Learning to Schedule Network Resources Throughput and Delay Optimally Using Q<sup>+</sup>-Learning

Bae, Jeongmin; Lee, Joohyun; Chong, Song

doi:10.1109/tnet.2021.3051663

Cited by 14 publications

(9 citation statements)

References 36 publications

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“…The authors note that their technique does not work well with frequently changing traffic patterns. Other model-free and AI-based solutions also suffer from similar issues [13][14][15][16].…”

Section: A Related Workmentioning

confidence: 99%

Scheduling Slice Requests in 5G Networks

Prasad

Sunny

2023

IEEE/ACM Trans. Networking

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5G and beyond networks are expected to support flows with varied Quality-of-Service (QoS) requirements under unpredictable traffic conditions. Consequently, designing policies ensuring optimal system utilization in such networks is challenging. Given this, we formulate a long-term time-averaged scheduling problem that minimizes a weighted function of packets dropped by the 5G wireless base station. We then present two policies for this problem. The first is a delay-guaranteed near-optimal policy, and the second is a delay-guaranteed sub-optimal policy that provides flow isolation. We perform extensive simulations to understand the performance of these policies. Further, we study these policies in the presence of a closed-loop flow rate-control mechanism.

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Section: A Related Workmentioning

confidence: 99%

Scheduling Slice Requests in 5G Networks

Prasad

Sunny

2023

IEEE/ACM Trans. Networking

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“…The scheme adaptively updates the likelihood based on fitness parameter. The likelihood, if ℱ 𝑡 > ℱ ↓ is computed as (18),…”

Section: Genetic Algorithm With Improved Crossover Mechanismmentioning

confidence: 99%

“…Namely, single level crossover (SLC) and two-level crossover (TLC). In SLC, two chromosomes are chosen to carryout crossover and likelihood of crossover is computed using (18) and (19). Except interior of sub-chromosomes, the point of crossover is arbitrarily selected from two parent chromosome.…”

Section: Genetic Algorithm With Improved Crossover Mechanismmentioning

confidence: 99%

“…Despite significant work done on QoS provisioning [9], [10], existing protocol of resource scheduling design [11]- [14] predominantly considering improving just throughput and resource utilization under homogenous network; however, improving user quality of experience play very important role in future generation cellular network which is heterogeneous in nature [15]- [17]. Recently, deep learning and reinforcement learning approach have been adopted for resource provisioning in cellular network [18]- [20]. However, these model require huge amount of data in prior and require higher training time.…”

Section: Introductionmentioning

confidence: 99%

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Delay aware downlink resource allocation scheme for future generation tactical wireless networks

Shankar

Patil

2021

IJ-AI

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<span lang="EN-US">For a very long time protecting physical border integrity is considered to be a challenging thing. Government organizations must provide trade operations for economic growth and at the same time must prevent malicious activity. A different resource such as drones, sensors, and radars are used for monitoring border areas which must be communicated to the remote border security force. Efficient wireless communication is required for communicating information. However, these devices cannot connect to a centralized network directly; thus, are connected in an ad-hoc fashion to connect centralized server. Different tactical network applications require different quality of service (QoS); hus efficient resource scheduling plays a very important role. Existing resource scheduling adopting deep learning and reinforcement techniques fails to meet the quality of experience (QoE) of the user and doesn’t assure access fairness among contending users. Further, require network information in prior and induce high training time. For overcoming research issues, this paper presents a delay-aware downlink resource scheduling (DADRA) technique for future generation networks. The optimization problem of reducing buffer overflow and improving scheduling QoS performance is solved using a genetic algorithm with an improved crossover function. Experiment outcome shows DADRA achieves much better throughput, slot utilization, and packet failure performance when compared with standard resource allocation technique.</span>

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“…In [15], Zhao et al proposed a delay minimization algorithm based on UCB, but neglected the joint optimization of energy consumption and delay. In [16], Bae et al proposed a downlink network routing algorithm based on UCB to jointly optimize throughput and delay, but ignored the influence of complex EMI and service priority. Moreover, all the abovementioned works do not consider the impact of complex EMI and service priority of smart grid on the joint optimization of route and power selection.…”

Section: Introductionmentioning

confidence: 99%

UCB-Based Route and Power Selection Optimization for SDN-Enabled Industrial IoT in Smart Grid

Zhou

Liang

et al. 2022

Wireless Communications and Mobile Computing

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As an essential building block for smart grid, the industrial internet of things (IIoT) plays a significant role in providing powerful sensing capability and ubiquitous connectivity for differentiated power services. The rapid development of smart grid imposes higher data monitoring and transmission requirements in terms of delay and energy efficiency. However, due to the severe electromagnetic interference (EMI) caused by massive electrical equipment, the transmission performance of IIoT becomes inferior. The traditional single-hop transmission mode evolves towards a multihop cooperation mode to satisfy differentiated quality of service (QoS) requirements. In this paper, we propose an upper confidence bound- (UCB-) based joint route and power selection optimization algorithm to support multihop cooperation mode evolution, which adopts a software-defined networking- (SDN-) enabled IIoT network framework to simplify network configuration and management. Compared with existing local-side-information-based route selection (LSI-RS) and random route selection (RRS) algorithms, simulation results demonstrate that the proposed algorithm has superior performances in total delay, energy efficiency, and utility.

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Learning to Schedule Network Resources Throughput and Delay Optimally Using Q⁺-Learning

Cited by 14 publications

References 36 publications

Scheduling Slice Requests in 5G Networks

Scheduling Slice Requests in 5G Networks

Delay aware downlink resource allocation scheme for future generation tactical wireless networks

UCB-Based Route and Power Selection Optimization for SDN-Enabled Industrial IoT in Smart Grid

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Learning to Schedule Network Resources Throughput and Delay Optimally Using Q+-Learning

Cited by 14 publications

References 36 publications

Scheduling Slice Requests in 5G Networks

Scheduling Slice Requests in 5G Networks

Delay aware downlink resource allocation scheme for future generation tactical wireless networks

UCB-Based Route and Power Selection Optimization for SDN-Enabled Industrial IoT in Smart Grid

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Learning to Schedule Network Resources Throughput and Delay Optimally Using Q⁺-Learning