The Semantic Grid: A future e-Science infrastructure

e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practice–aspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid

Management of Data Access with Quality of Service in PL-Grid Environment

e-Science applications increasingly require both computational power and storage resources, currently supported with a certain level of quality. Since in the grid and cloud environments, where we can execute the e-Science applications, heterogeneity of storage systems is higher than that of computational power resources, optimization of data access defines one of challenging tasks nowadays. In this paper we present our approach to management of data access in the grid environment. The main issue is to organize data in such a way that users requirements in the form of QoS/SLA are met. For this purpose we make use of a storage monitoring system and a mass storage system model -- CMSSM. The experiments are performed in the PL-Grid environment

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

User centric community clouds

With the evolution in cloud technologies, users are becoming acquainted with seamless service provision. Nevertheless, clouds are not a user centric technology, and users become completely dependent on service providers. We propose a novel concept for clouds, where users self-organize to create their clouds. We present such an architecture for user-centric clouds, which relies on self-managed clouds based on doctrine and on identity management concepts

UBIDEV: a homogeneous service framework for pervasive computing environments

This dissertation studies the heterogeneity problem of pervasive computing system from the viewpoint of an infrastructure aiming to provide a service-oriented application model. From Distributed System passing through mobile computing, pervasive computing is presented as a step forward in ubiquitous availability of services and proliferation of interacting autonomous entities. To better understand the problems related to the heterogeneous and dynamic nature of pervasive computing environments, we need to analyze the structure of a pervasive computing system from its physical and service dimension. The physical dimension describes the physical environment together wit the technology infrastructure that characterizes the interactions and the relations within the environment; the service dimension represents the services (being them software or not) the environment is able to provide [Nor99]. To better separate the constrains and the functionalities of a pervasive computing system, this dissertation classifies it in terms of resources, context, classification, services, coordination and application. UBIDEV, as the key result of this dissertation, introduces a unified model helping the design and the implementation of applications for heterogeneous and dynamic environments. This model is composed of the following concepts: • Resource: all elements of the environment that are manipulated by the application, they are the atomic abstraction unit of the model. • Context: all information coming from the environment that is used by the application to adapts its behavior. Context contains resources and services and defines their role in the application. • Classification: the environment is classified according to the application ontology in order to ground the generic conceptual model of the application to the specific environment. It defines the basic semantic level of interoperability. • Service: the functionalities supported by the system; each service manipulates one or more resources. Applications are defined as a coordination and adaptation of services. • Coordination: all aspects related to service composition and execution as well as the use of the contextual information are captured by the coordination concept. • Application Ontology: represents the viewpoint of the application on the specific context; it defines the high level semantic of resources, services and context. Applying the design paradigm proposed by UBIDEV, allows to describe applications according to a Service Oriented Architecture[Bie02], and to focus on application functionalities rather than their relations with the physical devices. Keywords: pervasive computing, homogenous environment, service-oriented, heterogeneity problem, coordination model, context model, resource management, service management, application interfaces, ontology, semantic services, interaction logic, description logic.Questa dissertazione studia il problema della eterogeneit`a nei sistemi pervasivi proponendo una infrastruttura basata su un modello orientato ai servizi. I sistemi pervasivi sono presentati come un’evoluzione naturale dei sistemi distribuiti, passando attraverso mobile computing, grazie ad una disponibilit`a ubiqua di servizi (sempre, ovunque ed in qualunque modo) e ad loro e con l’ambiente stesso. Al fine di meglio comprendere i problemi legati allintrinseca eterogeneit`a dei sistemi pervasivi, dobbiamo prima descrivere la struttura fondamentale di questi sistemi classificandoli attraverso la loro dimensione fisica e quella dei loro servizi. La dimensione fisica descrive l’ambiente fisico e tutti i dispositivi che fanno parte del contesto della applicazione. La dimensione dei servizi descrive le funzionalit`a (siano esse software o no) che l’ambiente `e in grado di fornire [Nor99]. I sistemi pervasivi vengono cos`ı classificati attraverso una metrica pi `u formale del tipo risorse, contesto, servizi, coordinazione ed applicazione. UBIDEV, come risultato di questa dissertazione, introduce un modello uniforme per la descrizione e lo sviluppo di applicazioni in ambienti dinamici ed eterogenei. Il modello `e composto dai seguenti concetti di base: • Risorse: gli elementi dell’ambiente fisico che fanno parte del modello dellapplicazione. Questi rappresentano l’unit`a di astrazione atomica di tutto il modello UBIDEV. • Contesto: le informazioni sullo stato dell’ambiente che il sistema utilizza per adattare il comportamento dell’applicazione. Il contesto include informazioni legate alle risorse, ai servizi ed alle relazioni che li legano. • Classificazione: l’ambiente viene classificato sulla base di una ontologia che rappresenta il punto di accordo a cui tutti i moduli di sistema fanno riferimento. Questa classificazione rappresenta il modello concettuale dell’applicazione che si riflette sull’intero ambiente. Si definisce cos`ı la semantica di base per tutto il sistema. • Servizi: le funzionalit`a che il sistema `e in grado di fornire; ogni servizio `e descritto in termini di trasformazione di una o pi `u risorse. Le applicazioni sono cos`ı definite in termini di cooperazione tra servizi autonomi. • Coordinazione: tutti gli aspetti legati alla composizione ed alla esecuzione di servizi cos`ı come l’elaborazione dell’informazione contestuale. • Ontologia dell’Applicazione: rappresenta il punto di vista dell’applicazione; definisce la semantica delle risorse, dei servizi e dell’informazione contestuale. Applicando il paradigma proposto da UBIDEV, si possono descrivere applicazioni in accordo con un modello Service-oriented [Bie02] ed, al tempo stesso, ridurre l’applicazione stessa alle sue funzionalit`a di alto livello senza intervenire troppo su come queste funzionalit` a devono essere realizzate dalle singole componenti fisiche

Resource Management in Grids: Overview and a discussion of a possible approach for an Agent-Based Middleware

14 pagesInternational audienceResource management and job scheduling are important research issues in computational grids. When software agents are used as resource managers and brokers in the Grid a number of additional issues and possible approaches materialize. The aim of this chapter is twofold. First, we discuss traditional job scheduling in grids, and when agents are utilized as grid middleware. Second, we use this as a context for discussion of how job scheduling can be done in the agent-based system under development