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22 Papers

Cloud Computing Market Segmentation

Authors: Caesar Wu , Rajkumar Buyya and Kotagiri Ramamohanarao

Affiliation: Cloud Computing and Distribution Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne and Australia

Digital Object Identifier: 10.5220/0006928008880897

Proceedings of SE-CLOUD 2018, ISBN: 978-989-758-320-9

Quick Abstract

The topics of cloud pricing models and resources management have been receiving enormous attention recently. However, very few studies have considered the importance of cloud market segmentation. Moreover, there is no a better, practical and quantifiable solution for a cloud service providers (CSP) to segment cloud market. We propose a novel solution that combines both hierarchical clustering and time series forecasting on the basis of the classical theory of market segmentation. In comparison with some traditional approaches, such as nested, analytic, Delphi, and strategy-based approaches, our method is much more effective, flexible, measurable and practical for CSPs to implement their cloud market strategies by rolling out different pricing models. Our tested results and empirical analysis show that our solution can efficiently segment cloud markets and also predict the market demands. Our primary goal is to offer a new solution so that CSPs can tail its limited cloud resources for its targeted market or cloud customers.

Predicting the Stability of Large-scale Distributed Stream Processing Systems on the Cloud

Topics: QoS for Applications on Clouds; Resource Management; Cloud Application Scalability and Availability; Cloud Applications Performance and Monitoring

Authors: Tri Minh Truong , Aaron Harwood and Richard O. Sinnott

Affiliation: University of Melbourne, Australia

Digital Object Identifier: 10.5220/0006357606030610

Proceedings of CLOSER 2017, ISBN: 978-989-758-243-1

Quick Abstract

Large-scale topology-based stream processing systems are non-trivial to build and deploy. They require understanding of the performance, cost of deployment and considerations of potential downtime. Our work considers stability as a primary characteristic of these systems. By stability, we mean that unstable systems exhibit large-spikes in latency and can drop throughput frequently or unpredictably. Such instabilities can be due to variations of workloads or underlying hardware platforms that are often difficult to predict. To understand and tackle this for large-scale stream processing systems, we apply queueing theory and simulate the results through a series of experiments on the Cloud.

Recovery-Oriented Resource Management in Hybrid Cloud Environments

Topics: Cloud Management Platforms; Cloud Reliability and Resilience; Hybrid Clouds and their Integration; Cloud Computing Architecture; Resource Management

Authors: Yasser Aldwyan ¹ and Richard O. Sinnott ²

Affiliations: ¹ The University of Melbourne and Islamic University, Australia , ² The University of Melbourne, Australia

Digital Object Identifier: 10.5220/0006359602530265

Proceedings of CLOSER 2017, ISBN: 978-989-758-243-1

Quick Abstract

Cloud-based systems suffer from an increased risk of individual server failures due to their scale. When failures happen, resource utilization and system reliability can be negatively affected. Hybrid cloud models allow utilization of local resources in private clouds with resources from public clouds as and when needed through cloudbursting. There is an urgent need to develop cloudbursting approaches that are cognisant of the reliability and fault tolerance of external cloud environments. Recovery oriented computing (ROC) is a new approach for building reliable services that places emphasis on recovery from failures rather than avoiding them completely since even the most dependable systems will eventually fail. All fault tolerant techniques aim to reduce time to recover (TTR). In this paper, we develop a ROC-based fault tolerant approach for managing resources in hybrid clouds by proposing failure models with associated feedback control supporting a local resource-aware resource provisioning algorithm. We present a recovery-oriented virtual infrastructure management system (RVIMS). Results show that RVIMS is more reliable than those of single cloud environments even though TTR in the single cloud environments are about 10% less than those of RVIMS.

Exposome, Health and Biomedical Informatics - An Emerging Discipline and Its Interaction with Current Biomedical Informatics

Topics: Medical and Nursing Informatics; Wearable Health Informatics; Mobile Technologies for Healthcare Applications; e-Health; e-Health for Public Health

Authors: Guillermo Lopez-Campos ¹ , Riccardo Bellazzi ² and Fernando Martin-Sanchez ¹

Affiliations: ¹ The University of Melbourne, Australia , ² University of Pavia, Italy

Digital Object Identifier: 10.5220/0005278405800584

Proceedings of HEALTHINF 2015/BIOSTEC, ISBN: 978-989-758-068-0

Quick Abstract

In the last decade we have witnessed the raising of the exposome (the set of a life-long individual exposures) as an increasingly interesting area and discipline due to its relationship with health. These new approaches rely heavily in the use of different informatics related methods and are generating new data types that in the future should be handled by biomedical informatics. This position paper refers to some of the challenges that are related with these new approaches from a biomedical informatics perspective, describing the interactions with related disciplines such as bioinformatics, public health informatics and others. We discuss as well the role of the exposome in bringing new data types that might be handled by biomedical informatics in the context of Big and small data generated in this approaches and its relationship with the participatory medicine and how they could influence future health information systems. Finally, we consider that the current situation of the exposome resembles the early years of genomics, when it was clear that genomic information had a great potential for health and drove a discussion about how to better integrate and analyse the most relevant pieces of information for health purposes.

Related Ontology Subjects/Areas/Topics (21)

Biomedical Engineering	e-Health	Mobile Technologies for Healthcare Applications
Biomedical Signal Processing	e-Health for Public Health	Neural Rehabilitation
Cardiovascular Technologies	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Cloud Computing	Health Information Systems	Physiological Computing Systems
Computing and Telecommunications in Cardiology	Human-Computer Interaction	Platforms and Applications
Devices	Medical and Nursing Informatics	Software Engineering
Distributed and Mobile Software Systems	Mobile Technologies	Wearable Sensors and Systems

Fuzzy Inference System to Analyze Ordinal Variables - The Case of Evaluating Teaching Activity

Topics: Approximate Reasoning and Fuzzy Inference; Fuzzy Systems Design, Modeling and Control

Authors: Michele Lalla ¹ , Davide Ferrari ² and Tommaso Pirotti ¹

Affiliations: ¹ University of Modena and Reggio Emilia, Italy , ² University of Melbourne, Australia

Digital Object Identifier: 10.5220/0005054400250036

Proceedings of FCTA 2014/IJCCI, ISBN: 978-989-758-053-6

Quick Abstract

The handling of ordinal variables presents many difficulties in both the measurements phase and the statistical data analysis. Many efforts have been made to overcome them. An alternative approach to traditional methods used to process ordinal data has been developed over the last two decades. It is based on a fuzzy inference system and is presented, here, applied to the student evaluations of teaching data collected via Internet in Modena, during the academic year 2009/10, by a questionnaire containing items with a four-point Likert scale. The scores emerging from the proposed fuzzy inference system proved to be approximately comparable to scores obtained through the practical, but questionable, procedure based on the average of the item value labels. The fuzzification using a number of membership functions smaller than the number of modalities of input variables yielded outputs that were closer to the average of the item value labels. The Center-of-Area defuzzification method showed good performances and lower dispersion around the mean of the value labels.

Related Ontology Subjects/Areas/Topics (6)

Approximate Reasoning and Fuzzy Inference		Computational Intelligence		Fuzzy Systems Design, Modeling and Control
Artificial Intelligence		Fuzzy Systems		Soft Computing

Managing Data and Knowledge for the InmunoFlu Research Project

Topics: Knowledge Management; Medical and Nursing Informatics; Databases and Datawarehousing; Clinical Problems and Applications

Authors: G. Lopez Campos ¹ , Enrique de Andres ² , R. Almansa ³ , I. Martin-Loeches ⁴ , V. Lopez-Alonso ⁵ , J. F. Bermejo-Martin ³ and F. Martin-Sanchez ¹

Affiliations: ¹ University of Melbourne, Australia , ² University of Oviedo, Spain , ³ Hospital Clínico Universitario de Valladolid-IECSCYL, Spain , ⁴ Sabadell University Hospital, Spain , ⁵ Institute of Health Carlos III, Spain

Digital Object Identifier: 10.5220/0004227701810186

Proceedings of HEALTHINF 2013/BIOSTEC, ISBN: 978-989-8565-37-2

Quick Abstract

The InmunoFlu project was funded during 2009-2011 by the Government of Spain (Biomedical Research Fund-FIS) for the study of the H1N1pdm influenza. It was an integrative project were clinicians from intensive care units (ICUs) across Spain came together with fundamental researchers to analyse at the molecular level the H1N1 infection. The multidisciplinary and geographical dispersion of the participants required the development of data and knowledge management tools. The InmunoFlu database was developed as a tool for the storage of all clinical data from patients associated with the ICUs and for the subsequent clinical annotation of the samples used in the molecular analysis of the infection and host response. The dispersion of participants in different centres fostered the development of InmunoFlu Web portal, a collaborative web portal using web 2.0 technologies, which served as a knowledge management tool for the project community. The web portal enabled among other characteristics document sharing as well as other collaborative tools such as chat, wiki, etc... The use of both tools played a central role in the success of this complex project.

Related Ontology Subjects/Areas/Topics (15)

Biomedical Engineering	Databases and Datawarehousing	Knowledge Management
Cardiovascular Technologies	Enterprise Information Systems	Medical and Nursing Informatics
Clinical Problems and Applications	Health Engineering and Technology Applications	Ontologies and the Semantic Web
Computing and Telecommunications in Cardiology	Health Information Systems	Society, e-Business and e-Government
Data Engineering	Information Systems Analysis and Specification	Web Information Systems and Technologies

IMPROVED ROBUST PERFORMANCE IN A SYSTEM FOR AUTOMATIC ADMINISTRATION OF A VASOACTIVE DRUG

Topics: Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics; Cardiovascular Signals

Authors: Nicolò Malagutti ¹ , Arvin Dehghani ² and Rodney A. Kennedy ¹

Affiliations: ¹ The Australian National University, Australia , ² The University of Melbourne, Australia

Digital Object Identifier: 10.5220/0003794002820290

Proceedings of BIOSIGNALS 2012/BIOSTEC, ISBN: 978-989-8425-89-8

Quick Abstract

The problem of automatic administration of vasoactive drugs to regulate mean arterial pressure in surgical and postsurgical patients can be considered as a setpoint tracking problem involving a system which is characterised by significant modelling uncertainty in the form of uncertain parameters, unmodelled dynamics and disturbances. Yet, specific levels of performance are required and patient safety must be guaranteed. As part of the development process of a novel Multiple-Model Adaptive Control (MMAC) architecture for this application, we have adopted a mixed-ì synthesis approach to controller design. Simulation results show that the new controllers are capable of improved disturbance rejection and robustness even in the face of large system delays and parametric uncertainty.

Related Ontology Subjects/Areas/Topics (4)

Biomedical Engineering		Cardiovascular Signals		Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
Biomedical Signal Processing

A NEW AND EFFICIENT VESSEL SEGMENTATION METHOD FROM COLOR RETINAL IMAGES

Topics: Image Analysis and Processing; Detection and Identification

Authors: Alauddin Bhuiyan ¹ , Ryo Kawasaki ¹ , Ecosse Lamoureux ² , Kotagiri Ramamohanarao ¹ and Tien Y. Wong ²

Affiliations: ¹ The University of Melbourne, Australia , ² The University of Melbourne and National University of Singapore, Australia

Digital Object Identifier: 10.5220/0003161404670471

Proceedings of BIOSIGNALS 2011/BIOSTEC, ISBN: 978-989-8425-35-5

Quick Abstract

Retinal blood vessel changes (e.g., vessel caliber) are important indicators for earlier diagnosis of cardiovascular diseases. To quantify the changes automatically, a reliable vessel detection is essential. However, blood vessel detection in retinal image is complicated by a huge variation in a number of factors such as local contrast, vessel width and vessel central reflex. In this paper, we propose a new technique to detect retinal blood vessels which is able to address these issues. The core of the technique is a new vessel edge selection method which combines the method of finding edge pattern and edge profiling techniques. Experimental results show that 92.40% success rate in the identification of vessel start-points and 88.73% success rate in tracking the major vessels.

Related Ontology Subjects/Areas/Topics (6)

Applications and Services		Biomedical Signal Processing		Detection and Identification
Biomedical Engineering		Computer Vision, Visualization and Computer Graphics		Medical Image Detection, Acquisition, Analysis and Processing

AN AUTOMATED METHOD FOR RETINAL IMAGE MATCHING USING VASCULAR FEATURES

Topics: Biometrics; Pattern Recognition

Authors: Alauddin Bhuiyan , Baikunth Nath , Kotagiri Ramamohanarao and Tien Y. Wong

Affiliation: The University of Melbourne Australia, Australia

Digital Object Identifier: 10.5220/0003275903420347

Proceedings of BIOSIGNALS 2011/BIOSTEC, ISBN: 978-989-8425-35-5

Quick Abstract

In this paper, we propose a method for retinal image matching that can be used in image matching for person identification or patient longitudinal study. Vascular invariant features are extracted from the retinal image and a feature vector is constructed for each of the vessel-segments in the retinal blood vessels. The feature vectors are represented in a tree structure with maintaining the vessel-segments actual hierarchical positions. Using these feature vectors, corresponding images are matched. An image matching method is demonstrated which identifies the same vessel in the corresponding images for comparing the desired feature(s). Initial results demonstrate that the method is suitable for image matching and patient longitudinal study.

Related Ontology Subjects/Areas/Topics (19)

Applications	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Artificial Intelligence	Human-Computer Interaction	Pattern Recognition
Biomedical Engineering	Methodologies and Methods	Physiological Computing Systems
Biomedical Signal Processing	Multimedia	Sensor Networks
Biometrics	Multimedia Signal Processing	Soft Computing
Biometrics and Pattern Recognition	Neurocomputing	Telecommunications
Data Manipulation

DECENTRALISED ORCHESTRATION OF SERVICE-ORIENTED SCIENTIFIC WORKFLOWS

Topics: Web Services; Service-Oriented Architecture; Service Composition

Authors: Adam Barker ¹ and Rajkumar Buyya ²

Affiliations: ¹ University of St Andrews, United Kingdom , ² University of Melbourne, Australia

Digital Object Identifier: 10.5220/0003384302220231

Proceedings of CLOSER 2011, ISBN: 978-989-8425-52-2

Quick Abstract

Service-oriented workflows in the scientific domain are commonly composed as Directed Acyclic Graphs (DAGs), formed from a collection of vertices and directed edges. When orchestrating service-oriented DAGs, intermediate data are typically routed through a single centralised engine, which results in unnecessary data transfer, increasing the execution time of a workflow and causing the engine to become a performance bottleneck. This paper introduces an architecture for deploying and executing a service-oriented DAG-based workflows across a peer-to-peer proxy network. A workflow is divided into a set of vertices, disseminated to a group of proxies and executed without centralised control over a peer-to-peer proxy network. Through a Web services implementation, we demonstrate across PlanetLab that by reducing intermediate data transfer and by sharing the workload between distributed proxies, end-to-end workflows are sped up. Furthermore, our architecture is non-intrusive: Web services owned and maintained by different institutions do not have to be altered prior to execution.

Related Ontology Subjects/Areas/Topics (20)

Cloud Computing	Mobile Software and Services	Software Engineering
Collaboration and e-Services	Model-Driven Software Development	Software Engineering Methods and Techniques
Communication and Software Technologies and Architectures	Ontologies and the Semantic Web	Technology Platforms
Data Engineering	Service Composition and Mashups	Telecommunications
e-Business	Service-Oriented Architectures	Web Services
Enterprise Information Systems	Services Science	Wireless Information Networks and Systems
Languages, Tools and Architectures	Software Agents and Internet Computing