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Simulation Study of Low-Latency Network Model with Orchestrator in MEC
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Summary :
Most of latency-sensitive mobile applications depend on computational resources provided by a cloud computing service. The problem of relying on cloud computing is that, sometimes, the physical locations of cloud servers are distant from mobile users and the communication latency is long. As a result, the concept of distributed cloud service, called mobile edge computing (MEC), is being introduced in the 5G network. However, MEC can reduce only the communication latency. The computing latency in MEC must also be considered to satisfy the required total latency of services. In this research, we study the impact of both latencies in MEC architecture with regard to latency-sensitive services. We also consider a centralized model, in which we use a controller to manage flows between users and mobile edge resources to analyze MEC in a practical architecture. Simulations show that the interval and controller latency trigger some blocking and error in the system. However, the permissive system which relaxes latency constraints and chooses an edge server by the lowest total latency can improve the system performance impressively.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E102-B No.11 pp.2139-2150
- Publication Date
- 2019/11/01
- Publicized
- 2019/05/16
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2018EBP3368
- Type of Manuscript
- PAPER
- Category
- Network
Authors
Krittin INTHARAWIJITR
Tokyo Institute of Technology
Katsuyoshi IIDA
Hokkaido University
Hiroyuki KOGA
University of Kitakyushu
Katsunori YAMAOKA
Tokyo Institute of Technology
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Krittin INTHARAWIJITR, Katsuyoshi IIDA, Hiroyuki KOGA, Katsunori YAMAOKA, "Simulation Study of Low-Latency Network Model with Orchestrator in MEC" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 11, pp. 2139-2150, November 2019, doi: 10.1587/transcom.2018EBP3368.
Abstract: Most of latency-sensitive mobile applications depend on computational resources provided by a cloud computing service. The problem of relying on cloud computing is that, sometimes, the physical locations of cloud servers are distant from mobile users and the communication latency is long. As a result, the concept of distributed cloud service, called mobile edge computing (MEC), is being introduced in the 5G network. However, MEC can reduce only the communication latency. The computing latency in MEC must also be considered to satisfy the required total latency of services. In this research, we study the impact of both latencies in MEC architecture with regard to latency-sensitive services. We also consider a centralized model, in which we use a controller to manage flows between users and mobile edge resources to analyze MEC in a practical architecture. Simulations show that the interval and controller latency trigger some blocking and error in the system. However, the permissive system which relaxes latency constraints and chooses an edge server by the lowest total latency can improve the system performance impressively.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3368/_p
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@ARTICLE{e102-b_11_2139,
author={Krittin INTHARAWIJITR, Katsuyoshi IIDA, Hiroyuki KOGA, Katsunori YAMAOKA, },
journal={IEICE TRANSACTIONS on Communications},
title={Simulation Study of Low-Latency Network Model with Orchestrator in MEC},
year={2019},
volume={E102-B},
number={11},
pages={2139-2150},
abstract={Most of latency-sensitive mobile applications depend on computational resources provided by a cloud computing service. The problem of relying on cloud computing is that, sometimes, the physical locations of cloud servers are distant from mobile users and the communication latency is long. As a result, the concept of distributed cloud service, called mobile edge computing (MEC), is being introduced in the 5G network. However, MEC can reduce only the communication latency. The computing latency in MEC must also be considered to satisfy the required total latency of services. In this research, we study the impact of both latencies in MEC architecture with regard to latency-sensitive services. We also consider a centralized model, in which we use a controller to manage flows between users and mobile edge resources to analyze MEC in a practical architecture. Simulations show that the interval and controller latency trigger some blocking and error in the system. However, the permissive system which relaxes latency constraints and chooses an edge server by the lowest total latency can improve the system performance impressively.},
keywords={},
doi={10.1587/transcom.2018EBP3368},
ISSN={1745-1345},
month={November},}
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TY - JOUR
TI - Simulation Study of Low-Latency Network Model with Orchestrator in MEC
T2 - IEICE TRANSACTIONS on Communications
SP - 2139
EP - 2150
AU - Krittin INTHARAWIJITR
AU - Katsuyoshi IIDA
AU - Hiroyuki KOGA
AU - Katsunori YAMAOKA
PY - 2019
DO - 10.1587/transcom.2018EBP3368
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2019
AB - Most of latency-sensitive mobile applications depend on computational resources provided by a cloud computing service. The problem of relying on cloud computing is that, sometimes, the physical locations of cloud servers are distant from mobile users and the communication latency is long. As a result, the concept of distributed cloud service, called mobile edge computing (MEC), is being introduced in the 5G network. However, MEC can reduce only the communication latency. The computing latency in MEC must also be considered to satisfy the required total latency of services. In this research, we study the impact of both latencies in MEC architecture with regard to latency-sensitive services. We also consider a centralized model, in which we use a controller to manage flows between users and mobile edge resources to analyze MEC in a practical architecture. Simulations show that the interval and controller latency trigger some blocking and error in the system. However, the permissive system which relaxes latency constraints and chooses an edge server by the lowest total latency can improve the system performance impressively.
ER -
English
