Low-latency cloud-fog network architecture and its load balancing strategy for medical big data

Yang, Jin

doi:10.1007/s12652-020-02245-y

Cited by 21 publications

(13 citation statements)

References 23 publications

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“…Also, reducing the complexity of the data can lead to low latency. Other solutions can be included such as load balancing technique as in [ 61 ]. Locality: in big data systems, the data are stored distributed in several physical locations.…”

Section: Challenges and Current Issues For The Proposed Business Inte...mentioning

confidence: 99%

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Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World

Darwiesh

Alghamdi

El-Baz

et al. 2022

Journal of Healthcare Engineering

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In this paper, we proposed an advanced business intelligence framework for firms in a post-pandemic phase to increase their performance and productivity. The proposed framework utilizes some of the most significant tools in this era, such as social media and big data analysis for business intelligence systems. In addition, we survey the most outstanding related papers to this study. Open challenges based on this framework are described as well, and a proposed methodology to minimize these challenges is given. Finally, the conclusion and further research points that are worth studying are discussed.

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Section: Challenges and Current Issues For The Proposed Business Inte...mentioning

confidence: 99%

“…Also, reducing the complexity of the data can lead to low latency. Other solutions can be included such as load balancing technique as in [ 61 ].…”

Section: Challenges and Current Issues For The Proposed Business Inte...mentioning

confidence: 99%

Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World

Darwiesh

Alghamdi

El-Baz

et al. 2022

Journal of Healthcare Engineering

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“…Since the 1990's, hardware machines have distributed the load over a network 16 . Many organizations, like healthcare, military, weather forecasting, businesses and so forth are becoming virtual and need improvement in terms of service quality characteristics such as low response time, fast processing, large bandwidth, and high security 17 . This is why organizations in this digital world have to become more focused on LB 18,19 .…”

Section: Background and Conceptsmentioning

confidence: 99%

“…Yang 17 suggested hybrid cloud‐fog network architecture with minimum‐latency that was used in medical big data, which tackles the business computation time in cloud computing platforms. The authors used the bat algorithm on the unconstrained optimization problem of local optimization and slow convergence and LB.…”

Section: Dynamic Lbmentioning

confidence: 99%

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

Kashyap

Kumar

2022

Concurrency and Computation

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SUMMARY Load balancing (LB) is nothing but the systematic distribution of load over different servers. The fog server is handling the maximum data of the cloud server to enhance the advancement of users' requests. The growth in data requests is escalating, and fog computing has intensified the accessibility of the data. Fog computing achieves many challenges according to the demands of the users, but even so, some challenges require more progress. The problem faced by fog computing is LB due to an increase in traffic on the network layer. Various LB techniques have already been proposed in the cloud layer, but until now they have only been in progress in the fog layer. Inefficient LB may cause a decrease in service quality, like delays in response time, processing time, security, and many more. In this survey, several algorithms have been discussed that are based on LB, which works out the issue of overloaded data on the network. Some parameters that authors have focused in LB are latency, bandwidth, deadlines, cost, security, execution time, and response time. Other parameters based on fault tolerance are also discussed with their quality parameter table and algorithm. In addition to this some of the limitations of the author's work, that is discussed in this article.

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“…Latency increases in transmitting results from the Cloud back to devices delays diagnosis for patients where quick decision making is essential for their health [8,9]. As medical data increases, communication overhead between the Cloud-IoT channel grows due to the frequent and large amount of medical data processing and data exchange in healthcare services [10]. Data analysis at the edge layer, which includes base stations, brings computation closer to the device layer, such as the IoMT reducing latency, bandwidth, and network delay to support real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Homomorphic Encryption Based Privacy-Preservation for IoMT

et al. 2021

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Healthcare applications store private user data on cloud servers and perform computation operations that support several patient diagnoses. Growing cyber-attacks on hospital systems result in user data being held at ransom. Furthermore, mathematical operations on data stored in the Cloud are exposed to untrusted external entities that sell private data for financial gain. In this paper, we propose a privacy-preserving scheme using homomorphic encryption to secure medical plaintext data from being accessed by attackers. Secret sharing distributes computations to several virtual nodes on the edge and masks all arithmetic operations, preventing untrusted cloud servers from learning the tasks performed on the encrypted patient data. Virtual edge nodes benefit from cloud computing resources to accomplish computing-intensive mathematical functions and reduce latency in device–edge node data transmission. A comparative analysis with existing studies demonstrates that homomorphically encrypted data stored at the edge preserves data privacy and integrity. Furthermore, secret sharing-based multi-node computation using virtual nodes ensures data confidentiality from untrusted cloud networks.

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Low-latency cloud-fog network architecture and its load balancing strategy for medical big data

Cited by 21 publications

References 23 publications

Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World

Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

Homomorphic Encryption Based Privacy-Preservation for IoMT

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