The cloud paradigm applied to e-Health

Abstract

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.

A Systematic Literature Review of Cloud Computing in Ehealth

“…For applications based on the private cloud, Bahga et al [25] presented an achievement of sematic interoperability between different kinds of healthcare data, while DACRA [26] built a platform for interoperability on the syntax level. Vilaplana et al [27]used queuing theory as the basic means to model the performance of an eHealth system based on the private cloud. Van Gorp et al [28]applied virtualisation techniques to allow patients themselves to build their own lifelong PHRs.…”

MetReS: A Metabolic Reconstruction Database for Cloud Computing

“…They are also responsible for sending service requests to the storage servers, which in turn manage the database. We designed our Back-end to support multiple processing servers, so multiple concurrent requests can be serviced, and consequently, concurrent requests to the database can be delivered at the same time in order to guarantee SLA [18]. In doing so, we propose some solutions to provide a high level of QoS for the analysis of large data sets, thus determining a means to fix the SLA in accessing the database for the particular case of our subject applications.…”

Performance Analysis of Cloud Computing in Healthcare System Using Tandem Queues

“…Furthermore, they have obtained waiting time of a class of units or patients in both M/M/s queue and M/M/1 queue and total waiting time for this model. J. Vilaplana et al [10] have discussed the queueing model consisting two systems M/M/m in series for the cloud e-Health platform. The first system offers compute services, and the good QoS, in terms of average waiting times.…”

“…When the repeating units from the orbit retries j th times and finds the server busy return to the orbit with probability ψ in order to retry (j+1) th times again for service after some random amount of time or leave the system always with probability (1 -ψ). In the existing model described in [10], after completing service from M/M/s queue usually, clients / patients / users request medical staff for accessing cloud database with probability 'p' and leave the entire system after service completion or users / patients themselves directly access the cloud database with probability (1-p) and leave the entire system. But, the situations in which the users / clients / patients cannot access the database without medical staff assistance due to not knowing how to access directly or service not necessary from the second queue which were not considered.…”