Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks

Zhang, Jiao; Hu, Xiping; Ning, Zhaolong; Ngai, Edith C.-H.; Zhou, Li; Wei, Jibo; Cheng, Jun; Hu, Bin

doi:10.1109/jiot.2017.2786343

Cited by 485 publications

(260 citation statements)

References 33 publications

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“…These challenges have yet to be addressed in the literature, especially in heterogeneous IIoT. Although there are extensive works about resource scheduling in MEC [10]- [12], the joint optimization for bandwidth, computing, and energy intake/output is of insufficient study. Offline algorithm for the joint optimization would require complete non-causal information of networks and may suffer from the curse of dimensionality when the system is in large scale.…”

Section: A Related Workmentioning

confidence: 99%

Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

Lyu

Tian

2019

IEEE Internet Things J.

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A spurt of progress in wireless power transfer (WPT) and mobile edge computing (MEC) provides a promising approach for Industrial Internet of Things (IIoT) to enhance the quality and productivity of manufacturing. Scheduling in such a scenario is challenging due to congested wireless channels, time-dependent energy constraints, complicated device heterogeneity, and prohibitive signaling overheads. In this paper, we first propose an online algorithm, called energy-aware resource scheduling (ERS), to maximize the system utility comprising throughput and fairness, with consideration on both system sustainability and stability. Based on Lyapunov optimization and convex optimization techniques, the proposed algorithm achieves asymptotic optimality for heterogeneous IIoT systems without prior knowledge of network state information (NSI). Subsequently, we extend the ERS algorithm to a more realistic scenario where the overhead and delay of NSI feedbacks are nonnegligible. The optimal scheduling decisions of the scenario are provided, and the optimality loss on system utility under outdated NSI is analyzed. Simulations verify our theoretical claims and demonstrate the gains of our proposed ERS algorithm over alternative benchmark schemes.

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Section: A Related Workmentioning

confidence: 99%

Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

Lyu

Tian

2019

IEEE Internet Things J.

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“…where κ is a coefficient depending on the chip architecture [11]. Thus, the objective of each worker i is to maximize the following utility function:…”

Section: System Modelmentioning

confidence: 99%

“…λ i = q i 2κc 2 i . Then, we equate the first derivative given in (11) to zero to derive the value of Lagrange multiplier, α, at the optimal point as:…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

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Motivating Workers in Federated Learning: A Stackelberg Game Perspective

2020

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Due to the large size of the training data, distributed learning approaches such as federated learning have gained attention recently. However, the convergence rate of distributed learning suffers from heterogeneous worker performance.In this paper, we consider an incentive mechanism for workers to mitigate the delays in completion of each batch. We analytically obtained equilibrium solution of a Stackelberg game. Our numerical results indicate that with a limited budget, the model owner should judiciously decide on the number of workers due to trade off between the diversity provided by the number of workers and the latency of completing the training.Y. Sarikaya (ysarikaya@sabanciuniv.edu) and O. Ercetin (oercetin@sabanciuniv.edu) are with the

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“…We assume that all of the PUs offload their computation tasks to the MEC server for executing, each SU can offload the computation task or compute locally. Similar to other works, we consider always having tasks arriving at the MEC server to be completed, thus the workload arriving model if full‐loaded model. We denote

a_{m_{i}} \in false{0, 1 false}, \forall m, i

as the offloading decision of SU m i .…”

Section: System Model and Offloading Problemmentioning

confidence: 99%

Edge computing and power control in NOMA‐enabled cognitive radio networks

Cheng

Chen

Liang

2019

Trans Emerging Tel Tech

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Due to the limited computation resources of mobile devices in cognitive radio networks, the secondary users in the network can suffer from long executing time, which is not acceptable for latency‐sensitive and computation‐intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user and within the available computation resources of the MEC server, the primary user and secondary users both can offload tasks to the MEC server through nonorthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the nonconvexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near‐optimal solution. Simulation results show that under the same parameters, such as the number of primary users, maximum transmit power, computational capability of the MEC server and the computational capability of the secondary users, the proposed NOMA‐enabled computation offloading scheme can effectively reduce the overall computing latency for the secondary network and improve the percentage of offloading secondary users than those of OMA‐enabled.

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Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks

Cited by 485 publications

References 33 publications

Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

Motivating Workers in Federated Learning: A Stackelberg Game Perspective

Edge computing and power control in NOMA‐enabled cognitive radio networks

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