A Network Function Parallelism-Enabled MEC Framework for Supporting Low-Latency Services

Mobile edge computing (MEC) enables users to offload computing tasks to edge servers for provisioning low-latency and computation-intensive services. To manage heterogeneous resources and improve service flexibility, MEC is entailed by new technologies, i.e., software defined networking (SDN) and ne...

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Vydané v:IEEE transactions on services computing Ročník 16; číslo 1; s. 40 - 52
Hlavní autori: Liu, Mengjie, Feng, Gang, Sun, Yao, Chen, Nan, Tan, Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Bending machines
Commodities
Computation offloading
Data processing
Edge computing
Hardware
Low latency communication
low-latency
Maximization
MEC
Mobile computing
network function parallelism
Network latency
Network slicing
New technology
NFP-enabled MEC framework
Noise measurement
Optimization
Parallel processing
Provisioning
Quality of service
Resource management
Resource utilization
Servers
Software
Software-defined networking
Throughput
ISSN:1939-1374, 2372-0204
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Popis
Shrnutí:Mobile edge computing (MEC) enables users to offload computing tasks to edge servers for provisioning low-latency and computation-intensive services. To manage heterogeneous resources and improve service flexibility, MEC is entailed by new technologies, i.e., software defined networking (SDN) and network function virtualization (NFV), which allow services running on common commodity hardware instead of proprietary hardware. However, data processing via software on commodity servers may induce high latency due to limited processing capacity, which impedes the quality of service. Meanwhile, MEC is a resource-sharing system and thus fairness should be considered. In this paper, we propose a network function parallelism (NFP)-enabled MEC (NFPMec) framework for supporting low-latency services. To reap the potential benefits of the NFPMec, we formulate the fairness-aware throughput maximization problem (FTMP) with aim of maximizing the fairness-aware system throughput while satisfying the QoS requirements. We propose a relaxation-based generalized benders algorithm (RGBA) to decouple the FTMP into two sub-problems based on the non-linear convex duality theory. After relaxation, the sub-problems are solved by the Karush-Kuhn-Tucker (KKT) approach. The convergence of the RGBA is theoretically proved. The simulation results demonstrate that the proposed NFPMec outperforms SDN-enabled MEC networks in terms of resource utilization, service latency and system throughput.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1939-1374
2372-0204
DOI:10.1109/TSC.2021.3130247