.png){kind=icon}
.png){kind=icon}
Return
ODM-BCSA: An Offloading Decision-Making Framework based on Binary Cuckoo Search Algorithm for Mobile Edge Computing
.png){kind=icon}
OA
AI
DOI：10.1016/j.comnet.2023.109647.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Computational task offloading facilitates real-time applications on constrained mobile devices that require a large amount of processing resources, high storage capacity and battery power. Mobile Edge Computing (MEC) is a computing paradigm that shifts computing resources closer to the user at the network's edges. Heavy tasks are then offloaded to edge nodes, thereby reducing the computations required on the mobile side. However, offloading computational tasks may result in additional energy consumption and delays, due to network congestion and time spent in server queues. Thus, to minimize completion time and energy consumption, it is essential to optimize offloading decisions, while also considering the financial costs. In this paper, we propose an Offloading Decision-Making Framework based on the Binary Cuckoo Search Algorithm for Mobile Edge Computing (ODM-BCSA). We formulated an offloading problem as a mixed-integer optimization problem to minimize time, energy, and payment costs. We resolved the problem of resource allocation using the Binary Cuckoo Search Algorithm (BCSA). The simulation results revealed that offloading decisions depend on multiple parameters, including the number of mobile devices being handled by the edge server, the bandwidth, and the number of tasks. Decisions made also depended on the priority assigned to each objective. Finally, we compared the ODM-BCSA against a brute force search, proving that the ODM-BCSA is more efficient and greatly minimizes execution time when a huge number of mobile devices are involved (by 99.9 %).
Keywords:
Offloading optimization
Mobile Edge Computing
Swarm intelligence
Binary cuckoo search
Task offloading
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.6
Papers:
1.5K
Citations:
1.6W