Auction method to prevent bid-rigging strategies in mobile blockchain edge computing resource allocation

2021 ◽  
Author(s):  
Hao Qiu ◽  
Tong Li
Keyword(s):  
Resource Allocation ◽  
Edge Computing ◽  
Computing Resource ◽  
Bid Rigging

Service Characteristics-Oriented Joint Optimization of Radio and Computing Resource Allocation in Mobile-Edge Computing

2021 ◽  
Vol 8 (11) ◽  
pp. 9407-9421
Author(s):  
Jie Feng ◽  
Lei Liu ◽  
Qingqi Pei ◽  
Fen Hou ◽  
Tingting Yang ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Edge Computing ◽  
Joint Optimization ◽  
Mobile Edge Computing ◽  
Computing Resource ◽  
Service Characteristics

scholarly journals Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization

2020 ◽  
Vol 69 (3) ◽  
pp. 3424-3438 ◽  
Author(s):  
Mushu Li ◽  
Nan Cheng ◽  
Jie Gao ◽  
Yinlu Wang ◽  
Lian Zhao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Efficient ◽  
Trajectory Optimization ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computing Resource

Joint Task Assignment, Transmission, and Computing Resource Allocation in Multilayer Mobile Edge Computing Systems

2019 ◽  
Vol 6 (2) ◽  
pp. 2872-2884 ◽  
Author(s):  
Pengfei Wang ◽  
Chao Yao ◽  
Zijie Zheng ◽  
Guangyu Sun ◽  
Lingyang Song
Keyword(s):  
Resource Allocation ◽  
Task Assignment ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computing Systems ◽  
Computing Resource ◽  
Joint Task

Model-Based Comparison of Cloud-Edge Computing Resource Allocation Policies

2020 ◽  
Vol 63 (10) ◽  
pp. 1564-1583
Author(s):  
Lili Jiang ◽  
Xiaolin Chang ◽  
Runkai Yang ◽  
Jelena Mišić ◽  
Vojislav B Mišić
Keyword(s):  
Resource Allocation ◽  
Service Provision ◽  
Edge Computing ◽  
Analytical Models ◽  
Arrival Process ◽  
Related Services ◽  
Computing Resource ◽  
Mean Response Time ◽  
Rejection Probability ◽  
Modeling Techniques

Abstract The rapid and widespread adoption of internet of things-related services advances the development of the cloud-edge framework, including multiple cloud datacenters (CDCs) and edge micro-datacenters (EDCs). This paper aims to apply analytical modeling techniques to assess the effectiveness of cloud-edge computing resource allocation policies from the perspective of improving the performance of cloud-edge service. We focus on two types of physical device (PD)-allocation policies that define how to select a PD from a CDC/EDC for service provision. The first is randomly selecting a PD, denoted as RandAvail. The other is denoted as SEQ, in which an available idle PD is selected to serve client requests only after the waiting queues of all busy PDs are full. We first present the models in the case of an On–Off request arrival process and verify the approximate accuracy of the proposed models through simulations. Then, we apply analytical models for comparing RandAvail and SEQ policies, in terms of request rejection probability and mean response time, under various system parameter settings.

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