Survivable Task Allocation in Cloud Radio Access Networks With Mobile-Edge Computing

Yang, Song; He, Nan; Li, Fan; Trajanovski, Stojan; Chen, Xu; Wang, Weichao; Fu, Xiaoming

doi:10.1109/jiot.2020.3010533

Cited by 18 publications

(8 citation statements)

References 48 publications

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“…For given R dl k , the downlink edge latency τ dl E,k of UE k is given as (20). For the non-orthogonal multiple access scheme as described above, we aim at jointly optimizing the variables p, Q, c, F and C F with the goal of minimizing the total latency τ T in (22). The problem can be written as…”

Section: B Non-orthogonal Multiple Accessmentioning

confidence: 99%

“…However, it was assumed in [21] that the physical-layer (PHY) functionalities, which include channel encoding and decoding, are located only at ENs. In [22], the authors addressed the task allocation and traffic path planning problem for a C-RAN system under the assumption that the service latency consists of task processing delay and path delay only on fronthaul links.…”

Section: Introductionmentioning

confidence: 99%

“…Initialize {c,p,Q, F, C F , τ , R} as arbitrary values/matrices that satisfy the constraints (31b)-(31j), and set t ← 1. Calculate the total latency τ T in(22) with the initialized {c,p,Q, F, C F , τ , R}, and set τ (0)T ← τ T . Set {λ, Γ, θ} according to(25) and(34).…”

mentioning

confidence: 99%

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Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Park

Jeong

et al. 2021

IEEE Trans. on Signal and Inf. Process. over Networks

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Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.

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Section: B Non-orthogonal Multiple Accessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Park

Jeong

et al. 2021

IEEE Trans. on Signal and Inf. Process. over Networks

View full text Add to dashboard Cite

show abstract

Section: B Non-orthogonal Multiple Accessmentioning

confidence: 99%

“…However, it was assumed in [21] that the physicallayer (PHY) functionalities, which include channel encoding and decoding, are located only at ENs. In [22], the authors addressed the task allocation and traffic path planning problem for a C-RAN system under the assumption that the service latency consists of task processing delay and path delay only on fronthaul links.…”

mentioning

confidence: 99%

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems with Minimal End-to-End Latency

Park¹,

Jeong²,

Na³

et al. 2021

Preprint

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Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with oneway uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finitecapacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.

show abstract

Self-play learning strategies for resource assignment in Open-RAN networks

et al. 2022

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Survivable Task Allocation in Cloud Radio Access Networks With Mobile-Edge Computing

Cited by 18 publications

References 48 publications

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems with Minimal End-to-End Latency

Self-play learning strategies for resource assignment in Open-RAN networks

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