Smart Homecare System for Health Tele-monitoring
ICDS '07 Proceedings of the First International Conference on the Digital Society
Low-power, intelligent sensor hardware interface for medical data preprocessing
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Evaluation Framework for Personal Health Records: Microsoft HealthVault Vs. Google Health
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Online optimization for scheduling preemptable tasks on IaaS cloud systems
Journal of Parallel and Distributed Computing
Virtualised e-Learning on the IRMOS real-time Cloud
Service Oriented Computing and Applications
IEEE 802.15.4: a developing standard for low-power low-cost wireless personal area networks
IEEE Network: The Magazine of Global Internetworking
An efficient test design for CMPs cache coherence realizing MESI protocol
VDAT'12 Proceedings of the 16th international conference on Progress in VLSI Design and Test
ICPP '12 Proceedings of the 2012 41st International Conference on Parallel Processing
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
Exploring Inter-Cloud Load Balancing by Utilizing Historical Service Submission Records
International Journal of Distributed Systems and Technologies
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The use of cloud computing for the better health care is more and more important. Patient's real-time physiological signals, such as electrocardiogram (ECG) and blood pressure, should be transmitted to hospital servers for remote monitoring, and stored in Data Centers (DCs) so that the authorized doctors are able to access the patient's disease history. This implies challenges in brokering between the cloud consumers and providers when a huge number of people gets the real-time services from the distributed medical organizations. This paper proposes a probability-based bandwidth model in a telehealth cloud system, which helps cloud broker to allocate the most efficient computing nodes and links. This brokering mechanism considers the location of Personal Health Record (PHR) in cloud and schedules the real- time signal with a low information transfer between different hosts. Furthermore, our broker uses a bandwidth evaluation for the model, and we also compare various predicting methods to obtain the best bandwidth allocating algorithm. We simulate an inter-host environment for measuring the performance of our bandwidth allocating method with various data coherence protocols, which controls the domain of PHR in cloud, and the results show that our model is effective at determining the best performing service, and the inserted service validates the utility of our approach.