摘要 :
This article defines and describes the numerous types of “clients” for picture archiving and communication systems (PACS). A radiologist uses a client to view images stored in the system. Many PACS are available in the market, a...
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This article defines and describes the numerous types of “clients” for picture archiving and communication systems (PACS). A radiologist uses a client to view images stored in the system. Many PACS are available in the market, and each offers different methods by which a client can view images from the server. The terminology used to describe these different methods can cause confusion and lead to poor choice for those imaging team members who are given the task of purchasing, implementing, and supporting the PACS. We propose a classification of clients with respect to their impact on client work stations, an effect often referred to as the application’s thickness. The thinner the client, the less effect it has on the hosting work station. In contrast, a thick client consumes the work station’s resources and often prevents a work station from being used to effectively run anything other than the client application. Functionality and supportability are highlighted as key and interacting metrics in determining optimal correct PACS solutions. The importance of a clear understanding of the needs and requirements of all users as well as the client application is emphasized. This relationship between supportability and functionality becomes increasingly important as the industry shifts to enterprise information technology solutions.
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Nowadays, the emergence of computation-intensive applications brings henefits to individuals and the commercial organization. However, it stillfaces many challenges due to the limited processing capacity of the local computing res...
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Nowadays, the emergence of computation-intensive applications brings henefits to individuals and the commercial organization. However, it stillfaces many challenges due to the limited processing capacity of the local computing resources. Besides, the local computing resources require a lot offinance and human forces. This problem, fortunately, has been made less severe, thanks to the recent adoption of Cloud Computing (CC) platform. CC enables offloading heavy processing tasks up to the "cloud", leaving only simplejobs to the user-end capacity-limited clients. Conversely, as CC is a pay-as-you-go model, it is necessary to find out an approach that guarantees the highly efficient execution time of cloud systems as well as the monetary cost for cloud resource use. Heretofore, a lot of research studies have been carried out, trying to eradicate problems, but they have still proved to be trivial. In this paper, wepresent a novel architecture, which is a collaboration of the computing resources on cloud provider side and the local computing resources (thick clients) on client side. In addition, the main factor of this framework is the dynamic genetic task scheduling to globally minimize the completion time in cloud service, while taking into account network condition and cloud cost paid by customers. Our simulation and comparison with other scheduling approaches show that the proposal produces a reasonable performance together with a noteworthy cost saving for cloud customers.
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Cloud computing (CC) has recently become a rising paradigm in the information and communications technology industry, drawing a lot of attentions to professionals and researchers. During the last decade, the dominance of smart pho...
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Cloud computing (CC) has recently become a rising paradigm in the information and communications technology industry, drawing a lot of attentions to professionals and researchers. During the last decade, the dominance of smart phones or tablet computers (known as thin clients) over traditional desktop or laptop computers (known as thick clients) has become more and more evident, reflecting a great change in the way people access the Internet. Despite the recent technology advancements that manufacture a new generation of mobile devices with generous resources, the fact that they can offer only limited processing capacity still remains a painful experience. This problem, fortunately, has been made less severe thanks to the recent adoption of CC platform. CC enables offloading heavy processing tasks up to the "cloud", leaving only simple jobs to the user-end capacity-limited thin clients. So far, a number of research studies have been carried out, trying to eliminate problems arising from shortcomings in the connection between thin clients and cloud networks, yet little have been found efficient. In this paper, we present a novel architecture, taking advantage of collaboration of thin and thick clients, particularly aiming at optimizing data distribution and utilizing CC resources so that expected Quality-of-Service requirements can be met. We also propose an algorithm to select an optimal resource allocation strategy to satisfy various Service Level Agreements. In order to justify our proposal, we have used both numerical analysis and programming approaches. Simulation result shows that our proposed schemes can improve resource allocation efficiency and achieve better performance than the existing ones.
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The emergence of mobile cloud computing (MCC) brings benefits to mobile users and cloud providers. However, due to the inherent limitations of the device such as battery life time, CPU and memory capacity, a mobile thin client dev...
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The emergence of mobile cloud computing (MCC) brings benefits to mobile users and cloud providers. However, due to the inherent limitations of the device such as battery life time, CPU and memory capacity, a mobile thin client device (e.g. smart phones, tablets, iWatch, Google Glass, etc) cannot meet the requirements of some demanding applications. To alleviate this limitation, the mobile device should cooperate with external resources to increase its performance. Recently, current research approaches have been unable to offer an efficient, seamless computing experience. In this paper, we present a comprehensive thin-thick client collaboration that involves conventional desktop or laptop computers, known as thick clients, by allowing the thin client to borrow resources from thick clients, particularly for optimizing data distribution and utilizing MCC resources to meet Service-Level Agreements, Quality-of-Service requirements and cloud service customers' budget. Our work uses both numerical analysis and simulation to prove that our proposed architecture can improve resource allocation efficiency and achieve better performance than other existing approaches in some cases.
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We describe an approach to quantifying the impact of network latency on interactive response and show that the adequacy of thin-client computing is highly variable and depend on both the application and available network quality. ...
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We describe an approach to quantifying the impact of network latency on interactive response and show that the adequacy of thin-client computing is highly variable and depend on both the application and available network quality. If near ideal network conditions (low latency and high bandwidth) can be guaranteed, thin clients offer a good computing experience. As network quality degrades, interactive performance suffers. It is latency - not bandwidth -that is the greater challenge. Tightly coupled tasks such as graphics editing suffer more than loosely coupled tasks such as Web browsing. The combination of worst anticipated network quality and most tightly coupled tasks determine whether a thin-client approach is satisfactory for an organization.
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