23,730 research outputs found
Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud
Electronic Health (e-Health) technology has brought the world with
significant transformation from traditional paper-based medical practice to
Information and Communication Technologies (ICT)-based systems for automatic
management (storage, processing, and archiving) of information. Traditionally
e-Health systems have been designed to operate within stovepipes on dedicated
networks, physical computers, and locally managed software platforms that make
it susceptible to many serious limitations including: 1) lack of on-demand
scalability during critical situations; 2) high administrative overheads and
costs; and 3) in-efficient resource utilization and energy consumption due to
lack of automation. In this paper, we present an approach to migrate the ICT
systems in the e-Health sector from traditional in-house Client/Server (C/S)
architecture to the virtualised cloud computing environment. To this end, we
developed two cloud-based e-Health applications (Medical Practice Management
System and Telemedicine Practice System) for demonstrating how cloud services
can be leveraged for developing and deploying such applications. The Windows
Azure cloud computing platform is selected as an example public cloud platform
for our study. We conducted several performance evaluation experiments to
understand the Quality Service (QoS) tradeoffs of our applications under
variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green
Computing (CGC 2013
Investigating Decision Support Techniques for Automating Cloud Service Selection
The compass of Cloud infrastructure services advances steadily leaving users
in the agony of choice. To be able to select the best mix of service offering
from an abundance of possibilities, users must consider complex dependencies
and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal
on investigating an intelligent decision support system for selecting Cloud
based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac
On the Load Balancing of Edge Computing Resources for On-Line Video Delivery
Online video broadcasting platforms are distributed, complex, cloud oriented, scalable, micro-service-based systems that are intended to provide over-the-top and live content to audience in scattered geographic locations. Due to the nature of cloud VM hosting costs, the subscribers are usually served under limited resources in order to minimize delivery budget. However, operations including transcoding require high-computational capacity and any disturbance in supplying requested demand might result in quality of experience (QoE) deterioration. For any online delivery deployment, understanding user's QoE plays a crucial role for rebalancing cloud resources. In this paper, a methodology for estimating QoE is provided for a scalable cloud-based online video platform. The model will provide an adeptness guideline regarding limited cloud resources and relate computational capacity, memory, transcoding and throughput capability, and finally latency competence of the cloud service to QoE. Scalability and efficiency of the system are optimized through reckoning sufficient number of VMs and containers to satisfy the user requests even on peak demand durations with minimum number of VMs. Both horizontal and vertical scaling strategies (including VM migration) are modeled to cover up availability and reliability of intermediate and edge content delivery network cache nodes
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