Scalable Modulation based Computation Offloading in Vehicular Edge Computing System

2020 ◽  
Author(s):  
Wenjie Li ◽  
Ning Zhang ◽  
Qiuyan Liu ◽  
Weiyang Feng ◽  
Ruirui Ning ◽  
...  
Keyword(s):  
Computing System ◽  
Edge Computing ◽  
Computation Offloading

Two-tier end-edge collaborative computation offloading for edge computing

2021 ◽  
pp. 1-12
Author(s):  
Yong Xiao ◽  
Ling Wei ◽  
Junhao Feng ◽  
Wang En
Keyword(s):  
Computing System ◽  
End Users ◽  
Edge Computing ◽  
Computation Offloading ◽  
Iot Applications ◽  
Delay Sensitive ◽  
Task Processing ◽  
Queue Lengths ◽  
The Stability ◽  
The Cost

Edge computing has emerged for meeting the ever-increasing computation demands from delay-sensitive Internet of Things (IoT) applications. However, the computing capability of an edge device, including a computing-enabled end user and an edge server, is insufficient to support massive amounts of tasks generated from IoT applications. In this paper, we aim to propose a two-tier end-edge collaborative computation offloading policy to support as much as possible computation-intensive tasks while making the edge computing system strongly stable. We formulate the two-tier end-edge collaborative offloading problem with the objective of minimizing the task processing and offloading cost constrained to the stability of queue lengths of end users and edge servers. We perform analysis of the Lyapunov drift-plus-penalty properties of the problem. Then, a cost-aware computation offloading (CACO) algorithm is proposed to find out optimal two-tier offloading decisions so as to minimize the cost while making the edge computing system stable. Our simulation results show that the proposed CACO outperforms the benchmarked algorithms, especially under various number of end users and edge servers.

scholarly journals Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 779
Author(s):  
Shichao Chen ◽  
Qijie Li ◽  
Mengchu Zhou ◽  
Abdullah Abusorrah
Keyword(s):  
Problem Formulation ◽  
Computing System ◽  
High Energy ◽  
Edge Computing ◽  
Computation Offloading ◽  
Future Research ◽  
Research Issues ◽  
Computing Paradigm ◽  
Collaborative Scheduling ◽  
And Storage

In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge server, it can be overloaded, thereby resulting in the high processing delay of many computing tasks and unexpectedly high energy consumption. On the other hand, the resources in idle edge devices may be wasted and resource-rich cloud centers may be underutilized. Therefore, it is essential to explore a computing task collaborative scheduling mechanism with an edge server, a cloud center and edge devices according to task characteristics, optimization objectives and system status. It can help one realize efficient collaborative scheduling and precise execution of all computing tasks. This work analyzes and summarizes the edge computing scenarios in an edge computing paradigm. It then classifies the computing tasks in edge computing scenarios. Next, it formulates the optimization problem of computation offloading for an edge computing system. According to the problem formulation, the collaborative scheduling methods of computing tasks are then reviewed. Finally, future research issues for advanced collaborative scheduling in the context of edge computing are indicated.

scholarly journals Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tawfiq Hasanin ◽  
Aisha Alsobhi ◽  
Adil Khadidos ◽  
Ayman Qahmash ◽  
Alaa Khadidos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optimization Algorithm ◽  
Computing System ◽  
Edge Computing ◽  
Bald Eagle ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Cloud Data ◽  
Expected Time ◽  
Search Optimization

Mobile edge computing (MEC) is a paradigm novel computing that promises the dramatic effect of reduction in latency and consumption of energy by computation offloading intensive; these tasks to the edge clouds in proximity close to the smart mobile users. In this research, reduce the offloading and latency between the edge computing and multiusers under the environment IoT application in 5G using bald eagle search optimization algorithm. The deep learning approach may consume high computational complexity and more time. In an edge computing system, devices can offload their computation-intensive tasks to the edge servers to save energy and shorten their latency. The bald eagle algorithm (BES) is the advanced optimization algorithm that resembles the strategy of eagle hunting. The strategies are select, search, and swooping stages. Previously, the BES algorithm is used to consume the energy and distance; to improve the better energy and reduce the offloading latency in this research and some delays occur when devices increase causes demand for cloud data, it can be improved by offering ROS (resource) estimation. To enhance the BES algorithm that introduces the ROS estimation stage to select the better ROSs, an edge system, which offloads the most appropriate IoT subtasks to edge servers then the expected time of execution, got minimized. Based on multiuser offloading, we proposed a bald eagle search optimization algorithm that can effectively reduce the end-end time to get fast and near-optimal IoT devices. The latency is reduced from the cloud to the local; this can be overcome by using edge computing, and deep learning expects faster and better results from the network. This can be proposed by BES algorithm technique that is better than other conventional methods that are compared on results to minimize the offloading latency. Then, the simulation is done to show the efficiency and stability by reducing the offloading latency.

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