NOMA-Based Energy-Delay Trade-Off For Mobile Edge Computation Offloading in 5G Networks

Nouri, Nima; Rafiee, Parisa; Tadaion, Aliakbar

doi:10.1109/istel.2018.8661110

Cited by 15 publications

(4 citation statements)

References 17 publications

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“…An SSPO algorithm was applied to minimize the EEf function. Nouri et al 84 reduced network costs by considering a weighted mix of ECp and delay based on each device's remaining energy. The SCA algorithm was used to solve a non‐convex MINLP problem.…”

Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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SummaryIn today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state‐of‐the‐art CO techniques in edge computing (EC) in the context of energy, Quality of Service (QoS), authentication and traceability. In this SLR, the evolution of offloading techniques is analyzed in detail. A total of 138 articles, spanning from 2016 to 2023 (till date), have been classified into QoS, energy, and authentication and traceability‐based CO techniques. The optimization‐based techniques are the most preferred choices to improve the QoS and reduce energy in the research field of CO in EC. In addition, this paper explores the significant issues and challenges that require further investigation. For future research, energy, QoS, and data provenance for dependent or dynamic task offloading in mobility scenarios can be explored further.

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Section: Energy‐based Co Techniques In Ecmentioning

confidence: 99%

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Kanupriya,

Chana,

Goyal

2024

Concurrency and Computation

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“…The optimal offloading probability and transit power that minimize delay and energy consumption are calculated in [12] using Interior Point Method to solve non-linear optimization problem. The energy delay tradeoff for MEC offloading in [14] is set as mixed integer programming problem and solved by convex approximation.…”

Section: Related Workmentioning

confidence: 99%

Cloud and Edge Computation Offloading for Latency Limited Services

et al. 2021

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Multi-access Edge Computing (MEC) is recognised as a solution in future networks to offload computation and data storage from mobile and IoT devices to the servers at the edge of mobile networks. It reduces the network traffic and service latency compared to passing all data to cloud data centers while offering greater processing power than handling tasks locally at terminals. Since MEC servers are scattered throughout the radio access network, their computation capacities are modest in comparison to large cloud data centers. Therefore, offloading decision between MEC and cloud server should minimize the usage of the resources while maximizing the number of accepted delay critical requests. In this work we formulate the joint optimization of communication and computation resources allocation for computation offloading (CO) requests with strict latency constraints. We show that the global optimization problem is NP-hard and propose an efficient heuristic solution based on the single user optimal solution. Simulation results are presented to show the effectiveness of the proposed algorithm, compared to optimal and baseline solution where tasks are allocated in the order of arrival, with different system parameters. They show that our algorithm performs close to the optimal in terms of resource utilization and outperforms the baseline algorithm in terms of acceptance rate.INDEX TERMS Cloud computing, multi access edge computing (MEC), computational offloading (CO), end-to-end latency, limited-latency services, joint resource allocation.

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“…x m,off (t) is defined in the same way for Q m,off (t). Besides, the computation results from the BS are usually of small size [42]- [44], and the standby power accounts for a relatively small proportion of overall power consumption by standby power management techniques such as autonomous normallyoff power transition [45], adaptive data retention voltage regulating scheme [46], and power-gating and source bias design technique [47]. Therefore, by neglecting the power consumption of downlink reception and standby mode on devices, the total power consumption can be given by…”

Section: Computation Modelmentioning

confidence: 99%

Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge

Hua,

Tian,

Lyu

et al. 2021

Preprint

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By exploiting the superiority of non-orthogonal multiple access (NOMA), NOMA-aided mobile edge computing (MEC) can provide scalable and low-latency computing services for the Internet of Things. However, given the prevalent stochasticity of wireless networks and sophisticated signal processing of NOMA, it is critical but challenging to design an efficient task offloading algorithm for NOMA-aided MEC, especially under a large number of devices. This paper presents an online algorithm that jointly optimizes offloading decisions and resource allocation to maximize the long-term system utility (i.e., a measure of throughput and fairness). Since the optimization variables are temporary coupled, we first apply Lyapunov technique to decouple the long-term stochastic optimization into a series of per-slot deterministic subproblems, which does not require any prior knowledge of network dynamics. Second, we propose to transform the non-convex per-slot subproblem of optimizing NOMA power allocation equivalently to a convex form by introducing a set of auxiliary variables, whereby the time-complexity is reduced from the exponential complexity to O(M 3/2 ). The proposed algorithm is proved to be asymptotically optimal, even under partial knowledge of the device states at the base station. Simulation results validate the superiority of the proposed algorithm in terms of system utility, stability improvement, and the overhead reduction.

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NOMA-Based Energy-Delay Trade-Off For Mobile Edge Computation Offloading in 5G Networks

Cited by 15 publications

References 17 publications

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

Cloud and Edge Computation Offloading for Latency Limited Services

Online Offloading Scheduling for NOMA-Aided MEC Under Partial Device Knowledge

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