Service Function Migration Scheduling based on Encoder-Decoder Recurrent Neural Network

Hirayama, Takahiro; Miyazawa, Tatsuo; Jibiki, Masahiro; Kafle, Ved P.

doi:10.1109/netsoft.2019.8806669

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…The resource control system inds a solution for resource arbitration among CNFs or NF migration between physical servers by using the CPU utilization prediction results received from the advanced data analytics system, and/or other external information such as network and service requirements. Because the main contribution of this paper is an advanced data analytics system, we omit a detailed explanation of the structure and mechanisms of the resource control system and refer readers to [22]- [26].…”

Section: Resource Control Systemmentioning

confidence: 99%

“…The application of ML techniques to data analytics contributes to satisfying various levels of QoS requirements in 5G/6G network and cloud systems based on their capability to enhance computational resource control and management by autonomously suppressing resource shortage with high ef iciency of resource utilization, as discussed in [22]- [27]. Such enhancement can also be achieved for the resource control and management of microservices (such as CNFs) by exploiting the advantages of ML techniques, such as reducing the time required to ind an appropriate solution for computational resource adjustments compared to existing complex optimization approaches (as discussed in [26]), and enabling proactive resource control through training and prediction processes. In this paper, we irst introduce a framework of computational resource control and management for CNFs to provide microservice-based application services.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advanced data analytics using three-stage intelligent model pipelining for containerized microservices in 5G networks and beyond

Miyazawa¹,

Kafle²,

Yokota³

et al. 2023

ITU J-FET

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The recent rapid advancement of cloud-native networking infrastructure has leveraged the resource virtualization technology of containers to realize diverse microservice-based applications in 5G/6G networks and clouds. Containers drastically enhance the efficiency of computational resource allocation and utilization as compared to the related virtualization technology of Virtual Machines (VMs). The networking environment leveraging both VM and container mixed virtualization technologies makes the most use of them to realize a computational platform whose resources can be dynamically adjusted to a fine granularity. To continuously meet the required levels of quality of services in 5G/6G networks and clouds in that platform, an agile and autonomous data analytics system in the control plane is essential for the accurate prediction of server workloads and dynamic allocation of enough amount of computational resource. In this paper, we introduce a framework, which complies with Recommendation ITU-T Y.3177, for autonomous computational resource control and management. The framework consists of an advanced data analytics system and a resource control system. We propose an architecture for the advanced data analytics system consisting of learning and prediction components. The learning component includes a three-stage intelligent model pipelining with three cascaded machine learning models, nonlinear regression, clustering, and multiple regression. These models determine the fluctuation trends in CPU utilization, classify services with similarities in the trends, and predict the peak CPU utilization of each containerized microservice. We evaluate the proposed models through experiments and numerical analysis. The results prove that the models support agile data analytics, which can complete data processing in the time granularity of seconds and achieve higher prediction accuracy of CPU utilization.

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Section: Resource Control Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced data analytics using three-stage intelligent model pipelining for containerized microservices in 5G networks and beyond

Miyazawa¹,

Kafle²,

Yokota³

et al. 2023

ITU J-FET

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“…3) VNF Migration: The authors in [24] considered VNF migration scheduling problem based on an encoder-decoder recurrent neural network. In [25], the authors considered impact of user's mobility on VNF placement in a cellular network, aiming to minimize the cost of VNF migration.…”

Section: A Motivationmentioning

confidence: 99%

AI-Based and Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

Pourghasemian,

Abedi,

Salarhosseini

et al. 2021

Preprint

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In this paper, we propose a novel joint intelligent trajectory design and resource allocation algorithm based on user's mobility and their requested services for unmanned aerial vehicles (UAVs) assisted networks, where UAVs act as nodes of a network function virtualization (NFV) enabled network. Our objective is to maximize energy efficiency and minimize the average delay on all services by allocating the limited radio and NFV resources. In addition, due to the traffic conditions and mobility of users, we let some Virtual Network Functions (VNFs) to migrate from their current locations to other locations to satisfy the Quality of Service requirements. We formulate our problem to find near-optimal locations of UAVs, transmit power, subcarrier assignment, placement, and scheduling the requested service's functions over the UAVs and perform suitable VNF migration. Then we propose a novel Hierarchical Hybrid Continuous and Discrete Action (HHCDA) deep reinforcement learning method to solve our problem. Finally, the convergence and computational complexity of the proposed algorithm and its performance analyzed for different parameters. Simulation results show that our proposed HHCDA method decreases the request reject rate and average delay by 31.5% and 20% and increases the energy efficiency by 40% compared to DDPG method.

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“…Khai et al formulate a MILP optimization problem with the objective of minimizing the migration time as part of their flexible, multi-step approach for VM migration [8]. Hirayama et al formulate an ILP problem to seek optimal resource allocation and train an encoder-decoder recurrent neural network, which is effective in preventing resource shortage and minimizing occurrences of VNF migration [9]. The works outlined above all address the VNF migration problem; however, they do not consider SLA requirements related to availability.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Migration Strategy for Virtual Network Function Instances

Manias¹,

Hawilo²,

Shami³

2020

Preprint

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With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased demand. Although Network Function Virtualization (NFV) has been identified as a promising solution, several challenges must be addressed to ensure its feasibility. In this paper, we address the Virtual Network Function (VNF) migration problem by developing the VNF Neural Network for Instance Migration (VNNIM), a migration strategy for VNF instances. The performance of VNNIM is further improved through the optimization of the learning rate hyperparameter through particle swarm optimization. Results show that the VNNIM is very effective in predicting the post-migration server exhibiting a binary accuracy of 99.07% and a delay difference distribution that is centered around a mean of zero when compared to the optimization model. The greatest advantage of VNNIM, however, is its run-time efficiency highlighted through a run-time analysis.

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Service Function Migration Scheduling based on Encoder-Decoder Recurrent Neural Network

Cited by 12 publications

References 10 publications

Advanced data analytics using three-stage intelligent model pipelining for containerized microservices in 5G networks and beyond

Advanced data analytics using three-stage intelligent model pipelining for containerized microservices in 5G networks and beyond

AI-Based and Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

A Machine Learning-Based Migration Strategy for Virtual Network Function Instances

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