Optimal auction for delay and energy constrained task offloading in mobile edge computing

Mashhadi, Farshad; Monroy, Sergio A. Salinas; Bozorgchenani, Arash; Tarchi, Daniele

doi:10.1016/j.comnet.2020.107527

Cited by 35 publications

(19 citation statements)

References 20 publications

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“…However, this work lacks a detailed formulation of the delay model and considers a simplified scenario. In [27], the authors have formulated user to server allocation as an auction problem and proposed two allocation and payment deep neural networks to maximize the profit of edge servers and satisfy the energy and delay constraints of users.…”

Section: Related Workmentioning

confidence: 99%

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Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

Bozorgchenani

Tarchi

Hossain

2022

IEEE Trans. on Mobile Comput.

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With the rapid advancement of Intelligent Transportation Systems (ITS) and vehicular communications, Vehicular Edge Computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the networkand base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.

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Section: Related Workmentioning

confidence: 99%

“…Constraint (27) guarantees that the sum of the probabilities equals to one. Constraint (28) assigns non-zero probability of selection to each network according to Definition 1.…”

Section: Overview Of the Off-policy Proceduresmentioning

confidence: 99%

Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

Bozorgchenani

Tarchi

Hossain

2022

IEEE Trans. on Mobile Comput.

Self Cite

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“…Mashhadia et al [153] propose an auction mechanism based on deep neural networks in order to maximize the profit of the edge servers, while satisfying the task processing delay and energy consumption constraints of MDs. In his respect, they design new penalty functions for the task computing delay and energy consumption constraints to guide the neural networks though feasible solutions.…”

Section: Auction-based Edge Resource Management and Pricingmentioning

confidence: 99%

A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Sharghivand¹,

Derakhshan

Siasi

2021

IEEE Access

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Driven by the vision of cloud and edge computing as the 5th utility, a major challenge is the efficient resources management and pricing taking into account incentives and preferences of cloud/edge providers, users, and the system. In recent years, auction mechanism design has gained wide attention as a tool for addressing this challenge. Here existing surveys lack detailed studies in this area, hence this work presents a comprehensive survey on the state-of-the-art auction-based mechanisms in the field of cloud/edge computing. First, the problem statements and background are presented for cloud/edge computing and auction theory. This is followed by a collaboration model on the benefits of auction mechanism designs to cloud/edge computing. Next, various auction mechanisms in cloud computing are reviewed based upon the following aspects: (1) direction of bids-forward, reverse, and two-sided auctions, (2) the heterogeneity of resources-combinatorial auctions, (3) the number and types of adopted attributes in winner determination-multi-attribute auctions, (4) the setting of auction-offline, online, or sequential auctions, and (5) the interoperability among cloud providers-inter-clouds. For each aspect, existing studies are summarized along with their saliencies and drawbacks. Moreover, existing auction mechanisms in the context of edge computing are also presented, followed by existing challenges and research directions.INDEX TERMS Auction mechanism design, cloud computing, edge computing, resource management, resource pricing.

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“…e proposed strategy had good performance in terms of energy consumption, load balancing, latency, and average execution time by using iFogSim and Google cluster trace. In [20], an optimal auction method for delay and energy constrained task offloading was proposed by a pair of deep neural networks. It maximized the profit of the edge servers while satisfied the task processing delay and energy consumption constraints of the mobile devices.…”

Section: Task Offloading In Mecmentioning

confidence: 99%

Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking

Pan

Wang

Jing-sheng

2021

Mobile Information Systems

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The body health plays an important metric in people’s everyday life, and it directly determines whether people have the ability to preferably contribute to the society. In fact, the physical training is a universal sport to enhance the body health. Therefore, the evaluation and analysis of physical training become particularly significant. With the rapid development and emerging of new techniques and networking paradigms, the traditional offline physical training evaluation and analysis cannot be performed well. Instead, this paper uses Mobile Edge Computing (MEC) and Software-Defined Networking (SDN) to implement the evaluation and analysis of physical training, shortened for MSPT, where MEC is the new computing technique and SDN is the new networking paradigm. The proposed MSPT includes two parts. At first, the physical training data from different mobile devices are migrated into the edge server for computing according to the current condition, in which the game theory is used to complete the task scheduling. Then, SDN is responsible for the global scheduling in the centralized control manner, in which the multigranularity scheduling strategy is used to handle the traffic between the SDN controller and edge computing server. The experiments are driven by OMNet, including three aspects of evaluation, i.e., task offloading of MEC, traffic scheduling of SDN, and performance analysis of physical training, and the results show that the proposed MSPT has better performance than the corresponding baselines.

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Optimal auction for delay and energy constrained task offloading in mobile edge computing

Cited by 35 publications

References 20 publications

Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking

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