This case aims to improve the network efficiency and QoE, e.g., video stalling
485
time, voice quality, etc., for each higher-layer application (e.g., YouTube, Skype) individually. For instance, operators employ deep packet inspection (DPI) to clas-sify each traffic flow and form the switch flow table rules (e.g., queue prioritization) in order to increase QoE in any IP-based operator network. So, the Application type and Application control protocol arguments are necessary to be identified.
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Further, two arguments are further identified: Requested RAN information
argu-Application-aware Performance Optimization
① Application type (VoIP, Video conference)
② Application protocol (SIP, HTTP, SMTP, ...)
① Target bearer
② Target KPI (E-RAB
KPI Evaluation and Traffic profiling
⑥ Target user
Network element Network element ID Configuration eNB list Cell ID, TAI, ECGI Geo-region, eNB capability, Connected MME/GW
UE list C-RNTI, GUTI, S-TMSI Connected eNB, UE category, UE capability
① Target bearer
② Applied policy & config.
(QCI, ARP, MBR, GBR)
③ Network element
④ Target user Network status and
configuration
Parameter
QCI Bearer Packet Delay Packet Loss
1 GBR 100ms 10^-2
2 GBR 150ms 10^-3
3 GBR 50ms 10^-3
4 GBR 300ms 10^-6
5 Non-GBR 100ms 10^-6
6 Non-GBR 300ms 10^-6
7 Non-GBR 100ms 10^-3
8 Non-GBR 300ms 10^-6
9 Non-GBBR 300ms 10^-6
Priority ARP Pre-emption
Capability Pre-emption Vulunability
High 1~H Yes No
Medium H+1~M No Yes
Low M+1~15 No Yes
QCI mapping table
ARP mapping table QoS parameter database
Application type of target UE Application protocol of target UE Bearer ID
VoIP SIP 7
Application database
③ Application quality (GBR,
MBR, Loss Rate, Priority) ⑧ Target user
⑥ Operate period
⑦ Geo-region
④ Requested RAN info (TNL, Throughput, ...)
Figure 9: Application-aware Performance Optimization application ment and Application quality argument. The former argument is used to request RAN information by the MEC application and the latter one is for allocating new QoS policy on the application. The arguments of this case are given as:
Arguments:Application type, Application control protocol, Application
qual-495
ity, Requested RAN information (Throughput, Delay jitter, etc.), Update frequency, Operate period, Concerned geo-region, Target user
In Fig. 9, three MEC services (Analytics, KPI Evaluation and Traffic profiling, Network status and configuration) and two common services (RNIS, EPS) are in-volved. The RNIS and EPS take the input from the support services and output the
500
parameters to the MEC services after loop-up in their internal DB. Afterwards, the Analytics service and the KPI Evaluation and Traffic profiling service provide the control-plane and the data-plane value-added RAN information that is sent back to the MEC application. Finally, the Network status and configuration service enact the configuration and policy on the target bearer.
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7 Conclusion
This paper presents a modular MEC server architecture for LTE/LTE-A that provides a rich network application development environment. It is composed of three main components, (1) MEC RAN abstraction interface in charge of establish-ing communication channels with the underlyestablish-ing networks, (2) MEC application
510
development framework provides services and high-level APIs for application de-velopment, and (3) MEC application offering value-added network service. Then, the proposed architecture is used to analyze the ETSI MEC use-cases, and de-rive the required network information and the mapping of the MEC services. The analysis reveals the interplay between MEC, RAN and cloud to enable a
slica-515
ble network for the future 5G system. Going forward, we plan to build a MEC proof-of-concept for RAN-aware video optimization and low-latency service for IoT leveraging OpenAirInterface software implementation of LTE/LTE-A system.
Acknowledgment
Research and development leading to these results has received funding from
520
the European Framework Program under H2020 grant agreement 671639 for the COHERENT project.
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