Performance Analysis of Personal Cloud Storage Services for Mobile Multimedia Health Record Management
Author(s) -
Mahmuda Akter,
Abdullah Gani,
Md. Obaidur Rahman,
Mohammad Mehedi Hassan,
Ahmad Almogren,
Shafiq Ahmad
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2869848
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Recently, the trend of mobile multimedia services and applications being used for e-health is growing in popularity. This is because people can get access to their electronic personal health records (PHRs), such as medical history, lab reports from an X-ray, MRI, clinical audio-visual notes, EEG/ECG data, and insurance policy details from anywhere, at any time, from their mobile or handheld devices. In this scenario, a medical care provider or a patient is responsible for uploading and managing the patient's health information via cloud storage services. There are a number of personal cloud storage services that could be used such as Dropbox, Google Drive, OneDrive, and Box. However, the different designs of these personal cloud storage services mean there are differences in their performance in terms of storing and managing PHRs. In this paper, we present the details of our study on the performance of personal cloud storage services, and we highlight the strengths and weaknesses of such services in terms of PHR management. We investigate the performance of personal cloud storage services by conducting a qualitative and quantitative analysis of them. The qualitative analysis highlights strengths and weaknesses in terms of supported capabilities/features and shortcomings in terms of potential features that have not been implemented. The capabilities we analyze are chunking, bundling, de-duplication, delta-encoding, and data compression. In the quantitative analysis, we investigate performance in terms of control data overhead, impact of data size on number of packets as well as transmission rate, synchronization initialization time, and protocol overhead. During testing with diverse benchmark size on distinct cloud storage services, we attained an average transmission of 93%, 3%, and 4% for application data, control data, and other data, respectively. This research allows us to identify open issues and to determine future directions for developing an efficient personal cloud storage service.
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