On Data-driven Network Performance Modeling for Mobile Cloud Computing

Karin Hummel; Rene Gabner; Hans-Peter Schwefel

doi:10.1109/SPAWC.2018.8445844

On Data-driven Network Performance Modeling for Mobile Cloud Computing

Karin Hummel
, Rene Gabner
, Hans-Peter Schwefel

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Abstract

Computationally intensive mobile apps may be migrated
to a cloud infrastructure for faster remote execution.
Decreased execution time and lower energy consumption at
the mobile device are the expected benefits when offloading
the application to the cloud. The migration decision can be
taken based on a continuous-time Markov model that considers
network quality, cloud and mobile device capabilities, as well as
migration costs, as we have shown in previous work. One of the
influencing dynamic characteristics is the network performance.
In this work, we focus on characterizing network performance
under node mobility in terms of throughput and latency. Our final
goal is to derive a mobile performance model that goes beyond
an on-off network model. The analysis is based on performance
measurements taken on a train while commuting. By clustering
the measurement data, we derive a realistic network model.

Original language	English
Title of host publication	Proceedings of 19th IEEE International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)
Number of pages	5
Place of Publication	Kalamata, Greece
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Publication date	25 Jun 2018
Article number	8445844
ISBN (Print)	978-1-5386-3513-1
ISBN (Electronic)	978-1-5386-3512-4
DOIs	https://doi.org/10.1109/SPAWC.2018.8445844
Publication status	Published - 25 Jun 2018
Event	19th IEEE International Workshop on Signal Processing Advances in Wireless Communication (SPAWC) - Kalamata, Greece Duration: 25 Jun 2018 → 28 Jun 2018

Conference

Conference	19th IEEE International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)
Country/Territory	Greece
City	Kalamata
Period	25/06/2018 → 28/06/2018

Keywords

Mobile Cloud Computing
Performance Analysis
Wireless Network Performance Measurements

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10.1109/SPAWC.2018.8445844

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@inproceedings{c1ccb4c0efc74063a3694b68b942a908,

title = "On Data-driven Network Performance Modeling for Mobile Cloud Computing",

abstract = "Computationally intensive mobile apps may be migrated to a cloud infrastructure for faster remote execution. Decreased execution time and lower energy consumption at the mobile device are the expected benefits when offloading the application to the cloud. The migration decision can be taken based on a continuous-time Markov model that considers network quality, cloud and mobile device capabilities, as well as migration costs, as we have shown in previous work. One of the influencing dynamic characteristics is the network performance. In this work, we focus on characterizing network performance under node mobility in terms of throughput and latency. Our final goal is to derive a mobile performance model that goes beyond an on-off network model. The analysis is based on performance measurements taken on a train while commuting. By clustering the measurement data, we derive a realistic network model.",

keywords = "Mobile Cloud Computing, Performance Analysis, Wireless Network Performance Measurements",

author = "Karin Hummel and Rene Gabner and Hans-Peter Schwefel",

year = "2018",

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doi = "10.1109/SPAWC.2018.8445844",

language = "English",

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booktitle = "Proceedings of 19th IEEE International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)",

publisher = "IEEE (Institute of Electrical and Electronics Engineers)",

address = "United States",

note = " 19th IEEE International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), SPAWC19 ; Conference date: 25-06-2018 Through 28-06-2018",

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Hummel, K, Gabner, R & Schwefel, H-P 2018, On Data-driven Network Performance Modeling for Mobile Cloud Computing. in Proceedings of 19th IEEE International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)., 8445844, IEEE (Institute of Electrical and Electronics Engineers), Kalamata, Greece, 19th IEEE International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), Kalamata, Greece, 25/06/2018. https://doi.org/10.1109/SPAWC.2018.8445844

On Data-driven Network Performance Modeling for Mobile Cloud Computing. / Hummel, Karin; Gabner, Rene; Schwefel, Hans-Peter.
Proceedings of 19th IEEE International Workshop on Signal Processing Advances in Wireless Communication (SPAWC). Kalamata, Greece: IEEE (Institute of Electrical and Electronics Engineers), 2018. 8445844.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - On Data-driven Network Performance Modeling for Mobile Cloud Computing

AU - Hummel, Karin

AU - Gabner, Rene

AU - Schwefel, Hans-Peter

PY - 2018/6/25

Y1 - 2018/6/25

N2 - Computationally intensive mobile apps may be migrated to a cloud infrastructure for faster remote execution. Decreased execution time and lower energy consumption at the mobile device are the expected benefits when offloading the application to the cloud. The migration decision can be taken based on a continuous-time Markov model that considers network quality, cloud and mobile device capabilities, as well as migration costs, as we have shown in previous work. One of the influencing dynamic characteristics is the network performance. In this work, we focus on characterizing network performance under node mobility in terms of throughput and latency. Our final goal is to derive a mobile performance model that goes beyond an on-off network model. The analysis is based on performance measurements taken on a train while commuting. By clustering the measurement data, we derive a realistic network model.

AB - Computationally intensive mobile apps may be migrated to a cloud infrastructure for faster remote execution. Decreased execution time and lower energy consumption at the mobile device are the expected benefits when offloading the application to the cloud. The migration decision can be taken based on a continuous-time Markov model that considers network quality, cloud and mobile device capabilities, as well as migration costs, as we have shown in previous work. One of the influencing dynamic characteristics is the network performance. In this work, we focus on characterizing network performance under node mobility in terms of throughput and latency. Our final goal is to derive a mobile performance model that goes beyond an on-off network model. The analysis is based on performance measurements taken on a train while commuting. By clustering the measurement data, we derive a realistic network model.

KW - Mobile Cloud Computing

KW - Performance Analysis

KW - Wireless Network Performance Measurements

UR - https://www.scopus.com/pages/publications/85053464841

U2 - 10.1109/SPAWC.2018.8445844

DO - 10.1109/SPAWC.2018.8445844

M3 - Article in proceeding

SN - 978-1-5386-3513-1

BT - Proceedings of 19th IEEE International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

PB - IEEE (Institute of Electrical and Electronics Engineers)

CY - Kalamata, Greece

T2 - 19th IEEE International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

Y2 - 25 June 2018 through 28 June 2018

ER -

On Data-driven Network Performance Modeling for Mobile Cloud Computing

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Keywords

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