scholarly journals Multilevel Task Offloading and Resource Optimization of Edge Computing Networks Considering UAV Relay and Green Energy

2020 ◽  
Vol 10 (7) ◽  
pp. 2592
Author(s):  
Zhixiong Chen ◽  
Nan Xiao ◽  
Dongsheng Han
Keyword(s):  
Utility Function ◽  
Resource Utilization ◽  
Energy State ◽  
Low Cost ◽  
Initial Energy ◽  
Green Energy ◽  
Edge Computing ◽  
Resource Optimization ◽  
Network Resource ◽  
Markov Decision

Unmanned aerial vehicle (UAV)-assisted relay mobile edge computing (MEC) network is a prominent concept, where network deployment is flexible and network coverage is wide. In scenarios such as emergency communications and low-cost coverage, optimization of offloading methods and resource utilization are important ways to improve system effectiveness due to limited terminal and UAV energy and hardware equipment. A multilevel edge computing network resource optimization model on the basis of UAV fusion that provides relay forwarding and offload services is established by considering the initial energy state of the UAV, the green energy charging function, and the reliability of computing offload. With normalized system utility function maximization as the goal, a Markov decision process algorithm meets the needs of the practical application scene and provides a flexible and effective unloading mode. This algorithm is adopted to solve the optimal offloading mode and the optimal resource utilization scheme. Simulations verify the effectiveness and reliability of the proposed multilevel offloading model. The proposed model can optimize system resource allocation and effectively improve the utility function and user experience of computing offloading systems.

Selective Offloading Network Resource Optimization Approaches in Collaborative Cloud-Edge Computing Networks

2021 ◽  
Author(s):  
Ling Liu ◽  
Ruixin Liang ◽  
Shoucui Wang ◽  
Hong Chen ◽  
Mingyi Gao ◽  
...  
Keyword(s):  
Edge Computing ◽  
Resource Optimization ◽  
Network Resource

scholarly journals Dynamic Task Migration Combining Energy Efficiency and Load Balancing Optimization in Three-Tier UAV-Enabled Mobile Edge Computing System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 190
Author(s):  
Wu Ouyang ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Genghua Yu ◽  
Heng Zhang
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Markov Decision Process ◽  
Decision Process ◽  
Base Station ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Migration ◽  
Migration Strategy ◽  
Markov Decision

As transportation becomes more convenient and efficient, users move faster and faster. When a user leaves the service range of the original edge server, the original edge server needs to migrate the tasks offloaded by the user to other edge servers. An effective task migration strategy needs to fully consider the location of users, the load status of edge servers, and energy consumption, which make designing an effective task migration strategy a challenge. In this paper, we innovatively proposed a mobile edge computing (MEC) system architecture consisting of multiple smart mobile devices (SMDs), multiple unmanned aerial vehicle (UAV), and a base station (BS). Moreover, we establish the model of the Markov decision process with unknown rewards (MDPUR) based on the traditional Markov decision process (MDP), which comprehensively considers the three aspects of the migration distance, the residual energy status of the UAVs, and the load status of the UAVs. Based on the MDPUR model, we propose a advantage-based value iteration (ABVI) algorithm to obtain the effective task migration strategy, which can help the UAV group to achieve load balancing and reduce the total energy consumption of the UAV group under the premise of ensuring user service quality. Finally, the results of simulation experiments show that the ABVI algorithm is effective. In particular, the ABVI algorithm has better performance than the traditional value iterative algorithm. And in a dynamic environment, the ABVI algorithm is also very robust.

A Optimization Traffic Grooming Algorithm Based on Bidirectional SONET Ring Topology Network

2015 ◽  
Vol 727-728 ◽  
pp. 996-999 ◽  
Author(s):  
Su Xia Cui
Keyword(s):  
Resource Utilization ◽  
Network Performance ◽  
Minimum Weight ◽  
Routing Algorithm ◽  
Traffic Grooming ◽  
Network Routing ◽  
Ring Network ◽  
Network Resource ◽  
Blocking Rate ◽  
The Impact

The issue of WDM network traffic grooming has been a hot in the field of research. The implementation of traffic grooming technology can improve the utilization of wavelength channels, reducing the link delay and the blocking rate of the network, which to improve network resource utilization and optimize network performance. This article mainly studies all-optical network routing algorithm utilizing WDM technology to achieve the dynamic traffic grooming and propose a optimization grooming policy -HaffmanGroom (M) algorithms which based on SONET / WDM ring network. The most important feature of this algorithm is that the SONET / WDM ring network of multiple multicast request packet , with a minimum weight of the light path priority selection method, the flow of requests each group effectively optimize ease . The algorithm takes into account the impact of the link request factor and link hops to optimize the link selection. The simulation results show that under the conditions of factors and the number of hop a request fully consider the impact of these two factors to the link, and can achieve optimal link with the smallest weights for effective data transmission, improving resource utilization, reducing blocking rate in order to achieve the purpose of optimizing network performance.

scholarly journals A climate investment trap in developing economies

2021 ◽  
Author(s):  
Nadia Ameli ◽  
Olivier Dessens ◽  
Matthew Winning ◽  
Jennifer Cronin ◽  
Hugues Chenet ◽  
...  
Keyword(s):  
Cost Of Capital ◽  
Developing Economies ◽  
Low Cost ◽  
Energy Transition ◽  
Green Energy ◽  
Electricity Production ◽  
Green Electricity ◽  
Net Zero ◽  
Sustainable Finance ◽  
The Cost

Abstract Finance is vital for the green energy transition, but the access to low cost finance is uneven as the cost of capital differs substantially between regions. This study shows how modelled decarbonisation pathways of developing economies are disproportionately impacted by assumptions around their cost of capital (WACC). For example, representing regionally specific WACC values indicates 35% lower green electricity production in Africa for a cost-optimal 2°C pathway. Moreover, results show that early convergence of WACC values for green and brown technologies in 2050 would allow Africa to reach net-zero emissions approximately 10 years earlier than when convergence is not considered. A “climate investment trap” arises for developing economies when climate-related investments remain chronically insufficient. Elements of sustainable finance frameworks currently present barriers to these finance flows and radical changes are needed so that capital is better allocated to the regions that most need it.

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