Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing

By Stefania Sardellitti, Gesualdo Scutari, Sergio Barbarossa. IEEE Transactions on Signal and Information Processing over Networks, 2014

877 citations recorded by the source corpus.

Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources-the transmit precoding matrices of the MUs-and the computational resources-the CPU cycles/second assigned by the cloud to each MU-in order to minimize the overall users' energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/edcc427da2213913b010c9de56a607514bf563ab

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Citations in the corpus

UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization (2018, methodology): “A multicell MEC system was studied in [5], where the total energy consumption was minimized by jointly optimizing the radio and computational resources.”
Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling (2017, methodology): “[13] jointly optimized the users’ transmit precoding matrices and the cloud’s computational resource to minimize the users’ energy consumption.”
Multiobjective Optimization for Computation Offloading in Fog Computing (2018, methodology): “Sardellitti et al. [19] formulated the ofﬂoading problem as the joint optimization of the radio resources (the transmit precoding matrices of the mobile users) and the computational resources (the CPU cycles/s assigned by the fog to each mobile user), in order to minimize the overall users’ energy…”
D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration (2016, background): “[10] consider a task offloading problem as the joint optimization of the radio resources and the computational resources in order to minimize the overall users’ energy consumption.”
A Survey on the Edge Computing for the Internet of Things (2018, background): “As applied to cloud and edge computing [77], [103], and various other areas of resource management, optimization has shown increasing promise, and is a contender that complements auction schemes.”
Computation Offloading Toward Edge Computing (2019, background): “[111] exploited cellular base stations to provide the capability of edge computing and studied the joint optimization of computational and radio resources.”
6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications (2020, background): “Hence, it makes sense to optimize the use of computation and communication resources jointly , as suggested for example in [51], in a static multi-user setup, where multiple small cells are served by a single edge computing host.”
Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing (2020, background): “There is a rich literature on resource management in MEC that aims at optimizing system latency [11]–[14], energy consumption [15]–[17], and overall cost of system latency and/or energy consumption [18]–[21].”
Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks (2017, background): “In the context of MEC, the problem of joint task offloading and resource allocation was studied in a single-user system with energy harvesting devices [29], and in a multi-cell multi-user systems [13]; however the congestion of computing resources at the MEC server was omitted.”
Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling (2016, methodology): “On the other hand, computation offloading schemes are studied in [9]–[17].”
Survey on Multi-Access Edge Computing for Internet of Things Realization (2018, background): “The game theoretic approach is also designed for selecting the most appropriate wireless channels to transmit offloading data in a multi-user multi-channel MEC systems [130], [131].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “In [208], the authors investigated a joint problem of radio and computational resources to minimize the total energy consumption of all mobile users under transmit power budget, latency, and maximum computing capability constraints.”
Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee (2018, methodology): “Similar to the existing works [6], [9], [10], [13], [20], [22], [23], to enable tractable analysis, we consider a quasistatic scenario where all UEs and the wireless network remain stationary during an offloading period (usually within several seconds [10]).”
Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds (2017, methodology): “where W (Hz) is the user bandwidth, and pi denotes the transmission power, which can be configured by the mobile user subject to a maximum transmission power constraint [11], [12], [15], [16].”
A survey on mobile edge computing (2016, background): “Beck et al. proposed Mobile Edge Computing enabled Voice over LTE (ME-VoLTE) architecture to reduce battery consumption of mobile devices during video call and provide a communication protocol for negotiating the offloading strategy[27].”
Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks (2016, background): “The authors in [14] formulated the task offloading problem as a joint optimization of the radio resources together with the computational resources, and proposed an iterative algorithm to solve the problem.”
Price-Based Distributed Offloading for Mobile-Edge Computing With Computation Capacity Constraints (2017, background): “2780128 categories: binary offloading [5]–[9] and partial offload-”