleware service should be devised in cloud to select and compose the required services based on the end user quality of service requirements. This new kind of cloud service for IoT is named as virtual sensor. In this paper, we propose an architecture for such a virtual sensor service in cloud and propose a multi-objective metaheuristic algorithm for sensor-service selection and composition in cloud middleware. In particular, a quantum-inspired genetic algorithm-based approach is used to address the problem. Simulations with sample IoT workflows were conducted to evaluate efficiency and performance of the proposed method. Our proposed approach for selection and composition of IoT services yields about 60% improvement in overall quality of service of the virtual sensor compared to rival algorithms.

An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments.

Virtual sensors provisioning is a central issue for sensors cloud middleware since it is responsible for selecting physical nodes, usually from Wireless Sensor Networks (WSN) of different owners, to handle user’s queries or applications. Recent works perform provisioning by clustering sensor nodes based on the correlation measurements and then selecting as few nodes as possible to preserve WSN energy. However, such works consider only homogeneous nodes (same set of sensors). Therefore, those works are not entirely appropriate for sensor clouds, which in most cases comprises heterogeneous sensor nodes. In this paper, we propose ACxSIMv2, an approach to enhance the provisioning task by considering heterogeneous environments. Two main algorithms form ACxSIMv2. The first one, ACASIMv1, creates multi-dimensional clusters of sensor nodes, taking into account the measurements correlations instead of the physical distance between nodes like most works on literature. Then, the second algorithm, ACOSIMv2, based on an Ant Colony Optimization system, selects an optimal set of sensors nodes from to respond user’s queries while attending all parameters and preserving the overall energy consumption. Results from initial experiments show that the approach reduces significantly the sensor cloud energy consumption compared to traditional works, providing a solution to be considered in sensor cloud scenarios.

Web user interface based on OGC standards for sensor cloud using big data

Sensor cloud is the combination of wireless sensor networks and Cloud computing which performs real-time data acquisition and processing in a distributed environment. The infrastructure enables collection, processing, sharing, visualisation, archival and searching of large amounts of sensor data. It makes it possible for sharing computing resources and data, on an unprecedented scale, among an infinite number of geographically distributed groups. The paper provides an implementation of efficient web based technology for processing large, heterogeneous set of data based on Open Geospatial Consortium Standards. The proposed interface uses the sensor cloud middleware and wireless sensors for distributed applications that process large amounts of data. Sensor cloud middleware in the proposed work uses Open Stack and Hadoop framework along with utilities like generic modelling environment, HBase and Zabbix. The user interface with features such as viewing sensor data, scheduling sensors for data collection and alerting users have also been discussed.

Web user interface based on OGC standards for sensor cloud using big data

Sensor cloud is the combination of wireless sensor networks and Cloud computing which performs real-time data acquisition and processing in a distributed environment. The infrastructure enables collection, processing, sharing, visualisation, archival and searching of large amounts of sensor data. It makes it possible for sharing computing resources and data, on an unprecedented scale, among an infinite number of geographically distributed groups. The paper provides an implementation of efficient web based technology for processing large, heterogeneous set of data based on Open Geospatial Consortium Standards. The proposed interface uses the sensor cloud middleware and wireless sensors for distributed applications that process large amounts of data. Sensor cloud middleware in the proposed work uses Open Stack and Hadoop framework along with utilities like generic modelling environment, HBase and Zabbix. The user interface with features such as viewing sensor data, scheduling sensors for data collection and alerting users have also been discussed.

The Research on Monitor Model of Middleware Based on Cloud Computing

According to multi-levels and concurrency features of cloud computing services, a monitor model based on middleware is proposed. The model analyzes cloud middleware architecture, types and functions under the cloud computing environment, defines load size and designs middleware load algorithms. It also proposes evaluation of middleware performance constructs the overall structure and function of middleware monitor model and ensures safe and stable operation of the middleware. Simulation results shows that the model can achieve real-time performance monitoring of middleware, and improve the reliability of cloud computing services.

SmartCityWare: A Service-Oriented Middleware for Cloud and Fog Enabled Smart City Services

Smart cities are becoming a reality. Various aspects of modern cities are being automated and integrated with information and communication technologies to achieve higher functionality, optimized resources utilization, and management, and improved quality of life for the residents. Smart cities rely heavily on utilizing various software, hardware, and communication technologies to improve the operations in areas, such as healthcare, transportation, energy, education, logistics, and many others, while reducing costs and resources consumption. One of the promising technologies to support such efforts is the Cloud of Things (CoT). CoT provides a platform for linking the cyber parts of a smart city that are executed on the cloud with the physical parts of the smart city, including residents, vehicles, power grids, buildings, water networks, hospitals, and other resources. Another useful technology is Fog Computing, which extends the traditional Cloud Computing paradigm to the edge of the network to enable localized and real-time support for operating-enhanced smart city services. However, proper integration and efficient utilization of CoT and Fog Computing is not an easy task. This paper discusses how the service-oriented middleware (SOM) approach can help resolve some of the challenges of developing and operating smart city services using CoT and Fog Computing. We propose an SOM called SmartCityWare for effective integration and utilization of CoT and Fog Computing. SmartCityWare abstracts services and components involved in smart city applications as services accessible through the service-oriented model. This enhances integration and allows for flexible inclusion and utilization of the various services needed in a smart city application. In addition, we discuss the implementation and experimental issues of SmartCityWare and demonstrate its use through examples of smart city applications.

Enabling Workflow-Oriented Science Gateways to Access Multi-Cloud Systems

In this paper we present a solution to cloud-enable workflow-oriented science gateways. The integration mechanism described in the paper is a generic method that can be followed by other gateway developers. The paper describes the principles and the concrete ways to integrate science gateways with multi-cloud systems. The concrete example to demonstrate the integration principles builds on the integration of WS-PGRADE/gUSE and the CloudBroker Platform (CBP). The integration of WS-PGRADE and the CloudBroker Platform offers a complete cloud-enabled science gateway platform for a diverse set of use-cases and user communities, with the availability to use mainstream cloud middleware types and services (Amazon, IBM, OpenStack, OpenNebula). The advantage of the integrated WS-PGRADE and CloubBroker Platform system is that if a domain-specific science gateway is customized from WS-PGRADE gateway framework it immediately inherits this cloud access flexibility, i.e. the user community of that gateway can access all the cloud types enabled by the integrated system presented.

Analytics‐as‐a‐service in a multi‐cloud environment through semantically‐enabled hierarchical data processing

SummaryA large number of cloud middleware platforms and tools are deployed to support a variety of internet‐of‐things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration and cooperation and can also lead to innovative use of the data. Multi‐cloud, privacy‐aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics‐as‐a‐service providers. There is a lack of both architectural blueprints that can support such diverse, multi‐cloud environments and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data‐processing architecture that utilises semantics at all the levels of IoT stack in multi‐cloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads. Copyright © 2016 John Wiley & Sons, Ltd.

Research on an Approach of Cloud Workloads Deployment to Public Cloud Based on Open Source Standard

Cloud computing is a kind of formula mode that is based on internet, and in this way, the sharing software and hardware resource and information can be provided for the computer and the other terminal devices on demand. However, due to technical differences among main service vendors, there is a compatibility issue on appropriate format that is based on the virtual machine manager of different cloud platforms. Therefore, it brings some hinder to flexible deployment of cloud workloads to some extent. While with the great development of open source standers, such issues can be solved in another train of thought. In this paper, it proposes a constructive solution to deploy cloud workloads based on open source standers. In the whole structure, the key point is that a cloud middleware is introduced and the application of MapReduce algorithm. The cloud middleware contains four kinds of modules, namely, OVF converter, classifier, interface matched and security check. And the improved MapReduce algorithm is used to the classification phase of the cloud workloads. In the program, cloud workloads are sent to the cloud middleware, and in the process of classified → packaged →matching interface → security check → deployment, which can make cloud workloads deployed easily and flexibly. And this paper will elaborate the progress of each section through a theory case. The biggest construction of this program is manifested in four aspects that are function modular, operation independence, security and scalability. This program must be further improvement, based on the addition of functional roles and the development of open source standards, and has a strong theoretical and practical significance.

A Mobile Cloud Middleware to Support Mobility and Cloud Interoperability

With the recent advances in cloud computing and the improvement in the capabilities of mobile devices in terms of speed, storage, and computing power, Mobile Cloud Computing (MCC) is emerging as one of important branches of cloud computing. MCC is an extension of cloud computing with the support of mobility. In this paper, the authors first present the specific concerns and key challenges in mobile cloud computing. They then discuss the different approaches to tackle the main issues in MCC that have been introduced so far, and finally focus on describing the proposed overall architecture of a middleware that will contribute to providing mobile users data storage and processing services based on their mobile devices capabilities, availability, and usage. A prototype of the middleware is developed and three scenarios are described to demonstrate how the middleware performs in adapting the provision of cloud web services by transforming SOAP messages to REST and XML format to JSON, in optimizing the results by extracting relevant information, and in improving the availability by caching. Initial analysis shows that the mobile cloud middleware improves the quality of service for mobiles, and provides lightweight responses for mobile cloud services.

Cloud Middleware Combining the Functionalities of Message Passing and Scaling Control

The paper is focused on a solution providing middleware control over both message passing and scaling in a cloud environment. We provide a detailed analysis of the issue and present the restrictions particular to the load-balancing and communications systems. We also justify the need of such middleware development for a cloud infrastructure. The paper describes the problems emerged while working on a concrete project and presents the results obtained so far.