421 research outputs found
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
Cloud Service Selection System Approach based on QoS Model: A Systematic Review
The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects
Software Reliability in Semantic Web Service Composition Applications
Web Service Composition allows the development of easily reconfigurable applications that can be quickly adapted to business changes. Due to the shift in paradigm from traditional systems, new approaches are needed in order to evaluate the reliability of web service composition applications. In this paper we present an approach based on intelligent agents for semiautomatic composition as well as methods for assessing reliability. Abstract web services, corresponding to a group of services that accomplishes a specific functionality are used as a mean of assuring better system reliability. The model can be extended with other Quality of Services â QoS attributes.Software Reliability, Web Service Composition, Intelligent Agents
Hybrid Honey Bees Mating Optimization Algorithm for Identifying the Near-Optimal Solution in Web Service Composition
This paper addresses the problem of optimality in semantic Web service composition by proposing a hybrid nature-inspired method for selecting the optimal or near-optimal solution in semantic Web Service Composition. The method hybridizes the Honey-Bees Mating Optimization algorithm with components inspired from genetic algorithms, reinforcement learning, and tabu search. To prove the necessity of hybridization, we have analyzed comparatively the experimental results provided by our hybrid selection algorithm versus the ones obtained with the classical Honey Bees Mating Optimization algorithm and with the genetic-inspired algorithm of Canfora et al
Service-oriented system engineering
Service-Oriented System Engineering (SOSE) is one of the emerging research areas that involves a number of research challenges in engineering service-oriented systems, the architecture and computing paradigm as well as the development and management of service-oriented systems. Service-Oriented Computing (SOC) exploits services as the fundamental elements for developing computer-based systems. It has been applied to various areas and promotes fundamental changes to system architecture, especially changing the way software systems are being analyzed, architected, designed, implemented, tested, evaluated, delivered, consumed, maintained and evolved. The innovations of SOC also offer many interesting avenues of research for scientific and industrial communities. In this paper, we present the concepts of the SOSE from the related work. The motivation, opportunities and challenges of the SOSE is highlighted thereafter. In addition to this, a brief overview of accepted papers in our Special Issue on SOSE is presented. Finally we highlight and summarize this paper.N/
Elephant Herding Optimization for Service Selection in QoS-Aware Web Service Composition
Web service composition combines available services
to provide new functionality. Given the number of available
services with similar functionalities and different non functional
aspects (QoS), the problem of finding a QoS-optimal web service
composition is considered as an optimization problem belonging to
NP-hard class. Thus, an optimal solution cannot be found by exact
algorithms within a reasonable time. In this paper, a meta-heuristic
bio-inspired is presented to address the QoS aware web service
composition; it is based on Elephant Herding Optimization (EHO)
algorithm, which is inspired by the herding behavior of elephant
group. EHO is characterized by a process of dividing and combining
the population to sub populations (clan); this process allows the
exchange of information between local searches to move toward
a global optimum. However, with Applying others evolutionary
algorithms the problem of early stagnancy in a local optimum
cannot be avoided. Compared with PSO, the results of experimental
evaluation show that our proposition significantly outperforms the
existing algorithm with better performance of the fitness value and a
fast convergence
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