Using Semantic Web Technologies to Query and Manage Information within Federated Cyber-Infrastructures

A standardized descriptive ontology supports efficient querying and manipulation of data from heterogeneous sources across boundaries of distributed infrastructures, particularly in federated environments. In this article, we present the Open-Multinet (OMN) set of ontologies, which were designed specifically for this purpose as well as to support management of life-cycles of infrastructure resources. We present their initial application in Future Internet testbeds, their use for representing and requesting available resources, and our experimental performance evaluation of the ontologies in terms of querying and translation times. Our results highlight the value and applicability of Semantic Web technologies in managing resources of federated cyber-infrastructures.EC/FP7/318389/EU/Federation for FIRE/Fed4FIREEC/FP7/732638/EU/Federation for FIRE Plus/Fed4FIREplu

Adaptive learning-based resource management strategy in fog-to-cloud

Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ensure the commitment for bringing the higher computing facilities near to the edge of the network and also help the large-scale computing system to be more intelligent. As the F2C is in its infantile state, therefore one of the biggest challenges for this computing paradigm is to efficiently manage the computing resources. Mainly, to address this challenge, in this work, we have given our sole interest for designing the initial architectural framework to build a proper, adaptive and efficient resource management mechanism in F2C. F2C has been proposed as a combined, coordinated and hierarchical computing platform, where a vast number of heterogeneous computing devices are participating. Notably, their versatility creates a massive challenge for effectively handling them. Even following any large-scale smart computing system, it can easily recognize that various kind of services is served for different purposes. Significantly, every service corresponds with the various tasks, which have different resource requirements. So, knowing the characteristics of participating devices and system offered services is giving advantages to build effective and resource management mechanism in F2C-enabled system. Considering these facts, initially, we have given our intense focus for identifying and defining the taxonomic model for all the participating devices and system involved services-tasks. In any F2C-enabled system consists of a large number of small Internet-of-Things (IoTs) and generating a continuous and colossal amount of sensing-data by capturing various environmental events. Notably, this sensing-data is one of the key ingredients for various smart services which have been offered by the F2C-enabled system. Besides that, resource statistical information is also playing a crucial role, for efficiently providing the services among the system consumers. Continuous monitoring of participating devices generates a massive amount of resource statistical information in the F2C-enabled system. Notably, having this information, it becomes much easier to know the device's availability and suitability for executing some tasks to offer some services. Therefore, ensuring better service facilities for any latency-sensitive services, it is essential to securely distribute the sensing-data and resource statistical information over the network. Considering these matters, we also proposed and designed a secure and distributed database framework for effectively and securely distribute the data over the network. To build an advanced and smarter system is necessarily required an effective mechanism for the utilization of system resources. Typically, the utilization and resource handling process mainly depend on the resource selection and allocation mechanism. The prediction of resources (e.g., RAM, CPU, Disk, etc.) usage and performance (i.e., in terms of task execution time) helps the selection and allocation process. Thus, adopting the machine learning (ML) techniques is much more useful for designing an advanced and sophisticated resource allocation mechanism in the F2C-enabled system. Adopting and performing the ML techniques in F2C-enabled system is a challenging task. Especially, the overall diversification and many other issues pose a massive challenge for successfully performing the ML techniques in any F2C-enabled system. Therefore, we have proposed and designed two different possible architectural schemas for performing the ML techniques in the F2C-enabled system to achieve an adaptive, advance and sophisticated resource management mechanism in the F2C-enabled system. Our proposals are the initial footmarks for designing the overall architectural framework for resource management mechanism in F2C-enabled system.La tecnologia del segle XXI avança ràpidament i ens condueix cap a un nou món intel·ligent, creant nous models d'arquitectures informàtiques. Fog-to-Cloud (F2C) és un d’ells, i sorgeix per garantir el compromís d’acostar les instal·lacions informàtiques a prop de la xarxa i també ajudar el sistema informàtic a gran escala a ser més intel·ligent. Com que el F2C es troba en un estat preliminar, un dels majors reptes d’aquest paradigma tecnològic és gestionar eficientment els recursos informàtics. Per fer front a aquest repte, en aquest treball hem centrat el nostre interès en dissenyar un marc arquitectònic per construir un mecanisme de gestió de recursos adequat, adaptatiu i eficient a F2C.F2C ha estat concebut com una plataforma informàtica combinada, coordinada i jeràrquica, on participen un gran nombre de dispositius heterogenis. La seva versatilitat planteja un gran repte per gestionar-los de manera eficaç. Els serveis que s'hi executen consten de diverses tasques, que tenen requisits de recursos diferents. Per tant, conèixer les característiques dels dispositius participants i dels serveis que ofereix el sistema és un requisit per dissenyar mecanismes eficaços i de gestió de recursos en un sistema habilitat per F2C. Tenint en compte aquests fets, inicialment ens hem centrat en identificar i definir el model taxonòmic per a tots els dispositius i sistemes implicats en l'execució de tasques de serveis. Qualsevol sistema habilitat per F2C inclou en un gran nombre de dispositius petits i connectats (conegut com a Internet of Things, o IoT) que generen una quantitat contínua i colossal de dades de detecció capturant diversos events ambientals. Aquestes dades són un dels ingredients clau per a diversos serveis intel·ligents que ofereix F2C. A més, el seguiment continu dels dispositius participants genera igualment una gran quantitat d'informació estadística. En particular, en tenir aquesta informació, es fa molt més fàcil conèixer la disponibilitat i la idoneïtat dels dispositius per executar algunes tasques i oferir alguns serveis. Per tant, per garantir millors serveis sensibles a la latència, és essencial distribuir de manera equilibrada i segura la informació estadística per la xarxa. Tenint en compte aquests assumptes, també hem proposat i dissenyat un entorn de base de dades segura i distribuïda per gestionar de manera eficaç i segura les dades a la xarxa. Per construir un sistema avançat i intel·ligent es necessita un mecanisme eficaç per a la gestió de l'ús dels recursos del sistema. Normalment, el procés d’utilització i manipulació de recursos depèn principalment del mecanisme de selecció i assignació de recursos. La predicció de l’ús i el rendiment de recursos (per exemple, RAM, CPU, disc, etc.) en termes de temps d’execució de tasques ajuda al procés de selecció i assignació. Adoptar les tècniques d’aprenentatge automàtic (conegut com a Machine Learning, o ML) és molt útil per dissenyar un mecanisme d’assignació de recursos avançat i sofisticat en el sistema habilitat per F2C. L’adopció i la realització de tècniques de ML en un sistema F2C és una tasca complexa. Especialment, la diversificació general i molts altres problemes plantegen un gran repte per realitzar amb èxit les tècniques de ML. Per tant, en aquesta recerca hem proposat i dissenyat dos possibles esquemes arquitectònics diferents per realitzar tècniques de ML en el sistema habilitat per F2C per aconseguir un mecanisme de gestió de recursos adaptatiu, avançat i sofisticat en un sistema F2C. Les nostres propostes són els primers passos per dissenyar un marc arquitectònic general per al mecanisme de gestió de recursos en un sistema habilitat per F2C.Postprint (published version

A decision framework to mitigate vendor lock-in risks in cloud (SaaS category) migration.

Cloud computing offers an innovative business model to enterprise IT services consumption and delivery. However, vendor lock-in is recognised as being a major barrier to the adoption of cloud computing, due to lack of standardisation. So far, current solutions and efforts tackling the vendor lock-in problem have been confined to/or are predominantly technology-oriented. Limited studies exist to analyse and highlight the complexity of vendor lock-in problem existing in the cloud environment. Consequently, customers are unaware of proprietary standards which inhibit interoperability and portability of applications when taking services from vendors. The complexity of the service offerings makes it imperative for businesses to use a clear and well understood decision process to procure, migrate and/or discontinue cloud services. To date, the expertise and technological solutions to simplify such transition and facilitate good decision making to avoid lock-in risks in the cloud are limited. Besides, little research investigations have been carried out to provide a cloud migration decision framework to assist enterprises to avoid lock-in risks when implementing cloud-based Software-as-a-Service (SaaS) solutions within existing environments. Such decision framework is important to reduce complexity and variations in implementation patterns on the cloud provider side, while at the same time minimizing potential switching cost for enterprises by resolving integration issues with existing IT infrastructures. Thus, the purpose of this thesis is to propose a decision framework to mitigate vendor lock-in risks in cloud (SaaS) migration. The framework follows a systematic literature review and analysis to present research findings containing factual and objective information, and business requirements for vendor-neutral interoperable cloud services, and/or when making architectural decisions for secure cloud migration and integration. The underlying research procedure for this thesis investigation consists of a survey based on qualitative and quantitative approaches conducted to identify the main risk factors that give rise to cloud computing lock-in situations. Epistemologically, the research design consists of two distinct phases. In phase 1, qualitative data were collected using open-ended interviews with IT practitioners to explore the business-related issues of vendor lock-in affecting cloud adoption. Whereas the goal of phase 2 was to identify and evaluate the risks and opportunities of lock-in which affect stakeholders’ decision-making about migrating to cloud-based solutions. In synthesis, the survey analysis and the framework proposed by this research (through its step-by-step approach), provides guidance on how enterprises can avoid being locked to individual cloud service providers. This reduces the risk of dependency on a cloud provider for service provision, especially if data portability, as the most fundamental aspect, is not enabled. Moreover, it also ensures appropriate pre-planning and due diligence so that the correct cloud service provider(s) with the most acceptable risks to vendor lock-in is chosen, and that the impact on the business is properly understood (upfront), managed (iteratively), and controlled (periodically). Each decision step within the framework prepares the way for the subsequent step, which supports a company to gather the correct information to make a right decision before proceeding to the next step. The reason for such an approach is to support an organisation with its planning and adaptation of the services to suit the business requirements and objectives. Furthermore, several strategies are proposed on how to avoid and mitigate lock-in risks when migrating to cloud computing. The strategies relate to contract, selection of vendors that support standardised formats and protocols regarding data structures and APIs, negotiating cloud service agreements (SLA) accordingly as well as developing awareness of commonalities and dependencies among cloud-based solutions. The implementation of proposed strategies and supporting framework has a great potential to reduce the risks of vendor lock-in