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An efficient cloud storage system for tele-health services

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Abstract

Healthcare service is a critical aspect of our daily lives. Enabled by technologies such as wearable devices and wireless sensor networks, tele-health has becoming a promising new field in IT industry. Wearable devices, which detect real-time human body conditions, form body sensor networks (BSNs) for patients. In a cloud-enabled tele-health ecosystem, health data are collected by the BSN and sent to mobile devices such as smart phones and tablets. These embedded devices process the data and forward them to remote data centers. Due to the energy and time constraints of embedded systems, the effectiveness of storage systems become a critical issue. For years, memory technologies such as SRAMs and DRAMs have been widely used in computer systems. SRAMs are fast while DRAMs have high density. However, SRAMs have the disadvantage of power leakage and low density. DRAMs are slower in read and write operations. New memory technology for embedded tele-health is needed. In the paper, we propose a hybrid memory system for embedded tele-health. We combine phase-change memory PCM with flash memory to meet energy and latency requirement while reducing capital expenditure. Moreover, the data allocation and storage on server side is also a challenging problem in tele-health. Effective storage system designs are desired to efficiently store and manage health care data from users. Therefore, in the paper, we design a ecosystem for tele-health including the memory storage for embedded devices and data storage for tele-health data centers. To fully utilize the proposed ecosystem, we design several resource allocation algorithms with dynamic programming and heuristics. The experiments show that our approaches can achieve up to 30% performance enhancement compared to greedy approaches.

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Acknowledgements

This work has been partially supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China ICT1600236 (Prof. Meikang Qiu).

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Authors and Affiliations

  1. Pace University, New York, NY, USA

    Longbin Chen, Meikang Qiu & Wenyun Dai

  2. Universitat Politecnica de Valencia, Valencia, Spain

    Houcine Hassan

Authors
  1. Longbin Chen
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  2. Meikang Qiu
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  3. Wenyun Dai
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  4. Houcine Hassan
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Correspondence to Longbin Chen.

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Chen, L., Qiu, M., Dai, W. et al. An efficient cloud storage system for tele-health services. J Supercomput 73, 2949–2965 (2017). https://doi.org/10.1007/s11227-017-1977-y

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  • DOI: https://doi.org/10.1007/s11227-017-1977-y

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