QoS-Aware Service Prediction and Orchestration in Cloud-Network
Integrated Beyond 5G
- URL: http://arxiv.org/abs/2309.10185v1
- Date: Mon, 18 Sep 2023 22:24:42 GMT
- Title: QoS-Aware Service Prediction and Orchestration in Cloud-Network
Integrated Beyond 5G
- Authors: Mohammad Farhoudi, Masoud Shokrnezhad, and Tarik Taleb
- Abstract summary: We present a non-linear programming model that formulates the optimization problem with the aiming objective of minimizing overall cost while enhancing latency.
We introduce a DDQL-based technique using RNNs to predict user behavior, empowered by a water-filling-based algorithm for service placement.
- Score: 11.864695986880347
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Novel applications such as the Metaverse have highlighted the potential of
beyond 5G networks, which necessitate ultra-low latency communications and
massive broadband connections. Moreover, the burgeoning demand for such
services with ever-fluctuating users has engendered a need for heightened
service continuity consideration in B5G. To enable these services, the
edge-cloud paradigm is a potential solution to harness cloud capacity and
effectively manage users in real time as they move across the network. However,
edge-cloud networks confront a multitude of limitations, including networking
and computing resources that must be collectively managed to unlock their full
potential. This paper addresses the joint problem of service placement and
resource allocation in a network-cloud integrated environment while considering
capacity constraints, dynamic users, and end-to-end delays. We present a
non-linear programming model that formulates the optimization problem with the
aiming objective of minimizing overall cost while enhancing latency. Next, to
address the problem, we introduce a DDQL-based technique using RNNs to predict
user behavior, empowered by a water-filling-based algorithm for service
placement. The proposed framework adeptly accommodates the dynamic nature of
users, the placement of services that mandate ultra-low latency in B5G, and
service continuity when users migrate from one location to another. Simulation
results show that our solution provides timely responses that optimize the
network's potential, offering a scalable and efficient placement.
Related papers
- AI Flow at the Network Edge [58.31090055138711]
AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers.
This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
arXiv Detail & Related papers (2024-11-19T12:51:17Z) - Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN [5.3807986199066375]
This paper aims to address the problem by globally optimizing the resilience of a dense multi-cell network based on multi-agent deep reinforcement learning.
Specifically, our proposed solution can dynamically tilt cell antennas and reconfigure transmit power to mitigate outages and increase both coverage and service availability.
arXiv Detail & Related papers (2024-07-25T14:19:59Z) - Edge computing service deployment and task offloading based on
multi-task high-dimensional multi-objective optimization [5.64850919046892]
This study investigates service deployment and task offloading challenges in a multi-user environment.
To ensure stable service provisioning, beyond considering latency, energy consumption, and cost, network reliability is also incorporated.
To promote equitable usage of edge servers, load balancing is introduced as a fourth task offloading objective.
arXiv Detail & Related papers (2023-12-07T07:30:47Z) - Deep Reinforcement Learning Based Resource Allocation for Cloud Native
Wireless Network [20.377823731801456]
Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability.
The vast array of cloud native services and applications presents a new challenge in resource allocation for dynamic cloud computing environments.
We introduce deep reinforcement learning techniques and introduce two model-free algorithms capable of monitoring the network state and dynamically training allocation policies.
Our findings demonstrate significant improvements in network efficiency, underscoring the potential of our proposed techniques in unlocking the full potential of cloud native wireless networks.
arXiv Detail & Related papers (2023-05-10T15:32:22Z) - Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural
Networks on Edge NPUs [74.83613252825754]
"smart ecosystems" are being formed where sensing happens concurrently rather than standalone.
This is shifting the on-device inference paradigm towards deploying neural processing units (NPUs) at the edge.
We propose a novel early-exit scheduling that allows preemption at run time to account for the dynamicity introduced by the arrival and exiting processes.
arXiv Detail & Related papers (2022-09-27T15:04:01Z) - Artificial Intelligence Empowered Multiple Access for Ultra Reliable and
Low Latency THz Wireless Networks [76.89730672544216]
Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era.
To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required.
This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
arXiv Detail & Related papers (2022-08-17T03:00:24Z) - On-Demand Resource Management for 6G Wireless Networks Using
Knowledge-Assisted Dynamic Neural Networks [13.318287511072354]
We study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process.
A dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements.
By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration.
arXiv Detail & Related papers (2022-08-02T23:40:03Z) - A Joint Energy and Latency Framework for Transfer Learning over 5G
Industrial Edge Networks [53.26338041079138]
We propose a transfer learning-enabled edge-CNN framework for 5G industrial edge networks.
In particular, the edge server can use the existing image dataset to train the CNN in advance.
With the aid of TL, the devices that are not participating in the training only need to fine-tune the trained edge-CNN model without training from scratch.
arXiv Detail & Related papers (2021-04-19T15:13:16Z) - On Topology Optimization and Routing in Integrated Access and Backhaul
Networks: A Genetic Algorithm-based Approach [70.85399600288737]
We study the problem of topology optimization and routing in IAB networks.
We develop efficient genetic algorithm-based schemes for both IAB node placement and non-IAB backhaul link distribution.
We discuss the main challenges for enabling mesh-based IAB networks.
arXiv Detail & Related papers (2021-02-14T21:52:05Z) - Deep Learning for Radio Resource Allocation with Diverse
Quality-of-Service Requirements in 5G [53.23237216769839]
We develop a deep learning framework to approximate the optimal resource allocation policy for base stations.
We find that a fully-connected neural network (NN) cannot fully guarantee the requirements due to the approximation errors and quantization errors of the numbers of subcarriers.
Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks.
arXiv Detail & Related papers (2020-03-29T04:48:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.