1,539 research outputs found
A framework for the characterization and analysis of software systems scalability
The term scalability appears frequently in computing literature, but it is a term that is poorly defined and
poorly understood. It is an important attribute of computer systems that is frequently asserted but rarely
validated in any meaningful, systematic way. The lack of a consistent, uniform and systematic treatment
of scalability makes it difficult to identify and avoid scalability problems, clearly and objectively describe
the scalability of software systems, evaluate claims of scalability, and compare claims from different
sources.
This thesis provides a definition of scalability and describes a systematic framework for the characterization
and analysis of software systems scalability. The framework is comprised of a goal-oriented
approach for describing, modeling and reasoning about scalability requirements, and an analysis technique
that captures the dependency relationships that underlie typical notions of scalability. The framework
is validated against a real-world data analysis system and is used to recast a number of examples
taken from the computing literature and from industry in order to demonstrate its use across different
application domains and system designs
Mobihealth: mobile health services based on body area networks
In this chapter we describe the concept of MobiHealth and the approach developed during the MobiHealth project (MobiHealth, 2002). The concept was to bring together the technologies of Body Area Networks (BANs), wireless broadband communications and wearable medical devices to provide mobile healthcare services for patients and health professionals. These technologies enable remote patient care services such as management of chronic conditions and detection of health emergencies. Because the patient is free to move anywhere whilst wearing the MobiHealth BAN, patient mobility is maximised. The vision is that patients can enjoy enhanced freedom and quality of life through avoidance or reduction of hospital stays. For the health services it means that pressure on overstretched hospital services can be alleviated
Recommended from our members
A distributed simulation methodology for large-scale hybrid modelling and simulation of emergency medical services
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHealthcare systems are traditionally characterised by complexity and heterogeneity. With the continuous increase in size and shrinkage of available resources, the healthcare sector faces the challenge of delivering high quality services with fewer resources. Healthcare organisations cannot be seen in isolation since the services of one such affects the performance of other healthcare organisations. Efficient management and forward planning, not only locally but rather across the whole system, could support healthcare sector to overcome the challenges. An example of closely interwoven organisations within the healthcare sector is the emergency medical services (EMS). EMS operate in a region and usually consist of one ambulance
service and the available accident and emergency (A&E) departments within the coverage area. EMS provide, mainly, pre-hospital treatment and transport to the appropriate A&E units. The life-critical nature of EMS demands continuous systems improvement practices. Modelling and Simulation (M&S) has been used to analyse either the ambulance services or the A&E departments. However, the size and complexity of EMS systems constitute the conventional M&S techniques inadequate to model the system as a whole. This research adopts the approach of distributed simulation to model all the EMS components as individual and composable simulations that are able to run as standalone simulation, as well as federates in a distributed simulation (DS) model. Moreover, the hybrid approach connects agent-based simulation (ABS) and discrete event simulation (DES) models in order to accommodate the heterogeneity of the EMS components. The proposed FIELDS Framework for Integrated EMS Large-scale Distributed Simulation supports the re-use of
existing, heterogeneous models that can be linked with the High Level Architecture (HLA) protocol for distributed simulation in order to compose large-scale simulation models. Based on FIELDS, a prototype ABS-DES distributed simulation EMS model was developed based on the London EMS. Experiments were conducted with the model and the system was tested in terms of performance and scalability measures to assess the feasibility of the proposed approach. The yielded results indicate that it is feasible to develop hybrid DS models of EMS that enables holistic analysis of the system and support model re-use. The main contributions of this thesis is a distributed simulation methodology that derived along the process of conducting this project, the FIELDS framework for hybrid EMS distributed simulation studies that support re-use of existing simulation models, and a prototype distributed simulation model that can be potentially used as a tool for EMS analysis and improvement.MATCH Programm
Attributes of innovations and approaches to scalability - lessons from a national program to extend the scope of practice of health professionals
The context for the paper was the evaluation of a national program in Australia to investigate extended scopes of practice for health professionals (paramedics, physiotherapists, and nurses). The design of the evaluation involved a mixed-methods approach with multiple data sources. Four multidisciplinary models of extended scope of practice were tested over an 18-month period, involving 26 organizations, 224 health professionals, and 36 implementation sites. The evaluation focused on what could be learned to inform scaling up the extended scopes of practice on a national scale. The evaluation findings were used to develop a conceptual framework for use by clinicians, managers, and policy makers to determine appropriate strategies for scaling up effective innovations. Development of the framework was informed by the literature on the diffusion of innovations, particularly an understanding that certain attributes of innovations influence adoption. The framework recognizes the role played by three groups of stakeholders: evidence producers, evidence influencers, and evidence adopters. The use of the framework is illustrated with four case studies from the evaluation. The findings demonstrate how the scaling up of innovations can be influenced by three quite distinct approaches - letting adoption take place in an uncontrolled, unplanned, way; actively helping the process of adoption; or taking deliberate steps to ensure that adoption takes place. Development of the conceptual framework resulted in two sets of questions to guide decisions about scalability, one for those considering whether to adopt the innovation (evidence adopters), and the other for those trying to decide on the optimal strategy for dissemination (evidence influencers)
EVA: Emergency Vehicle Allocation
Emergency medicine plays a critical role in the development of a community, where the goal is
to provide medical assistance in the shortest possible time. Consequently, the systems that
support emergency operations need to be robust, efficient, and effective when managing the
limited resources at their disposal. To achieve this, operators analyse historical data in search
of patterns present in past occurrencesthat could help predict future call volume. This is a time consuming and very complex task that could be solved by the usage of machine learning
solutions, which have been performed appropriately in the context of time series forecasting.
Only after the future demands are known, the optimization of the distribution of available
assets can be done, for the purpose of supporting high-density zones. The current works aim to
propose an integrated system capable of supporting decision-making emergency operations in
a real-time environment by allocating a set of available units within a service area based on
hourly call volume predictions. The suggested system architecture employs a microservices
approach along with event-based communications to enable real-time interactions between
every component. This dissertation focuses on call volume forecasting and optimizing allocation
components. A combination of traditional time series and deep learning models was used to
model historical data from Virginal Beach emergency calls between the years 2010 and 2018,
combined with several other features such as weather-related information. Deep learning
solutions offered better error metrics, with WaveNet having an MAE value of 0.04. Regarding
optimizing emergency vehicle location, the proposed solution is based on a Linear Programming
problem to minimize the number of vehicles in each station, with a neighbour mechanism,
entitled EVALP-NM, to add a buffer to stations near a high-density zone. This solution was also
compared against a Genetic Algorithm that performed significantly worse in terms of execution
time and outcomes. The performance of EVALP-NM was tested against simulations with
different settings like the number of zones, stations, and ambulances.A medicina de emergência desempenha um papel fundamental no desenvolvimento da
Sociedade, onde o objetivo é prestar assistência médica no menor tempo possível.
Consequentemente, os sistemas que apoiam as operações de emergência precisam de ser
robustos, eficientes e eficazes na gestão dos recursos limitados. Para isso, são analisados dados
históricos no intuito de encontrar padrões em ocorrências passadas que possam ajudar a prever
o volume futuro de chamadas. Esta é uma tarefa demorada e muito complexa que poderia ser
resolvida com o uso de soluções de Machine Learning, que têm funcionado adequadamente no
contexto da previsão de séries temporais. Só depois de conhecida a demanda futura poderá ser
feita a otimização da distribuição dos recursos disponíveis, com o objetivo de suportar zonas de
elevada densidade populacional. O presente trabalho tem como objetivo propor um sistema
integrado capaz de apoiar a tomada de decisão em operações de emergência num ambiente de
tempo real, atribuindo um conjunto de unidades disponíveis dentro de uma área de serviço
com base em previsões volume de chamadas a cada hora. A arquitetura de sistema sugerida
emprega uma abordagem de microserviços juntamente com comunicações baseadas em
eventos para permitir interações em tempo real entre os componentes. Esta dissertação centra se nos componentes de previsão do volume de chamadas e otimização da atribuição. Foram
usados modelos de séries temporais tradicionais e Deep Learning para modelar dados históricos
de chamadas de emergência de Virginal Beach entre os anos de 2010 e 2018, combinadas com
informações relacionadas ao clima. As soluções de Deep Learning ofereceram melhores
métricas de erro, com WaveNet a ter um valor MAE de 0,04. No que diz respeito à otimização
da localização dos veículos de emergência, a solução proposta baseia-se num problema de
Programação Linear para minimizar o número de veículos em cada estação, com um mecanismo
de vizinho, denominado EVALP-NM, para adicionar unidades adicionais às estações próximas
de uma zona de alta densidade de chamadas. Esta solução foi comparada com um algoritmo
genético que teve um desempenho significativamente pior em termos de tempo de execução
e resultados. O desempenho do EVALP-NM foi testado em simulações com configurações
diferentes, como número de zonas, estações e ambulâncias
Distributed coordination in unstructured intelligent agent societies
Current research on multi-agent coordination and distributed problem
solving is still not robust or scalable enough to build large real-world
collaborative agent societies because it relies on either centralised components
with full knowledge of the domain or pre-defined social structures.
Our approach allows overcoming these limitations by using
a generic coordination framework for distributed problem solving on
totally unstructured environments that enables each agent to decompose
problems into sub-problems, identify those which it can solve
and search for other agents to delegate the sub-problems for which it
does not have the necessary knowledge or resources. Regarding the
problem decomposition process, we have developed two distributed
versions of the Graphplan planning algorithm. To allow an agent
to discover other agents with the necessary skills for dealing with
unsolved sub-problems, we have created two peer-to-peer search algorithms
that build and maintain a semantic overlay network that
connects agents relying on dependency relationships, which improves
future searches. Our approach was evaluated using two different scenarios,
which allowed us to conclude that it is efficient, scalable and
robust, allowing the coordinated distributed solving of complex problems
in unstructured environments without the unacceptable assumptions
of alternative approaches developed thus far.As abordagens actuais de coordenação multi-agente e resolução distribuída de problemas não são suficientemente robustas ou escaláveis
para criar sociedades de agentes colaborativos uma vez que assentam
ou em componentes centralizados com total conhecimento do
domínio ou em estruturas sociais pré-definidas. A nossa abordagem
permite superar estas limitações através da utilização de um algoritmo
genérico de coordenação de resolução distribuída de problemas
em ambientes totalmente não estruturados, o qual permite a cada
agente decompor problemas em sub-problemas, identificar aqueles que
consegue resolver e procurar outros agentes a quem delegar os subproblemas
para os quais não tem conhecimento suficiente. Para a
decomposição de problemas, criámos duas versões distribuídas do algoritmo
de planeamento Graphplan. Para procurar os agentes com as
capacidades necessárias à resolução das partes não resolvidas do problema,
criámos dois algoritmos de procura que constroem e mantêm
uma camada de rede semântica que relaciona agentes dependentes
com o fim de facilitar as procuras. A nossa abordagem foi avaliada
em dois cenários diferentes, o que nos permitiu concluir que ´e uma
abordagem eficiente, escalável e robusta, possibilitando a resolução
distribuída e coordenada de problemas complexos em ambientes não
estruturados sem os pressupostos inaceitáveis em que assentava o trabalho
feito até agora
Distributed network and service architecture for future digital healthcare
According to World Health Organization (WHO), the worldwide prevalence of chronic diseases increases fast and new threats, such as Covid-19 pandemic, continue to emerge, while the aging population continues decaying the dependency ratio. These challenges will cause a huge pressure on the efficacy and cost-efficiency of healthcare systems worldwide. Thanks to the emerging technologies, such as novel medical imaging and monitoring instrumentation, and Internet of Medical Things (IoMT), more accurate and versatile patient data than ever is available for medical use. To transform the technology advancements into better outcome and improved efficiency of healthcare, seamless interoperation of the underlying key technologies needs to be ensured. Novel IoT and communication technologies, edge computing and virtualization have a major role in this transformation. In this article, we explore the combined use of these technologies for managing complex tasks of connecting patients, personnel, hospital systems, electronic health records and medical instrumentation. We summarize our joint effort of four recent scientific articles that together demonstrate the potential of the edge-cloud continuum as the base approach for providing efficient and secure distributed e-health and e-welfare services. Finally, we provide an outlook for future research needs
- …