Joint QoS and energy-efficient resource allocation and scheduling in 5G Network Slicing

Saibharath, S; Mishra, Sudeepta; Hota, Chittaranjan

doi:10.1016/j.comcom.2023.02.009

Cited by 17 publications

(4 citation statements)

References 20 publications

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“…This approach ensures a more sustainable and efficient operation of cellular networks. Saibharath et al [42] have discussed the Joint QoS and energy-efficient resource allocation and scheduling in a 5G Network. Slicing is the process of optimizing the allocation and scheduling of resources among different slices in a 5G network to ensure high quality of service (QoS) for users while also minimizing energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Sangeetha,

Logeshwaran,

Faheem

et al. 2024

The Journal of Engineering

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This paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy‐aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.

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Section: Literature Reviewmentioning

confidence: 99%

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Sangeetha,

Logeshwaran,

Faheem

et al. 2024

The Journal of Engineering

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“…The job scheduling process involves selecting a job from the job queue, considering the user service quality desired. Then, the server of resource information is consulted to obtain a suitable list of resources that meet the user's QoS criteria [26]. The role of the RIS is to provide a resource list along with their estimated response times for task completion.…”

Section: Proposed Systemmentioning

confidence: 99%

Customizing the minimum number of replicas for achieving fault tolerance in a cloud/grid environment

S. Almhanna,

A. Murshedi,

Al-Turaihi

et al. 2024

Bulletin EEI

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Networks consist of numerous resources; it is crucial not to overlook fault tolerance and consider it during planning. This is because errors during implementation can result in wasted time and effort, thereby squandering these resources. One solution to address this issue effectively is to implement the task on multiple resources to minimize the occurrence of failed tasks. However, employing an unspecified or fixed number of resources can lead to the depletion of network resources and the overall failure of the network. Replication plays a pivotal role in enhancing data availability in distributed systems. By storing data in multiple locations, users can still access it even if some copies are unavailable due to site failure. Many replication-based algorithms utilize a predetermined number of iterations per function, which may consume excessive network resources, even if the ongoing task does not require such abundant resources. This paper proposes task replication as a viable mechanism for an efficient and fault-tolerant scheduling system. We introduce an algorithm that dynamically selects the optimal and minimal number of replicas based on the network's failure history. This approach aims to minimize the failure rate during task execution.

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“…Network slice request (NSR) is a logical service formed by a group of VNFs through network interconnection 15 . According to VNF importance index, VNF reliability requirements are calculated to provide different reliability guarantees.…”

Section: G Low Orbit Constellation Network Slice Reliability Mappingmentioning

confidence: 99%

Research on reliability mapping of 5G low orbit constellation network slice based on deep reinforcement learning

Xiao,

Li,

et al. 2024

Sci Rep

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Reliability mapping of 5G low orbit constellation network slice is an important means to ensure link network communication. The problem of state space explosion is a typical problem. The deep reinforcement learning method is introduced. Under the 5G low orbit constellation integrated network architecture based on software definition network (SDN) and network function virtualization (NFV), the resource requirements and resource constraints of the virtual network function (VNF) are comprehensively considered to build the 5G low orbit constellation network slice reliability mapping model, and the reliability mapping model parameters are trained and learned by using deep reinforcement learning, solve the problem of state space explosion in the reliability mapping process of 5G low orbit constellation network slices. In addition, node backup and link backup strategies based on importance are adopted to solve the problem that VNF/link reliability is difficult to meet in the reliability mapping process of 5G low orbit constellation network slice. The experimental results show that this method improves the network throughput, packet loss rate and intra slice traffic of 5G low orbit constellation, and can completely repair network faults within 0.3 s; For different number of 5G low orbit constellation network slicing requests, the reliability of this method remains above 98%; For SFC with different lengths, the average network delay of this method is less than 0.15 s.

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Joint QoS and energy-efficient resource allocation and scheduling in 5G Network Slicing

Cited by 17 publications

References 20 publications

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Customizing the minimum number of replicas for achieving fault tolerance in a cloud/grid environment

Research on reliability mapping of 5G low orbit constellation network slice based on deep reinforcement learning

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