5,448 research outputs found
Composing Distributed Data-intensive Web Services Using a Flexible Memetic Algorithm
Web Service Composition (WSC) is a particularly promising application of Web
services, where multiple individual services with specific functionalities are
composed to accomplish a more complex task, which must fulfil functional
requirements and optimise Quality of Service (QoS) attributes, simultaneously.
Additionally, large quantities of data, produced by technological advances,
need to be exchanged between services. Data-intensive Web services, which
manipulate and deal with those data, are of great interest to implement
data-intensive processes, such as distributed Data-intensive Web Service
Composition (DWSC). Researchers have proposed Evolutionary Computing (EC)
fully-automated WSC techniques that meet all the above factors. Some of these
works employed Memetic Algorithms (MAs) to enhance the performance of EC
through increasing its exploitation ability of in searching neighbourhood area
of a solution. However, those works are not efficient or effective. This paper
proposes an MA-based approach to solving the problem of distributed DWSC in an
effective and efficient manner. In particular, we develop an MA that hybridises
EC with a flexible local search technique incorporating distance of services.
An evaluation using benchmark datasets is carried out, comparing existing
state-of-the-art methods. Results show that our proposed method has the highest
quality and an acceptable execution time overall.Comment: arXiv admin note: text overlap with arXiv:1901.0556
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
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
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