Jordi Mateo

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.

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.

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.

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.

An SLA and power-saving scheduling consolidation strategy for shared and heterogeneous clouds

This paper presents a power-aware scheduling policy algorithm of Virtual Machines into nodes called Green Cloud (GreenC) for Heterogeneous cloud systems. GreenC takes into account optimal assignments according to physical and virtual machine heterogeneity, the current host workload and communication between the different virtual machines. An initial test case has been performed by modelling the policies to be executed by a solver that demonstrates the applicability of our proposal for saving energy and also guaranteeing the QoS. The proposed policy has been implemented using the OpenStack software and the obtained results showed that energy consumption can be significantly lowered by applying GreenC to allocate virtual machines to physical hosts.

Database constraints applied to metabolic pathway reconstruction tools.

Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes.

Rare Disease Discovery: An Optimized Disease Ranking System

The initial diagnosis of rare diseases is difficult because they are infrequent and doctors do not often see or recognize their symptoms. Developing tools to assist in this diagnosis would provide a way to facilitate medical practice in this area. The broader goal of this project is to develop such a tool, which we name rare disease discovery (http://disease-discovery.udl.cat). This tool is designed to identify rare diseases on the basis of a patients symptoms. To create it, several software entities were designed and integrated. First, a database of symptoms associated with every human rare disease known was designed and implemented. This database was derived from information available from Orphanet. Orphanet provides gold-standard data regarding rare diseases in the world. Second, a user-friendly website was also designed, implemented, and connected to the database. This website connects the users, the database, and the third software entity, a disease prediction engine. Overall, we create an accurate, efficient, and user-friendly diagnosis tool that can be quickly learnt and handled by medical doctors for the prediction of rare diseases.