An evolutionary fuzzy scheduler for multi-objective resource allocation in fog computing

2021 ◽  
Vol 117 ◽  
pp. 498-509
Author(s):  
Chu-ge Wu ◽  
Wei Li ◽  
Ling Wang ◽  
Albert Y. Zomaya
Keyword(s):  
Resource Allocation ◽  
Fog Computing ◽  
Multi Objective

scholarly journals Multi-Objective Resource Allocation for IRS-Aided SWIPT

2021 ◽  
pp. 1-1
Author(s):  
Ata Khalili ◽  
Shayan Zargari ◽  
Qingqing Wu ◽  
Derrick Wing Kwan Ng ◽  
Rui Zhang
Keyword(s):  
Resource Allocation ◽  
Multi Objective

scholarly journals Method for Dynamic Service Orchestration in Fog Computing

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1796
Author(s):  
Nerijus Morkevicius ◽  
Algimantas Venčkauskas ◽  
Nerijus Šatkauskas ◽  
Jevgenijus Toldinas
Keyword(s):  
Fog Computing ◽  
Optimization Method ◽  
Service Orchestration ◽  
Multi Objective ◽  
System A ◽  
Service Distribution ◽  
Judgment Matrix ◽  
Hierarchy Process ◽  
Application Specific ◽  
Constrained Devices

Fog computing is meant to deal with the problems which cloud computing cannot solve alone. As the fog is closer to a user, it can improve some very important QoS characteristics, such as a latency and availability. One of the challenges in the fog architecture is heterogeneous constrained devices and the dynamic nature of the end devices, which requires a dynamic service orchestration to provide an efficient service placement inside the fog nodes. An optimization method is needed to ensure the required level of QoS while requiring minimal resources from fog and end devices, thus ensuring the longest lifecycle of the whole IoT system. A two-stage multi-objective optimization method to find the best placement of services among available fog nodes is presented in this paper. A Pareto set of non-dominated possible service distributions is found using the integer multi-objective particle swarm optimization method. Then, the analytical hierarchy process is used to choose the best service distribution according to the application-specific judgment matrix. An illustrative scenario with experimental results is presented to demonstrate characteristics of the proposed method.

scholarly journals Fog Computing-Assisted Energy-Efficient Resource Allocation for High-Mobility MIMO-OFDMA Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Lingyun Lu ◽  
Tian Wang ◽  
Wei Ni ◽  
Kai Li ◽  
Bo Gao
Keyword(s):  
Resource Allocation ◽  
Energy Efficient ◽  
High Speed ◽  
Optimization Problem ◽  
Fog Computing ◽  
High Mobility ◽  
High Speed Train ◽  
Efficient Resource ◽  
Energy Efficient Resource Allocation ◽  
System Configurations

This paper presents a suboptimal approach for resource allocation of massive MIMO-OFDMA systems for high-speed train (HST) applications. An optimization problem is formulated to alleviate the severe Doppler effect and maximize the energy efficiency (EE) of the system. We propose to decouple the problem between the allocations of antennas, subcarriers, and transmit powers and solve the problem by carrying out the allocations separately and iteratively in an alternating manner. Fast convergence can be achieved for the proposed approach within only several iterations. Simulation results show that the proposed algorithm is superior to existing techniques in terms of system EE and throughput in different system configurations of HST applications.

scholarly journals Multi-objective Resource Allocation for D2D and Enabled MC-NOMA Networks by Tchebycheff Method

2021 ◽  
pp. 1-1
Author(s):  
Siavash Bayat ◽  
Ata Khalili ◽  
Shayan Zargari ◽  
Mohammad Robat Mili ◽  
Zhu Han
Keyword(s):  
Resource Allocation ◽  
Multi Objective

Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets

2017 ◽  
Vol 4 (5) ◽  
pp. 1216-1228 ◽  
Author(s):  
Lina Ni ◽  
Jinquan Zhang ◽  
Changjun Jiang ◽  
Chungang Yan ◽  
Kan Yu
Keyword(s):  
Resource Allocation ◽  
Petri Nets ◽  
Fog Computing ◽  
Allocation Strategy ◽  
Timed Petri Nets ◽  
Resource Allocation Strategy

scholarly journals Mobile Fog Computing-Assisted Resource Allocation for Two-Hop SWIPT OFDM Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaofei Di ◽  
Yu Zhang ◽  
Tong Liu ◽  
Shaoli Kang ◽  
Yue Zhao
Keyword(s):  
Resource Allocation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Fog Computing ◽  
Achievable Rate ◽  
Fast Time ◽  
Power Splitting ◽  
Frequency Division ◽  
Decode And Forward ◽  
Subcarrier Pairing ◽  
Receiver Architecture

The mobile fog computing-assisted resource allocation (RA) is studied for simultaneous wireless information and power transfer (SWIPT) two-hop orthogonal frequency division multiplexing (OFDM) networks, where a decode-and-forward (DF) relay first harvests energy from signals emitted by a source and then helps the source to forward information to its destination by using the harvested energy. Power splitting (PS) strategy is adopted at the relay and a different PS (DPS) receiver architecture is proposed, where the PS factors of all subcarriers are different. A RA problem is formulated to maximize the system’s achievable rate by jointly optimizing subcarrier pairing, power allocation, and PS factors. Since the RA problem is a nonconvex problem and is difficult to solve, an efficient RA algorithm is designed. As the wireless channels are fast time-varying, the computation is performed in mobile fog node close to end nodes, instead of remote clouds. Results demonstrate that the achievable rate is significantly increased by using the proposed RA algorithm. It is also found that the computation complexity of RA algorithm of DPS receiver architecture is much lower than the existing identical PS (IPS) receiver architecture, and thus the proposed DPS architecture is more suitable for computation-constrained fog system.

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