Low-latency service function chain migration in edge-core networks based on open Jackson networks

Liang, Wenjie; Cui, Lin; Tso, Fung Po

doi:10.1016/j.sysarc.2022.102405

Cited by 15 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, we consider that the service transmission between nodes is performed through the core network [34]. The transmission rate between core networks is denoted by Ω , for a service duplicate of size α i , the migration delay can be expressed as:…”

Section: Service Migration Processmentioning

confidence: 99%

Service migration in MEC: an Approach Based on Deep Reinforcement Learning and Fuzzy Logic

Zhang,

Bu,

Wang

2023

Preprint

View full text Add to dashboard Cite

As a new computing paradigm, Mobile Edge Computing (MEC) sinks resources to the network edge to support real-time responses of services. Facing the demand for migrating services caused by the frequent movement of the mobile device, it is a great challenge to reduce the operation costs of migrating services with user requirements satisfied, especially for the continuous optimization of services deployed in hot spot areas. In order to address the above challenge, we propose the mechanism of dynamically migrating and optimally deploying services, which optimizes the latency and energy consumption of the service migration. Firstly, we introduce Markov Decision Process (MDP) to model changes of deployment locations of the single-user service, and devise the approach to determine whether to migrate a service at the current network status based on the dueling Deep Q-Network (DQN). Secondly, we propose the service pre-migration decision approach to determine where to migrate a service by combining the Long Short-Term Memory (LSTM) network and the attention mechanism, so as to determine the future moving area of the user and perform the migration decision in advance with the impact of migration service on service quality considered. Finally, we devise the distance-weighted matching algorithm based on the fuzzy logic to further optimize the selection of the most appropriate services to migrate in hotspot areas and the corresponding service migration regions, thus, the load pressure of the edge computing node caused by the imbalanced deployment density of service among multiple regions. Simulation results show that the proposed mechanism has significant improvements in overall operating costs compared with the cur-rent state of the art.

show abstract

Section: Service Migration Processmentioning

confidence: 99%

Service migration in MEC: an Approach Based on Deep Reinforcement Learning and Fuzzy Logic

Zhang,

Bu,

Wang

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The DRL-based strategy handles as many service requests as feasible using the set of available resource-constrained auxiliary servers. Liang et al (2022) proposed the PHS and AUB algorithm in edge-Open Jackson Networks to minimize general SFC deployment delay in open Jackson-based edge-core networks. It is not considered for minimizing the network overhead and fault tolerance.…”

Section: Literature Reviewmentioning

confidence: 99%

Service Chain Placement by Using an African Vulture Optimization Algorithm Based VNF in Cloud-Edge Computing

Pandey,

Singh

2023

ADCAIJ

View full text Add to dashboard Cite

The use of virtual network functions (VNFs) enables the implementation of service function chains (SFCs), which is an innovative approach for delivering network services. The deployment of service chains on the actual network infrastructure and the establishment of virtual connections between VNF instances are crucial factors that significantly impact the quality of network services provided. Current research on the allocation of vital VNFs and resource constraints on the edge network has overlooked the potential benefits of employing SFCs with instance reuse. This strategy offers significant improvements in resource utilization and reduced startup time. The proposed approach demonstrates superior performance compared to existing state-of-the-art methods in maintaining inbound service chain requests, even in complex network typologies observed in real-world scenarios. We propose a novel technique called African vulture optimization algorithm for virtual network functions (AVOAVNF), which optimizes the sequential arrangement of SFCs. Extensive simulations on edge networks evaluate the AVOAVNF methodology, considering metrics such as latency, energy consumption, throughput, resource cost, and execution time. The results indicate that the proposed method outperforms BGWO, DDRL, BIP, and MILP techniques, reducing energy consumption by 8.35%, 12.23%, 29.54%, and 52.29%, respectively.

show abstract

“…In [15], Virtual network function (VNF) chains placement problems are also formulated as a variant of bin-packing problem and request scheduling problems are modeled based on the key concepts from open Jackson network. In [16], the authors model the Latency-aware Edge-Core Service Function Chains (SFCs) Migration problems based on open Jackson 1 https://github.com/Free-Wei/SliceDimensioning.git network and have proposed an algorithms to optimize the average latency of all SFCs in edge-core networks. The proposed approach consist in selecting in the first step the appropriate SFCs, VNFs and target servers for migration.…”

Section: Related Workmentioning

confidence: 99%

Dimensioning resources of Network Slices for energy-performance trade-off

Huang

Araldo

Castel-Taleb

et al. 2022

2022 IEEE Symposium on Computers and Communications (ISCC)

View full text Add to dashboard Cite

Within network slicing, Virtual Network Embedding has been vastly studied, i.e., deciding in which physical nodes and links to place virtual functions and links. However, the performance of slices does not only depend on where virtual functions and links are placed, but also on how much resources they can use, which has been mostly neglected in the literature.We thus propose a method for optimal resource dimensioning, via dimensioning capacities of multiple Jackson networks (one per slice) co-existing in the same resource-constrained network. Despite the long history of Jackson networks, we are the first, to the best of our knowledge, to model such a problem. The objective is to minimize energy consumption while satisfying the latency requirements of heterogeneous service providers. We show numerically that our solution is able to achieve both goals, differently from classic approaches, which assume that the amount of resources assigned to slices is fixed a-priori.

show abstract

Low-latency service function chain migration in edge-core networks based on open Jackson networks

Cited by 15 publications

References 37 publications

Service migration in MEC: an Approach Based on Deep Reinforcement Learning and Fuzzy Logic

Service migration in MEC: an Approach Based on Deep Reinforcement Learning and Fuzzy Logic

Service Chain Placement by Using an African Vulture Optimization Algorithm Based VNF in Cloud-Edge Computing

Dimensioning resources of Network Slices for energy-performance trade-off

Contact Info

Product

Resources

About