Multiple access computational offloading with computation constraints

Salmani, Mahsa; Davidson, T.N.

doi:10.1109/spawc.2017.8227713

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…where F(y (tj ) ) denotes the feasible set of problem (26) when fixing y = y (tj ) . By taking limit along the converging subsequence (x (tj ) , y (tj ) ) on both sides of (30), we get…”

Section: B Proof Of Theoremmentioning

confidence: 99%

“…There are some related works worth mentioning. The works [22] and [26] consider a joint optimization of radio and computation resources for energy minimization with latency constraints in single-cell and multicell networks, respectively (resp. ), where all the computation is done at the cloud with the UE-BS association prefixed.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, since y (t0) is obtained by minimizing problem (26) with fixed x (t0) , we have ν(x (t0) , y (t0) ; x (t) ) ≤ ν(x (t0) , y; x (t) ), ∀ y (33)…”

mentioning

confidence: 99%

“…Recall that for fixed x, the optimal y can be uniquely computed in closed form by (13). Therefore, the optimal ȳ of problem (26) takes the form of ( 13) (with V replaced by V ). Now, by substituting this specific ȳ into ζ k, ( x, ȳ), one can easily verify that the following holds:…”

mentioning

confidence: 99%

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Min-Max Latency Optimization for Multiuser Computation Offloading in Fog-Radio Access Networks

Liu

Lin

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper considers computation offloading in fogradio access networks (F-RAN), where multiple user equipments (UEs) offload their computation tasks to the F-RAN through a number of fog nodes. Each UE can choose one of the fog nodes to offload its task, and each fog node may simultaneously serve multiple UEs. Depending on the computation burden at the fog nodes, the tasks may be computed by the fog nodes or further offloaded to the cloud via capacity-limited fronthaul links. To compute all UEs tasks as fast as possible, joint optimization of UE-Fog association, radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs. This min-max problem is formulated as a mixed integer nonlinear program (MINP). We first show that the MINP can be reformulated as a continuous optimization problem, and then employ the majorization minimization (MM) approach to finding a solution for it. The MM approach that we develop herein is unconventional in that-each MM subproblem is inexactly solved with the same provable convergence guarantee as the conventional exact MM. In addition, we also consider a cooperative offloading model, where the fog nodes compress-andforward their received signals to the cloud. Under this model, a similar min-max latency optimization problem is formulated and tackled again by the inexact MM approach. Simulation results show that the proposed algorithms outperform some heuristic offloading strategies, and that the cooperative offloading is generally better than the non-cooperative one.

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“…where F(y (tj ) ) denotes the feasible set of problem (26) when fixing y = y (tj ) . By taking limit along the converging subsequence (x (tj ) , y (tj ) ) on both sides of (30), we get…”

Section: B Proof Of Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…On the other hand, since y (t0) is obtained by minimizing problem (26) with fixed x (t0) , we have ν(x (t0) , y (t0) ; x (t) ) ≤ ν(x (t0) , y; x (t) ), ∀ y (33)…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Min-Max Latency Optimization for Multiuser Computation Offloading in Fog-Radio Access Networks

Liu

Lin

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In addition, [23] proposed a wireless communication resource allocation algorithm that can meet the constraints of system computing resources and designed a loosely coupled architecture to reduce the burden on the fronthaul link and to achieve ultralow latency. References [24] and [25] studied the optimization of wireless and computing resources to minimize energy consumption in a single-cell and multicell network under a given delay constraint by deleting them jointly. However, all tasks in this case are performed in the cloud, and the access relationship between the user and the base station is set in advance.…”

Section: Introductionmentioning

confidence: 99%

Joint Allocation on Communication and Computing Resources for Fog Radio Access Networks

Wang

Xiong

et al. 2020

IEEE Access

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To improve the user experience, an increasing number of mobile applications offload their computing tasks to servers with powerful computing capabilities. The fog radio access network (F-RAN) incorporates the concept of "fog computing" into the access network architecture, endowing an edge network with computing, storage, communication and control functions. In this paper, we consider a multiple fog access point (F-AP) and a multiuser F-RAN, where each user generates two different tasks: communication and computation. To satisfy the diverse quality of service requirements of different users, we jointly optimize the spectrum access, computation offloading and radio resource allocation. The problem is modeled as a mixed integer nonlinear programming problem, which is difficult to solve. In view of this, we propose a genetic algorithm based on convex optimization, i.e., the genetic convex optimization algorithm (GCOA), which divides the mixed integer nonlinear programming problem into two parts, i.e., optimization and convex optimization, to solve it in polynomial time. Simulation results are provided to verify the effectiveness of the algorithm.

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Latency minimization for multiuser computation offloading in fog-radio access networks

Liu²,

Lin³

et al. 2023

Digital Communications and Networks

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Multiple access computational offloading with computation constraints

Cited by 7 publications

References 15 publications

Min-Max Latency Optimization for Multiuser Computation Offloading in Fog-Radio Access Networks

Min-Max Latency Optimization for Multiuser Computation Offloading in Fog-Radio Access Networks

Joint Allocation on Communication and Computing Resources for Fog Radio Access Networks

Latency minimization for multiuser computation offloading in fog-radio access networks

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