2,079 research outputs found
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A monitoring approach for runtime service discovery
Effective runtime service discovery requires identification of services based on different service characteristics such as structural, behavioural, quality, and contextual characteristics. However, current service registries guarantee services described in terms of structural and sometimes quality characteristics and, therefore, it is not always possible to assume that services in them will have all the characteristics required for effective service discovery. In this paper, we describe a monitor-based runtime service discovery framework called MoRSeD. The framework supports service discovery in both push and pull modes of query execution. The push mode of query execution is performed in parallel to the execution of a service-based system, in a proactive way. Both types of queries are specified in a query language called SerDiQueL that allows the representation of structural, behavioral, quality, and contextual conditions of services to be identified. The framework uses a monitor component to verify if behavioral and contextual conditions in the queries can be satisfied by services, based on translations of these conditions into properties represented in event calculus, and verification of the satisfiability of these properties against services. The monitor is also used to support identification that services participating in a service-based system are unavailable, and identification of changes in the behavioral and contextual characteristics of the services. A prototype implementation of the framework has been developed. The framework has been evaluated in terms of comparison of its performance when using and when not using the monitor component
Investigating Decision Support Techniques for Automating Cloud Service Selection
The compass of Cloud infrastructure services advances steadily leaving users
in the agony of choice. To be able to select the best mix of service offering
from an abundance of possibilities, users must consider complex dependencies
and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal
on investigating an intelligent decision support system for selecting Cloud
based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac
Towards robust and reliable multimedia analysis through semantic integration of services
Thanks to ubiquitous Web connectivity and portable multimedia devices, it has never been so easy to produce and distribute new multimedia resources such as videos, photos, and audio. This ever-increasing production leads to an information overload for consumers, which calls for efficient multimedia retrieval techniques. Multimedia resources can be efficiently retrieved using their metadata, but the multimedia analysis methods that can automatically generate this metadata are currently not reliable enough for highly diverse multimedia content. A reliable and automatic method for analyzing general multimedia content is needed. We introduce a domain-agnostic framework that annotates multimedia resources using currently available multimedia analysis methods. By using a three-step reasoning cycle, this framework can assess and improve the quality of multimedia analysis results, by consecutively (1) combining analysis results effectively, (2) predicting which results might need improvement, and (3) invoking compatible analysis methods to retrieve new results. By using semantic descriptions for the Web services that wrap the multimedia analysis methods, compatible services can be automatically selected. By using additional semantic reasoning on these semantic descriptions, the different services can be repurposed across different use cases. We evaluated this problem-agnostic framework in the context of video face detection, and showed that it is capable of providing the best analysis results regardless of the input video. The proposed methodology can serve as a basis to build a generic multimedia annotation platform, which returns reliable results for diverse multimedia analysis problems. This allows for better metadata generation, and improves the efficient retrieval of multimedia resources
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An architecture for certification-aware service discovery
Service-orientation is an emerging paradigm for building complex systems based on loosely coupled components, deployed and consumed over the network. Despite the original intent of the paradigm, its current instantiations are limited to a single trust domain (e.g., a single organization). Also, some of the key promises of service-orientation - such as the dynamic orchestration of externally provided software services, using runtime service discovery and deployment - are still unachieved. One of the main reasons for this is the trust gap that normally arises when software services, offered by previously unknown providers, are to be selected at run-time, without any human intervention. To close this gap, the concept of machine-readable security certificates (called asserts) has been recently introduced, which paves the way to automated processing about security properties of services. Similarly to current security certification schemes, the assessment of the security properties of a service is delegated to an independent third party (certification authority), who issues a corresponding assert, bound to the service. In this paper, we propose an architecture, which exploits the assert concept to realise a certification-aware service discovery framework. The architecture supports the discovery of single services based on certified security properties (in additional to the usual functional properties), as well as the dynamic synthesis of service compositions, that satisfy the given security properties. The architecture is extensible, thus allowing for a range of domain specific matchmaking components, to cover dimensions related to, e.g., performance, cost and other non-functional characteristics
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of
their customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the
Cloud computing providers are unable to predict geographic distribution of
users consuming their services, hence the load coordination must happen
automatically, and distribution of services must change in response to changes
in the load. To counter this problem, we advocate creation of federated Cloud
computing environment (InterCloud) that facilitates just-in-time,
opportunistic, and scalable provisioning of application services, consistently
achieving QoS targets under variable workload, resource and network conditions.
The overall goal is to create a computing environment that supports dynamic
expansion or contraction of capabilities (VMs, services, storage, and database)
for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across
multiple vendor clouds. We have validated our approach by conducting a set of
rigorous performance evaluation study using the CloudSim toolkit. The results
demonstrate that federated Cloud computing model has immense potential as it
offers significant performance gains as regards to response time and cost
saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape
Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing
Automatic service composition in mobile and pervasive computing faces many
challenges due to the complex and highly dynamic nature of the environment.
Common approaches consider service composition as a decision problem whose
solution is usually addressed from optimization perspectives which are not
feasible in practice due to the intractability of the problem, limited
computational resources of smart devices, service host's mobility, and time
constraints to tailor composition plans. Thus, our main contribution is the
development of a cognitively-inspired agent-based service composition model
focused on bounded rationality rather than optimality, which allows the system
to compensate for limited resources by selectively filtering out continuous
streams of data. Our approach exhibits features such as distributedness,
modularity, emergent global functionality, and robustness, which endow it with
capabilities to perform decentralized service composition by orchestrating
manifold service providers and conflicting goals from multiple users. The
evaluation of our approach shows promising results when compared against
state-of-the-art service composition models.Comment: This paper will appear on AIMS'19 (International Conference on
Artificial Intelligence and Mobile Services) on June 2
Parallel and Distributed Stream Processing: Systems Classification and Specific Issues
Deploying an infrastructure to execute queries on distributed data streams sources requires to identify a scalable and robust solution able to provide results which can be qualified. Last decade, different Data Stream Management Systems have been designed by exploiting new paradigm and technologies to improve performances of solutions facing specific features of data streams and their growing number. However, some tradeoffs are often achieved between performance of the processing, resources consumption and quality of results. This survey 5 suggests an overview of existing solutions among distributed and parallel systems classified according to criteria able to allow readers to efficiently identify relevant existing Distributed Stream Management Systems according to their needs ans resources
Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert
A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs' monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient's condition on the most suited device within the physician's reach
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