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Combinational Auction-Based Service Provider Selection in Mobile Edge Computing Networks

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TLDR
A multi-round-sealed sequential combinatorial auction mechanism is proposed and the properties of the auction are proved and various simulation results are done to show that the proposed approach has better system performance compared with the existing algorithms.
Abstract
Via processing the computation intensive applications (apps) at the network edge, mobile edge computing (MEC) becomes a promising technology to enhance the ability of the user equipments (UEs). Most existing works usually focus on whether to offload or where to offload the apps under the premise that sufficient resources are owned by the network edge. However, the demand heterogeneity of UEs and the limitation of resources are usually failed to be considered. Since the limited resources may constrain the number of accessed UEs, how the MEC service providers (SPs) choose the UEs to serve while ensuring UEs’ Quality of Service (QoS) is a key issue. Under this context, in this paper, we study the matching problem between the MEC SPs and the UEs in a multi-MEC and multi-UE scenario. Within this scenario, MEC SPs are equipped with limited wireless and computational resources. Auction theory is utilized to model the matching relationship between MEC SPs and UEs as the commodity trading. With this trading, UEs can obtain MEC service from SPs, when they successfully purchase the combinational resources (including computational and wireless resources) from SPs. To complete the auction process, a multi-round-sealed sequential combinatorial auction mechanism is proposed. The properties of the auction are proved and various simulation results are done to show that the proposed approach has better system performance compared with the existing algorithms.

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Citations
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Journal ArticleDOI

Joint Computation Offloading and Resource Allocation Optimization in Heterogeneous Networks With Mobile Edge Computing

TL;DR: Simulation results show that the distributed JCORAO scheme can effectively decrease the energy consumption and task completion time with lower complexity.
Journal ArticleDOI

Energy Efficient Secure Computation Offloading in NOMA-Based mMTC Networks for IoT

TL;DR: This work forms the joint computation and communication resource allocation algorithm for secure computation offloading in NOMA-based mMTC networks for IoT, where the relay equipped with an MEC server and a passive malicious eavesdropper are presented.
Journal ArticleDOI

Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint

TL;DR: Simulation results are presented to demonstrate the effectiveness of the proposed adaptive service offloading scheme over other existing state-of-the-art solutions, in terms of service latency, utility value, revenue, and utilization.
Journal ArticleDOI

An Online Incentive Mechanism for Collaborative Task Offloading in Mobile Edge Computing

TL;DR: A novel online mechanism based on the primal-dual optimization framework for collaborative task offloading in mobile edge computing can guarantee feasibility, truthfulness, and computational efficiency, andoretical analyses show.
Journal ArticleDOI

A review on the computation offloading approaches in mobile edge computing: A game-theoretic perspective

TL;DR: This article provides a systematic literature review on the GT‐based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state‐of‐the‐art mechanisms on this important topic and to provide open issues as well.
References
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Journal ArticleDOI

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

TL;DR: In this article, a game theoretic approach for computation offloading in a distributed manner was adopted to solve the multi-user offloading problem in a multi-channel wireless interference environment.
Journal ArticleDOI

Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?

TL;DR: The cloud heralds a new era of computing where application services are provided through the Internet, but is it the ultimate solution for extending such systems' battery lifetimes?
Journal ArticleDOI

Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices

TL;DR: In this paper, a low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computing offloading.
Posted Content

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

TL;DR: This paper designs a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics.
Journal ArticleDOI

Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading

TL;DR: This paper studies resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-divisionmultiple access (OFDMA), for which the optimal resource allocation is formulated as a mixed-integer problem.
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