Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks
By Tuyen X. Tran, Dario Pompili. IEEE Transactions on Vehicular Technology, 2017
803 citations recorded by the source corpus.
Mobile-edge computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this paper, an MEC enabled multi-cell wireless network is considered where each base station (BS) is equipped with a MEC server that assists mobile users in executing computation-intensive tasks via task offloading. The problem of joint task offloading and resource allocation is studied in order to maximize the users’ task offloading gains, which is measured by a weighted sum of reductions in task completion time and energy consumption. The considered problem is formulated as a mixed integer nonlinear program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the combinatorial nature of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, we propose to decompose the original problem into a resource allocation (RA) problem with fixed task offloading decision and a task offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users’ offloading utility over traditional approaches.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/0325677c16a638fc8e4756bf0822508818acf086
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
In Electrical and Electronic Engineering
Citations in the corpus
- Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network (2018, background): “Multi-server mobile edge computing is only considered in Tran and Pompili [38], but this work does not consider the ultra dense network, it is hard to obtain insights for the design of key parameters.”
- Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics (2020, background): “In [5], the authors addressed the resource allocation problem using convex and quasi-convex optimization techniques, and further proposed a novel heuristic algorithm to the task offloading problem, which can achieve a suboptimal solution in polynomial time.”
- Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks (2018, methodology): “For instance, meta-heuristic search algorithms are proposed in [7] and [12] to optimize the offloading decisions.”
- A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “in [306] explored the problem of server selection and resource allocation, where resources were subchannel used to offload computations to the target server, transmit power of mobile users, and computing resources at”
- Optimal Edge User Allocation in Edge Computing with Variable Sized Vector Bin Packing (2018, background): “In [11], the authors formulated a problem similar to the EUA problem but with different objectives, which are to reduce task completion time and energy consumption.”