A view at desktop clouds

Cloud has emerged as a new computing paradigm that promises to move into computing-as-utility era. Desktop Cloud is a new type of Cloud computing. It merges two computing models: Cloud computing and volunteer computing. The aim of Desktop Cloud is to provide Cloud services out of infrastructure that is not made for this purpose in order to reduce running and maintenance costs. This paper discusses this new type of Cloud by comparing it with current Cloud and Desktop Grid models. It, also, presents several research challenges in Desktop Cloud that require further attention

Toward a framework for data quality in cloud-based health information system

This Cloud computing is a promising platform for health information systems in order to reduce costs and improve accessibility. Cloud computing represents a shift away from computing being purchased as a product to be a service delivered over the Internet to customers. Cloud computing paradigm is becoming one of the popular IT infrastructures for facilitating Electronic Health Record (EHR) integration and sharing. EHR is defined as a repository of patient data in digital form. This record is stored and exchanged securely and accessible by different levels of authorized users. Its key purpose is to support the continuity of care, and allow the exchange and integration of medical information for a patient. However, this would not be achieved without ensuring the quality of data populated in the healthcare clouds as the data quality can have a great impact on the overall effectiveness of any system. The assurance of the quality of data used in healthcare systems is a pressing need to help the continuity and quality of care. Identification of data quality dimensions in healthcare clouds is a challenging issue as data quality of cloud-based health information systems arise some issues such as the appropriateness of use, and provenance. Some research proposed frameworks of the data quality dimensions without taking into consideration the nature of cloud-based healthcare systems. In this paper, we proposed an initial framework that fits the data quality attributes. This framework reflects the main elements of the cloud-based healthcare systems and the functionality of EHR

Evaluation metrics for VM allocation mechanisms in desktop clouds

Desktop Cloud computing is the idea of benefiting from computing resources around us to build a Cloud system in order to have better usage of these resources instead of them being idle. However, such resources are prone to failure at any given time without prior knowledge. Such failure events have a can negative impact on the outcome of a Desktop Cloud system. This paper proposes metrics that can evaluate the behaviour of Virtual Machine (VM) allocation mechanisms in the presence of node failures. The metrics are throughput, power consumption and availability. Three VM allocation mechanisms (Greedy, FCFS and RoundRobin mechanisms) are evaluated using the given metrics

A fault-tolerant mechanism for desktop cloud systems

Cloud computing is a paradigm that promises to move IT another step towards the age of computing utility. Traditionally, Clouds employ dedicated resources located in data centres to provide services to clients. The resources in such Cloud systems are known to be highly reliable with a low probability of failure. Desktop Cloud computing is a new type of Cloud computing that aims to provide Cloud services at little or no cost. This ambition can be achieved by combining Cloud computing and Volunteer computing into Desktop Clouds, harnessing non-dedicated resources when idle.The resources can be any type of computing machine, for example a standard PC, but such computing resources are renowned for their volatility; failures can happen at any time without warning. In Cloud computing, tasks are submitted by Cloud users or brokers to be processed and executed by virtual machines (VMs), and virtual mechanisms are hosted by physical machines (PMs). In this context, throughput is defined as the proportion of the total number of tasks that are successfully processed, so the failure of a PM can have a negative impact on this measure of a Desktop Cloud system by causing the destruction of all hosted VMs, leading to the loss of submitted tasks currently being processed. The aim of this research is to design a VM allocation mechanism for Desktop Cloud systems that is tolerant to node failure. VM allocation mechanisms are responsible for allocating VMs to PMs and migrating them during runtime with the objective of optimisation, yet those available pay little attention to node failure events.The contribution of this research is to propose a Fault-Tolerant VM allocation mechanism that handles failure events in PMs in Desktop Clouds to ensure that the throughput of Desktop Cloud system remains within acceptable levels by employing a replication technique. Since doing so causes an increase of power consumption in PMs, the mechanism is enhanced with a migration policy to minimise this effect, evaluated using three metrics: throughput of tasks; power consumption of PMs; and service availability. The evaluation is conducted using DesktopCloudSim, a tool developed for the purpose by this study as an extension to CloudSim, the well-known Cloud simulation tool, to simulate node failure events in Cloud systems, analysing node failure with real data sets of collected from Failure Trace Archives. The experiments demonstrate that the FT mechanism improves the throughput of Cloud systems statistically significantly compared with traditional mechanisms (First Come First Serve, Greedy and RoundRobin) in the presence of node failures. The FT mechanism reduces power consumption statistically significantly when its migration policy is employed.<br/

Evaluation of node failures in cloud computing using empirical data

Cloud has emerged as a new computing paradigm that promises to move into computing-as-utility era. Desktop Cloud is a new type of Cloud computing introduced to further achieve this ambition with an aim to reduce costs. It merges two computing models: Cloud computing and volunteer computing. The aim of Desktop Cloud is to provide Cloud services out of infrastructure that is not made for this purpose, like PCs and laptops. Such computing resources lead to a high level of volatility as a result of the fact that they can leave without prior knowledge. This paper studies the impact of node failures using evaluation metrics based on real data collected from public archive to simulate failure events in the infrastructure of a Desktop Cloud. The contribution of this paper is: (i) analysing the failure events, (ii) proposing metrics to evaluate Desktop Clouds, and (iii) evaluating several VM allocation mechanisms in the presence of node failure

The Impact of COVID-19 Pandemic on Mental Health among Individuals infected with Corona virus, in Qassim Region, Saudi Arabia, October-December 2020

Introduction: The COVID-19 pandemic symbolizes many overwhelming stresses. Such as loss of family members, friends, or colleagues, financial uncertainty; and isolation from others, mostly in those who live alone. That's why the physician must sort out demoralization from depression. Aim: This study aimed to determine the impact of COVID-19 pandemic on mental health among individuals infected with Corona virus, in Qassim region, Saudi Arabia. Materials and Methods&nbsp;: This is a cross-sectional study conducted among individuals infected with Corona virus in Qassim region. A pre-designed questionnaire was distributed among all the individuals infected with COVID-19 using available communication methods provided by the COVID-19 committee. A total response of 800, when applying the inclusion and exclusion criteria, only 580 participants have met the criteria. The questionnaire contains socio-demographic characteristics of the participants, anxiety, panic disorder and depression. The collection of data was during the period from October to December of 2020. All statistical data were calculated using SPSS version 21. Results: A total of 580 COVID-19 patients were enrolled. The most common age group was 20 – 29 with 52.6% were males. The prevalence of patients with anxiety was 14.0% while the prevalence of depression 20.7% and those with panic disorder was 43.1%. In multivariate regression model. Those who developed symptoms at the time of diagnosis were the independent factors associated with both anxiety and depression while family income negatively affected by the pandemic was the independent factor associated with anxiety, depression and panic disorder. Conclusion: Impact of COVID on mental health was high. Panic disorder in people infected with COVID-19 found to be higher than anxiety and depression, lastly panic disorder provided greater negative effect with their mental health. These results require more investigation and further research, in addition patient who was infected with COVID-19 should be reviewed with the psychiatrist. &nbsp

A resource allocation model for desktop clouds

Cloud computing is a new paradigm that promises to move IT a step further towards utility computing, in which computing services are delivered as a utility service. Traditionally, Cloud employs dedicated resources located in one or more data centres in order to provide services to clients. Desktop Cloud computing is a new type of Cloud computing that aims at providing Cloud capabilities at low or no cost. Desktop Clouds harness non dedicated and idle resources in order to provide Cloud services. However, the nature of such resources can be problematic because they are prone to failure at any time without prior notice. This research focuses on the resource allocation mechanism in Desktop Clouds.The contributions of this chapter are threefold. Firstly, it defines and explains Desktop Clouds by comparing them with both Traditional Clouds and Desktop Grids. Secondly, the paper discusses various research issues in Desktop Clouds. Thirdly, it proposes a resource allocation model that is able to handle node failures