Effects of Differentiated 5G Services on Computational and Radio Resource Allocation Performance

Jankovic, Jasna; Ilić, Željko; Oracevic, Alma; Kazmi, S. M. Ahsan; Hussain, Rasheed

doi:10.1109/tnsm.2021.3060865

Cited by 19 publications

(11 citation statements)

References 28 publications

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“…There are also some studies that solved the problem of computing and communication resource allocation among multiple tenants. The problem can be solved by various algorithms such as the upper-tier first with the latency-bounded over-provisioning prevention (UFLOP) algorithm proposed in [16], the convex optimization used in [17], and the computational load distribution algorithm adopt in [18]. In addition, the authors in [19] jointly considered communication and computing resources and used the DQN algorithm to maximize the utility of MVNO.…”

Section: A Research On Slicing-based Resource Optimizationmentioning

confidence: 99%

Slicing-based resource optimization in multi-access edge network using ensemble learning aided DDPG algorithm

Gong

Wei

et al. 2023

J. Commun. Netw.

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Section: A Research On Slicing-based Resource Optimizationmentioning

confidence: 99%

Slicing-based resource optimization in multi-access edge network using ensemble learning aided DDPG algorithm

Gong

Wei

et al. 2023

J. Commun. Netw.

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“…Although the new services supply a selection of new use-cases to be implemented, it is still difficult to ensure the High QoS they require. The authors in [31] take a look at the characteristics of the new 5G services (such as the results of separated 5G services on the radio, computational resource allocation) as well as recommend a joint computational and also radio resource allocation strategy that evaluates the QoS performance of each 5G service separately. They take into account different aspects of 5G services such as traffic qualities, resource demands, as well as QoS requirements.…”

Section: Related Workmentioning

confidence: 99%

Delay-Based Resource Allocation With Fairness Guarantee and Minimal Loss for eMBB in 5G Heterogeneous Networks

2022

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Heterogeneity is defined as one of the core merits in the 5th Generation (5G) mobile systems. Such a trait is prominent in modern 5G traffic scenarios, i.e., enhanced Mobile Broadband (eMBB), where coarse Quality of Service (QoS) profiles need to be satisfied over the shared transmission channel. For that, Radio Resource Management (RRM) became a challenging problem that should be figured out based on the traffic scenario. Extensive efforts dwelled in the literature attempt to solve the resource allocation problem in the downlink channel by maximizing the gain for a certain aspect in the QoS profile. This, however, poses a challenge for 5G-related traffic scenarios such that multi-dimension QoS shall be attainable. Therefore, in this paper, a Delay-aware Resource Allocation for Guaranteed Fairness and minimal Loss (DRAGFL) is proposed to enhance the QoS for Real-Time (RT) services in eMBB 5G Heterogeneous Networks. The ultimate objective of DRAGFL is to maintain a steadily low latency for delay-sensitive flows without restraining the service fairness and the data loss in the overload states. DRAGFL enhances the efficiency of the MAC layer scheduler throughout three phases. Firstly, generating delay-derived weight matrix of the resources blocks to radio bearers from eMBB different classes. Then, greedy-based flows prioritization for Resource Blocks (RBs) assignment. Finally, the data rate of the RT and Non RT (NRT) flows is further emphasized through a channel-aware principle that is invoked conditioning to the remaining radio resources and available data to be scheduled in the interval. The performance evaluation using system-level simulations reveals prominent gains of DRAGFL in sustaining a steadily low end-to-end delay in the high congestion states. Moreover, it maintains ideal states of service fairness with low data loss for different traffic loads to indicate a promising MAC scheduler for the developing scenarios of 5G.

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“…We consider a topology [20] (not shown in this paper due to space limitation) with 52 edge nodes, each edge node is associated with 3 CSs. The radio configuration is in line with the RAN VNF parameter specified in [21]. We assume that all SFCs are running with 4 VNFs.…”

Section: A Simulation Setupmentioning

confidence: 99%

“…We set the simulation time as 1000 time units. For each SFC request, the resource block N k , data rate λ k (Mbps), and memory(GB) are set as randomly distributed value within [50, 100], [20,200] and [1,8], respectively [21].…”

Section: A Simulation Setupmentioning

confidence: 99%

Deterministic Service Function Chaining over Beyond 5G Edge Fabric

Taleb

Zhang

2021

2021 IEEE Global Communications Conference (GLOBECOM)

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Effects of Differentiated 5G Services on Computational and Radio Resource Allocation Performance

Cited by 19 publications

References 28 publications

Slicing-based resource optimization in multi-access edge network using ensemble learning aided DDPG algorithm

Slicing-based resource optimization in multi-access edge network using ensemble learning aided DDPG algorithm

Delay-Based Resource Allocation With Fairness Guarantee and Minimal Loss for eMBB in 5G Heterogeneous Networks

Deterministic Service Function Chaining over Beyond 5G Edge Fabric

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