scholarly journals Optimal Pricing-Based Edge Computing Resource Management in Mobile Blockchain

2018 ◽  
Author(s):  
Zehui Xiong ◽  
Shaohan Feng ◽  
Dusit Niyato ◽  
Ping Wang ◽  
Zhu Han
Keyword(s):  
Resource Management ◽  
Edge Computing ◽  
Optimal Pricing ◽  
Computing Resource

scholarly journals Optimization Strategy of Task Offloading with Wireless and Computing Resource Management in Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xintao Wu ◽  
Jie Gan ◽  
Shiyong Chen ◽  
Xu Zhao ◽  
Yucheng Wu
Keyword(s):  
Resource Management ◽  
Optimal Solution ◽  
Edge Computing ◽  
Base Stations ◽  
Mobile Edge Computing ◽  
Optimization Strategy ◽  
Resource Allocation Algorithm ◽  
Computing Resource ◽  
Task Offloading

Mobile edge computing (MEC) provides user equipment (UE) with computing capability through wireless networks to improve the quality of experience (QoE). The scenario with multiple base stations and multiple mobile users is modeled and analyzed. The optimization strategy of task offloading with wireless and computing resource management (TOWCRM) in mobile edge computing is considered. A resource allocation algorithm based on an improved graph coloring method is used to allocate wireless resource blocks (RBs). The optimal solution of computing resource is obtained by using KKT conditions. To improve the system utility, a semi-distributed TOWCRM strategy is proposed to obtain the task offloading decision. Theoretical simulations under different system parameters are executed, and the proposed semi-distributed TOWCRM strategy can be completed with finite iterations. Simulation results have verified the effectiveness of the proposed algorithm.

scholarly journals Energy-aware Resource Management for Federated Learning in Multi-access Edge Computing Systems

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chit Wutyee Zaw ◽  
Shashi Raj Pandey ◽  
Kitae Kim ◽  
Choong Seon Hong
Keyword(s):  
Resource Management ◽  
Edge Computing ◽  
Energy Aware ◽  
Computing Systems ◽  
Multi Access

A fuzzy-based approach for resource management in SDN-VANETs: Effect of trustworthiness on assessment of available edge computing resources

2021 ◽  
pp. 1-12
Author(s):  
Ermioni Qafzezi ◽  
Kevin Bylykbashi ◽  
Phudit Ampririt ◽  
Makoto Ikeda ◽  
Keita Matsuo ◽  
...  
Keyword(s):  
Resource Management ◽  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Distributed Processing ◽  
Transportation Systems ◽  
Edge Computing ◽  
Processing Capability ◽  
Hoc Networks ◽  
Available Resources

Vehicular Ad hoc Networks (VANETs) aim to improve the efficiency and safety of transportation systems by enabling communication between vehicles and roadside units, without relying on a central infrastructure. However, since there is a tremendous amount of data and significant number of resources to be dealt with, data and resource management become their major issues. Cloud, Fog and Edge computing, together with Software Defined Networking (SDN) are anticipated to provide flexibility, scalability and intelligence in VANETs while leveraging distributed processing environment. In this paper, we consider this architecture and implement and compare two Fuzzy-based Systems for Assessment of Neighboring Vehicles Processing Capability (FS-ANVPC1 and FS-ANVPC2) to determine the processing capability of neighboring vehicles in Software Defined Vehicular Ad hoc Networks (SDN-VANETs). The computational, networking and storage resources of vehicles comprise the Edge Computing resources in a layered Cloud-Fog-Edge architecture. A vehicle which needs additional resources to complete certain tasks and process various data can use the resources of the neighboring vehicles if the requirements to realize such operations are fulfilled. The proposed systems are used to assess the processing capability of each neighboring vehicle and based on the final value, it can be determined whether the edge layer can be used by the vehicles in need. FS-ANVPC1 takes into consideration the available resources of the neighboring vehicles and the predicted contact duration between them and the present vehicle, while FS-ANVPC2 includes in addition the vehicles trustworthiness value. Our systems take also into account the neighboring vehicles’ willingness to share their resources and determine the processing capability for each neighbor. We evaluate the proposed systems by computer simulations. The evaluation results show that FS-ANVPC1 decides that helpful neighboring vehicles are the ones that are predicted to be within the vehicle communication range for a while and have medium/large amount of available resources. FS-ANVPC2 considers the same neighboring vehicles as helpful neighbors only if they have at least a moderate trustworthiness value ( VT = 0.5). When VT is higher, FS-ANVPC2 takes into consideration also neighbors with less available resources.

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