scholarly journals A ROBUST CALCULUS OFFLOADING STRUCTURE FOR AMBULANT HEALTH INQUIRIES

2021 ◽  
pp. 144-151
Author(s):  
Pitta Rebecca Alekhya ◽  
K. Tulasi Krishna Kumar Nainar
Keyword(s):  
Resource Utilization ◽  
Mobile Health ◽  
Completion Time ◽  
Edge Computing ◽  
Computation Offloading ◽  
Utilization Rate ◽  
Task Completion ◽  
Base Line ◽  
Internet Of Vehicles ◽  
Multi Access

Recently, research intergrading medicine and Artificial Intelligence has attracted extensive attention. Mobile health has emerged as a promising paradigm for improving people’s work and life in the future. However, high mobility of mobile devices and limited resources pose challenges for users to deal with the applications in mobile health that require large amount of computational resources. In this paper, a novel computation offloading mechanism is proposed in the environments combining of the Internet of Vehicles and Multi-Access Edge Computing. Through the proposed mechanism, mobile health applications are divided into several parts and can be offloaded to appropriate nearby vehicles while meeting the requirements of application completion time, energy consumption, and resource utilization. A particle swarm optimization based approach is proposed to optimize the aforementioned computation offloading problem in a specific medical application. Evaluations of the proposed algorithms against local computing method serves as base line method are conducted via extensive simulations. The average task completion time saved by our proposed task allocation scheme increases continually compared with the local solution. Specially, the global resource utilization rate increased from 71.8% to 94.5% compared with the local execution time. KEY WORDS: Computation Offloading, Mobile Health, Internet of Vehicles, Multi-Access Edge Computing.

Capsule Network Distributed Learning with Multi-Access Edge Computing for the Internet of Vehicles

2021 ◽  
Vol 59 (8) ◽  
pp. 52-57
Author(s):  
Jie Xu ◽  
F. Richard Yu ◽  
Jingyu Wang ◽  
Qi Qi ◽  
Haifeng Sun ◽  
...  
Keyword(s):  
Distributed Learning ◽  
Edge Computing ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Multi Access

scholarly journals Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

2019 ◽  
Vol 10 (1) ◽  
pp. 203 ◽  
Author(s):  
Luan N. T. Huynh ◽  
Quoc-Viet Pham ◽  
Xuan-Qui Pham ◽  
Tri D. T. Nguyen ◽  
Md Delowar Hossain ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
5G Networks ◽  
Swarm Optimization ◽  
Multi Access

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

Coalitional Games for Computation Offloading in NOMA-Enabled Multi-Access Edge Computing

2020 ◽  
Vol 69 (2) ◽  
pp. 1982-1993 ◽  
Author(s):  
Quoc-Viet Pham ◽  
Hoang T. Nguyen ◽  
Zhu Han ◽  
Won-Joo Hwang
Keyword(s):  
Edge Computing ◽  
Computation Offloading ◽  
Coalitional Games ◽  
Multi Access

A Prolific Scheme for Load Balancing Relying on Task Completion Time

2018 ◽  
Vol 8 (3) ◽  
pp. 1741
Author(s):  
V. Anand ◽  
K. Anuradha
Keyword(s):  
Load Balancing ◽  
Uniform Distribution ◽  
Resource Utilization ◽  
Completion Time ◽  
Computation Time ◽  
Task Completion ◽  
The Internet ◽  
Task Completion Time ◽  
Network Load ◽  
Computation Load

In networks with lot of computation, load balancing gains increasing significance. To offer various resources, services and applications, the ultimate aim is to facilitate the sharing of services and resources on the network over the Internet. A key issue to be focused and addressed in networks with large amount of computation is load balancing. Load is the number of tasks‘t’ performed by a computation system. The load can be categorized as network load and CPU load. For an efficient load balancing strategy, the process of assigning the load between the nodes should enhance the resource utilization and minimize the computation time. This can be accomplished by a uniform distribution of load of to all the nodes. A Load balancing method should guarantee that, each node in a network performs almost equal amount of work pertinent to their capacity and availability of resources. Relying on task subtraction, this work has presented a pioneering algorithm termed as E-TS (Efficient-Task Subtraction). This algorithm has selected appropriate nodes for each task. The proposed algorithm has improved the utilization of computing resources and has preserved the neutrality in assigning the load to the nodes in the network.

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