Silviu Panica

Portable Cloud applications-From theory to practice

The adoption of the Cloud computing concept and its market development are nowadays hindered by the problem of application, data and service portability between Clouds. Open application programming interfaces, standards and protocols, as well as their early integration in the software stack of the new technological offers, are the key elements towards a widely accepted solution and the basic requirements for the further development of Cloud applications. An approach for a new set of APIs for Cloud application development is discussed in this paper from the point of view of portability. The first available, proof-of-the-concept, prototype implementation of the proposed API is integrated in a new open-source deployable Cloudware, namely mOSAIC, designed to deal with multiple Cloud usage scenarios and providing further solutions for portability beyond the API.

Architecturing a sky computing platform

Current Cloud computing solutions force people to be stranded into locked, proprietary systems. In order to overcome this limitation several efforts of the research community are addressing issues such as common programming models, open standard interfaces, adequate service level agreements or portability of applications. In this context, we argue about the need for an open-source Cloud application programming interface and a platform targeted for developing multi-Cloud oriented applications. This paper describes the approach that we propose for a platform that allows the deployment of component-based applications in Cloud environments taking into account multiple Cloud provider offers.

Serving legacy distributed applications by a self-configuring cloud processing platform

Clouds are intend mainly to host Web applications exposed through the interfaces of services but due to several particular and interesting aspects of the Clouds, like elasticity and scalability, legacy standalone and distributed applications can also benefit from the resources offered by the Clouds. We describe in this paper a simple-to-use platform allowing to run legacy applications on Cloud computing environments. This platform is one of the results of a large project dealing with the portability of the applications running on Clouds.

Towards Open-Source Cloudware

Current Platform as a Service solutions restrict the user to certain programming languages, paradigms or Cloud service types. A new deployable open-source Platform as a Service that includes a set of extensible APIs eases the deployment and control of the software stack required by Cloud applications. This paper describes shortly its vision, concepts and status of implementation with focus on a particular platform component used for controlling the deployment and behavior of the applications components.

A Hierarchical Approach in Distributed Evolutionary Algorithms for Multiobjective Optimization

This paper presents a hierarchical and easy configurable framework for the implementation of distributed evolutionary algorithms for multiobjective optimization problems. The proposed approach is based on a layered structure corresponding to different execution environments like single computers, computing clusters and grid infrastructures. Two case studies, one based on a classical test suite in multiobjective optimization and one based on a data mining task, are presented and the results obtained both on a local cluster of computers and in a grid environment illustrates the characteristics of the proposed implementation framework.

Remote Sensed Image Processing on Grids for Training in Earth Observation

Remote sensing involves techniques that use sensors to detect and record signals emanating from target of interest not in direct contact with the sensors. Remote sensing systems integrate cameras, scanners, radiometers, radar and other devices, and deal with the collection, processing, and distribution of large amounts of data. They often require massive computing resources to generate the data of interest for their users. Nowadays, remote sensing is mainly applied to satellite imagery. Satellites have proven in the last two decades their powerful capabilities to allow the Earth observation on a global scale. This observation is currently used in strategic planning and management of natural resources. The applications based on satellite data are often encountered in at least six disciplines: (1) agriculture, forestry and range resources in vegetation type, vigor and stress, biomass, soil conditions, or forest fire detection; (2) land use and mapping for classification, cartographic mapping, urban areas, or transportation networks; (3) geology for rock types, delineation, landforms, or regional structures detection; (4) water resources for water boundaries, surface, depth, volume, floods, snow areal, sediments, or irrigated fields detection; (5) oceanography and marine resources for marine organisms, turbidity patterns, or shoreline changes detection; (6) environment for surface mining, water pollution, air pollution, natural disasters, or defoliation detection. Current applications involving satellite data needs huge computational power and storage capacities. Grid computing technologies that have evolved in the last decade promise to make feasible the creation of an environment, for these kinds of applications, which can to handle hundreds of distributed databases, heterogeneous computing resources, and simultaneous users. Grid-based experimental platforms were developed already at this century’s beginning with a strong support from NASA and ESA. In this context, the chapter presents for the beginners an overview of the technological challenges and user requirements in remote sensed image processing, as well as the solutions provided by the Grid-based platforms built in the last decade. Section 2 starts with a short description of the basic principles of the satellite imagery, the technical problems and state of the art in solving them. It points also the fact that the training activities in Earth observation are not following the intensity of the research activities and there is a clear gap between the request for specialists and the labor market offer.

Fuzzy clustering of large satellite images using high performance computing

Fuzzy clustering is one of the most frequently used methods for identifying homogeneous regions in remote sensing images. In the case of large images, the computational costs of fuzzy clustering can be prohibitive unless high performance computing is used. Therefore, efficient parallel implementations are highly desirable. This paper presents results on the efficiency of a parallelization strategy for the Fuzzy c-Means (FCM) algorithm. In addition, the parallelization strategy has been extended in the case of two FCM variants, which incorporates spatial information (Spatial FCM and Gaussian Kernel-based FCM with spatial bias correction). The high-level requirements that guided the formulation of the proposed parallel implementations are: (i) find appropriate partitioning of large images in order to ensure a balanced load of processors; (ii) use as much as possible the collective computations; (iii) reduce the cost of communications between processors. The parallel implementations were tested through several test cases including multispectral images and images having a large number of pixels. The experiments were conducted on both a computational cluster and a BlueGene/P supercomputer with up to 1024 processors. Generally, good scalability was obtained both with respect to the number of clusters and the number of spectral bands.

Web and Grid services for training in Earth observation

Remote sensing instruments are producing daily huge quantities of data about Earth surface. The data processing and storing can be done nowadays only using wide-area distributed systems. The management of data distribution and fast processing is still an issue despite the increased availability of computing, storage and access facilities to supercomputing and data centers. The Grid architectures are responding partially to the needs of remote sensing community and this fact has been recognized in the latest years. While intensive research activities in the direction of building Grid-based platforms for remote sensing processing have been registered recently, the training activities are lagging behind. We discuss in this paper the special requirements of a Grid-based platform for training and high education in Earth observation and the technical solutions that have been proposed to overcome the problems of distributed data management. Moreover, a case study for training in archaeology using remote sensing data is described and discussed.

Cloud resource orchestration within an open‐source component‐based platform as a service

In the growing market of cloud computing dominated by proprietary solutions, the adoption of open‐source and deployable middleware that hides the service heterogeneity and ensure code portability can provide important benefits for the fast development of new services. Therefore, this paper exposes the mechanisms for orchestrating cloud‐enabled hardware and software resources supported by a recently developed open‐source platform as a service.Copyright

Distributed Resource Identification Service for Cloud Environments

Resource identification in massive deployed distributed systems, like the cloud environments, is a common problem when multiple types of resources need to be managed. This is the case of platform as a service (PaaS) where the platform offers different types resources that are deployed in heterogeneous systems. While most of the solutions on the market follow a predefined architecture using common naming services in a centralized manner this paper proposes a new approach based on a distributed naming and resource identification service. The proposed solution allows a client to consume or communicate with a resource without having to deal with resource location or discovery protocols.