.png){kind=icon}
Return
A Multi-Objective Evolutionary Approach: Task Offloading and Resource Allocation Using Enhanced Decomposition-Based Algorithm in Mobile Edge Computing
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3444607.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the rapid growth of the mobile computing techniques, a wide variety of mobile edge computing (MEC) applications have emerged recently, aiming to provide computationally intensive and delay-sensitive network services. Through MEC, various complex tasks of mobile devices can be offloaded to the edge of network system for execution by edge servers, which greatly reduces the local computing burden. However, how to effectively allocate computational and communication resources in edge-cloud remains a challenging task, especially when multiple mobile users and edge servers are involved. In this paper, we propose a decomposition-based multi-objective optimization algorithm based on estimation-of-distribution models (MOEA/D-EoD) to deal with the task offloading and resource allocation problem in MEC. Especially, considering the features of multi-user and multi-server cloud-edge-end collaboration wireless MEC system, we construct a joint optimization model of task offloading and resource allocation, where limited communication and computational resource constraints are considered. To deal with the optimization model, we design an efficient decomposition-based algorithm, which incorporates two novel estimation-of-distribution models to deal with discrete and continuous decision variables of the problem. Experimental results obtained from benchmark test suites DTLZ and ZDT demonstrate that the proposed method exhibits significantly superior performance compared to other comparative algorithms. The proposed model and algorithm are simulated and tested on different test instances, and experimental results show the effectiveness and efficiency of our proposed method.
Keywords:
Task analysis
Resource management
Cloud computing
Servers
Computational modeling
Optimization
Mobile handsets
Multi-access edge computing
Mobile edge computing
task offloading
resource allocation
multi-objective optimization
estimation-of-distribution model
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
ShareSaveDiscovery of a Novel Cationic Surfactant: Tributyltetradecyl-Phosphonium Chloride for Iron Ore Flotation: From Prediction to Experimental Verification.png)
Minerals
OAAI
MineralsIF0
OAAIShareSaveMobile Edge Computing With Wireless Backhaul: Joint Task Offloading and Resource AllocationIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSaveShareSave
View more