Prioritizing Health Care Data Traffic in a Congested IoT Cloud Network

Beitelspacher, Sara; Mubashir, Mohammad; Besher, Kedir Mamo

doi:10.1109/wcncw48565.2020.9124867

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…As such, we expect this research to draw relatively generic conclusions with respect to the time efficiency of bufferaided relaying in edge-cloud data transmissions. Particularly, in our developed app, since Firebase [4] https://firebase.google.com/products/storage/ [5] https://cloud.google.com/storage/ Storage allows an upload task to include a single file only, we merge the generated individual reports into one PDF file when buffering them. Correspondingly, we predefine and adjust the buffer threshold by varying the number of reports to be merged.…”

Section: Two Transmission Mechanismsmentioning

confidence: 99%

“…Therefore, we assume that the role of remote hospital merely keeps the prediagnosis reports after receiving them. In this testbed, we employed Firebase Storage [4] to act as the report receiver. Considering that Firebase Storage is backed by Google Cloud Storage [5] , the transmitted data are eventually stored in the Google cloud, which essentially enables our testbed to reflect real edge-cloud application features.…”

Section: Testbed Setupmentioning

confidence: 99%

“…L bf (s) = 11.561s + 314.01 (4) where L bf (s) indicates the combinatory latency of buffering and uploading s prediagnosis reports, and the buffer size is also s.…”

Section: Modeling Analysis For Buffer-aided Transmissionmentioning

confidence: 99%

Section: ) Application-specific Workload Regimementioning

confidence: 99%

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Take one for the team: On the time efficiency of application-level buffer-aided data relaying in edge cloud communication

Millar-Bilbao

Rojas

et al. 2021

Preprint

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Background: Adding buffers to networks is part of the fundamental advance in data communication. Since edge cloud computing is based on the heterogeneous collaboration network model in a federated environment, it is natural to consider buffer-aided data communication for edge cloud applications. However, the existing studies generally pursue the beneficial features of buffering at a cost of time, not to mention that many investigations are focused on lower-layer data packets rather than application-level communication transactions.Aims: Driven by a counterintuitive argument against the claim that buffers "can introduce additional delay to the communication between the source and destination", this research aims to investigate whether or not (and if yes, to what extent) the application-level buffering mechanism can improve the time efficiency in edge-cloud data transmissions.Method: To collect empirical evidence for the theoretical discussion, we built up a testbed to simulate a remote health monitoring system, and conducted both experimental and modeling investigations into the first-in-first-served (FIFS) and buffer-aided data transmissions at a relay node in the system.Results: An empirical inequality system is established for revealing the time efficiency of buffer-aided edge cloud communication. For example, given the reference of transmitting the 11th data entity in the FIFS manner, the inequality system suggests buffering up to 50 data entities into one transmission transaction on our testbed.Conclusions: Despite the trade-off benefits (e.g., energy efficiency and fault tolerance) of buffering data, our investigation argues that the buffering mechanism can also speed up data transmission under certain circumstances, and thus it would be worth taking data buffering into account when designing and developing edge cloud applications even in the time-critical context.

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Section: Two Transmission Mechanismsmentioning

confidence: 99%

Section: Testbed Setupmentioning

confidence: 99%

“…L bf (s) = 11.561s + 314.01 (4) where L bf (s) indicates the combinatory latency of buffering and uploading s prediagnosis reports, and the buffer size is also s.…”

Section: Modeling Analysis For Buffer-aided Transmissionmentioning

confidence: 99%

Section: ) Application-specific Workload Regimementioning

confidence: 99%

See 2 more Smart Citations

Take one for the team: On the time efficiency of application-level buffer-aided data relaying in edge cloud communication

Millar-Bilbao

Rojas

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Despite such a limit, we argue the importance of identifying the practical characteristics of edge-cloud data transmission at runtime, because the data transmission characteristics can vary considerably over both different workload regimes and different environmental conditions. For example, by intuitively assuming the network congestion due to big data volumes, some researchers propose to prioritize the health data traffic, which however would incur unnecessary processing delay and result in unwanted unfairness among patients in the context of our project [45][46][47]. Furthermore, considering the usually heterogeneous capacity of upload bandwidth and uneven request rates from different edge nodes [48], we also use this limit to argue the necessity of replicating the characterization implementation rather than simply reusing the previous characterization results in different situations.…”

Section: ) Application-specific Workload Regimementioning

confidence: 99%

Take one for the team: on the time efficiency of application-level buffer-aided relaying in edge cloud communication

Millar-Bilbao

Rojas

et al. 2021

J Cloud Comp

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Background Adding buffers to networks is part of the fundamental advance in data communication. Since edge cloud computing is based on the heterogeneous collaboration network model in a federated environment, it is natural to consider buffer-aided data communication for edge cloud applications. However, the existing studies generally pursue the beneficial features of buffering at a cost of time, not to mention that many investigations are focused on lower-layer data packets rather than application-level communication transactions. Aims Driven by our argument against the claim that buffers “can introduce additional delay to the communication between the source and destination”, this research aims to investigate whether or not (and if yes, to what extent) the application-level buffering mechanism can improve the time efficiency in edge-cloud data transmissions. Method To collect empirical evidence for the theoretical discussion, we built up a testbed to simulate a remote health monitoring system, and conducted both experimental and modeling investigations into the first-in-first-served (FIFS) and buffer-aided data transmissions at a relay node in the system. Results An empirical inequality system is established for revealing the time efficiency of buffer-aided edge cloud communication. For example, given the reference of transmitting the 11th data entity in the FIFS manner, the inequality system suggests buffering up to 50 data entities into one transmission transaction on our testbed. Conclusions Despite the trade-off benefits (e.g., energy efficiency and fault tolerance) of buffering data, our investigation argues that the buffering mechanism can also speed up data transmission under certain circumstances, and thus it would be worth taking data buffering into account when designing and developing edge cloud applications even in the time-critical context.

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