Jordi Vilaplana

A queuing theory model for cloud computing

The ability to deliver guaranteed QoS (Quality of Service) is crucial for the commercial success of cloud platforms. This paper presents a model based on queuing theory to study computer service QoS in cloud computing. Cloud platforms are modeled with an open Jackson network that can be used to determine and measure the QoS guarantees the cloud can offer regarding the response time. The analysis can be performed according to different parameters, such as the arrival rate of customer services and the number and service rate of processing servers, among others. Detailed results for the model are presented. When scaling the system and depending on the types of bottleneck in the system, we show how our model can provide us with the best option to guarantee QoS. The results obtained confirm the usefulness of the model presented for designing real cloud computing systems.

The cloud paradigm applied to e-Health.

BackgroundCloud computing is a new paradigm that is changing how enterprises, institutions and people understand, perceive and use current software systems. With this paradigm, the organizations have no need to maintain their own servers, nor host their own software. Instead, everything is moved to the cloud and provided on demand, saving energy, physical space and technical staff. Cloud-based system architectures provide many advantages in terms of scalability, maintainability and massive data processing.MethodsWe present the design of an e-health cloud system, modelled by an M/M/m queue with QoS capabilities, i.e. maximum waiting time of requests.ResultsDetailed results for the model formed by a Jackson network of two M/M/m queues from the queueing theory perspective are presented. These results show a significant performance improvement when the number of servers increases.ConclusionsPlatform scalability becomes a critical issue since we aim to provide the system with high Quality of Service (QoS). In this paper we define an architecture capable of adapting itself to different diseases and growing numbers of patients. This platform could be applied to the medical field to greatly enhance the results of those therapies that have an important psychological component, such as addictions and chronic diseases.

SLA-Aware Load Balancing in a Web-Based Cloud System over OpenStack

This paper focuses on the scalability problem in cloud-based systems when changing the computing requirements, this is, when there is a high degree of requesting service variability in cloud-computing environments. We study a specific scenario for web-based application deployed in a cloud system, where the number of requests can change with time. This paper deals with guaranteeing the SLA (Service-Level Agreement) in scalable clouds with web-based load variability.

S-PC: an e-treatment application for management of smoke-quitting patients.

The main objective of this paper is to present a new program that facilitates the management of people who want to quit smoking, implemented through an e-treatment software called S-PC (Smoker Patient Control). S-PC is a web-based application that manages groups of patients, provides a bidirectional communication through mobile text messages and e-mails between patients and clinicians and offers advice and control to keep track of the patients and their status. A total of 229 patients were enrolled in the study, randomly divided into two groups, although some variables were tested to ensure that there were no significant differences between the groups that could have an impact on the outcome of the treatment. There were no significant differences between the two groups regarding the ratio/number of males/females, tobacco dependence, co-oximetry, average cigarette consumption, current age and age when smoking started. The first group was made up of 104 patients (45.4% of the total) and followed a treatment that incorporated the S-PC tool, while the second one had 125 patients without the S-PC tool. S-PC was evaluated for its effectiveness at assisting the patients to give up smoking, and its effect on clinician time management. 74% of the S-PC group completed the treatment without relapses and remained abstinent three months after the completion of the treatment, understanding abstinence as being continuous (with no relapses allowed and co-oximetry below 1 ppm) from the day of stopping. In contrast only 45.6% of the No S-PC group completed the treatment without relapses and remained abstinent three months after completion of the treatment. The rate of admittance to the program has doubled in one year and patients went from having to wait for 3 months to be immediately admitted into the program. This therapeutic e-health program aims at maximizing the number of patients that a professional can effectively help to quit smoking. In addition, the system also detects patients who are not progressing appropriately, allowing the professional to improve their treatment parameters dynamically.

H-PC: a cloud computing tool for supervising hypertensive patients

Hypertension or high blood pressure is a condition on the rise. Not only does it affect the elderly but it is also increasingly spreading to younger sectors of the population. Treating it involves exhaustive monitoring of patients. Current health services can be improved to perform this task more effectively. A tool adapted to the particular requirements of hypertension can greatly facilitate monitoring and diagnosis. This paper presents the computer application Hypertension Patient Control (H-PC), which allows patients with hypertension to send their readings through mobile phone Short Message Service (SMS) or e-mail to a cloud computing datacenter. Through a graphic interface, clinicians can keep track of their patients, thus facilitating monitoring. Cloud-based datacenters provide a series of advantages in terms of scalability, maintainability, and massive data processing. However, the ability to guarantee Quality of Service (QoS) is crucial for the commercial success of cloud platforms. A novel and efficient cloud-based platform managing H-PC with QoS is also proposed in this paper.

Computer-assisted initial diagnosis of rare diseases

Introduction. Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Discovery. Finally, we also aimed at testing the performance of the prototype. Methods. Rare Disease Discovery uses the publicly available ORPHANET data set of association between rare diseases and their symptoms to automatically predict the most likely rare diseases based on a patient’s symptoms. We apply the method to retrospectively diagnose a cohort of 187 rare disease patients with confirmed diagnosis. Subsequently we test the precision, sensitivity, and global performance of the system under different scenarios by running large scale Monte Carlo simulations. All settings account for situations where absent and/or unrelated symptoms are considered in the diagnosis. Results. We find that this expert system has high diagnostic precision (≥80%) and sensitivity (≥99%), and is robust to both absent and unrelated symptoms. Discussion. The Rare Disease Discovery prediction engine appears to provide a fast and robust method for initial assisted differential diagnosis of rare diseases. We coupled this engine with a user-friendly web interface and it can be freely accessed at http://disease-discovery.udl.cat/. The code and most current database for the whole project can be downloaded from https://github.com/Wrrzag/DiseaseDiscovery/tree/no_classifiers.

A Green Strategy for Federated and Heterogeneous Clouds with Communicating Workloads

Providers of cloud environments must tackle the challenge of configuring their system to provide maximal performance while minimizing the cost of resources used. However, at the same time, they must guarantee an SLA (service-level agreement) to the users. The SLA is usually associated with a certain level of QoS (quality of service). As response time is perhaps the most widely used QoS metric, it was also the one chosen in this work. This paper presents a green strategy (GS) model for heterogeneous cloud systems. We provide a solution for heterogeneous job-communicating tasks and heterogeneous VMs that make up the nodes of the cloud. In addition to guaranteeing the SLA, the main goal is to optimize energy savings. The solution results in an equation that must be solved by a solver with nonlinear capabilities. The results obtained from modelling the policies to be executed by a solver demonstrate the applicability of our proposal for saving energy and guaranteeing the SLA.

A Green Scheduling Policy for Cloud Computing

This paper presents a power-aware scheduling policy algorithm called Green Preserving SLA (GPSLA) for cloud computing systems with high workload variability. GPSLA aims to guarantee the SLA (Service-Level Agreement) by minimizing the system response time and, at the same time, tries to reduce the energy consumption. We present a formal solution, based on linear programming, to assign the system load to the most powerful Virtual Machines, while respecting the SLA and lowering the power consumption as far as possible. GPSLA is thought for one node load-aware and jobs formed by embarrassingly parallel heterogeneous tasks.

TControl: A mobile app to follow up tobacco-quitting patients

BACKGROUND AND OBJECTIVE Tobacco smoking is a major risk factor for a wide range of respiratory and circulatory diseases in active and passive smokers. Well-designed campaigns are raising awareness to the problem and an increasing number of smokers seeks medical assistance to quit their habit. In this context, there is the need to develop mHealth Apps that assist and manage large smoke quitting programs in efficient and economic ways. OBJECTIVES Our main objective is to develop an efficient and free mHealth app that facilitates the management of, and assistance to, people who want to quit smoking. As secondary objectives, our research also aims at estimating the economic effect of deploying that App in the public health system. METHODS Using JAVA and XML we develop and deploy a new free mHealth App for Android, called TControl (Tobacco-quitting Control). We deploy the App at the Tobacco Unit of the Santa Maria Hospital in Lleida and determine its stability by following the crashes of the App. We also use a survey to test usability of the app and differences in aptitude for using the App in a sample of 31 patients. Finally, we use mathematical models to estimate the economic effect of deploying TControl in the Catalan public health system. RESULTS TControl keeps track of the smoke-quitting users, tracking their status, interpreting it, and offering advice and psychological support messages. The App also provides a bidirectional communication channel between patients and clinicians via mobile text messages. Additionally, registered patients have the option to interchange experiences with each other by chat. The App was found to be stable and to have high performances during startup and message sending. Our results suggest that age and gender have no statistically significant effect on patient aptitude for using TControl. Finally, we estimate that TControl could reduce costs for the Catalan public health system (CPHS) by up to € 400M in 10 years. CONCLUSIONS TControl is a stable and well behaved App, typically operating near optimal performance. It can be used independent of age and gender, and its wide implementation could decrease costs for the public health system.

BPcontrol. A Mobile App to Monitor Hypertensive Patients.

BACKGROUND Hypertension or high blood pressure is on the rise. Not only does it affect the elderly but is also increasingly spreading to younger sectors of the population. Treating this condition involves exhaustive monitoring of patients. The current mobile health services can be improved to perform this task more effectively. OBJECTIVE To develop a useful, user-friendly, robust and efficient app, to monitor hypertensive patients and adapted to the particular requirements of hypertension. METHODS This work presents BPcontrol, an Android and iOS app that allows hypertensive patients to communicate with their health-care centers, thus facilitating monitoring and diagnosis. Usability, robustness and efficiency factors for BPcontrol were evaluated for different devices and operating systems (Android, iOS and system-aware). Furthermore, its features were compared with other similar apps in the literature. RESULTS BPcontrol is robust and user-friendly. The respective start-up efficiency of the Android and iOS versions of BPcontrol were 2.4 and 8.8 times faster than a system-aware app. Similar values were obtained for the communication efficiency (7.25 and 11.75 times faster for the Android and iOS respectively). When comparing plotting performance, BPcontrol was on average 2.25 times faster in the Android case. Most of the apps in the literature have no communication with a server, thus making it impossible to compare their performance with BPcontrol. CONCLUSIONS Its optimal design and the good behavior of its facilities make BPcontrol a very promising mobile app for monitoring hypertensive patients.