Breakout Local Search Solution to the Offloading Decision Problem in a Multi-Access Edge Computing Cloud-Enabled Network

Cloud offloading is an important technique for Internet of Things systems, as it allows devices with limited capabilities to access the powerful resources in the cloud when executing their applications. However, relying solely on the remote cloud is problematic, as the long access time from the far...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computing Vol. 13; no. 3; pp. 1328 - 1338
Main Authors: Kato, Mina, Rodrigues, Tiago Koketsu, Kato, Nei
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Access time
Binary integer linear programming
breakout local search
Cloud computing
Computational modeling
Delays
Devices
Edge computing
Integer linear programming
Integer programming
Internet of Things
Linear programming
Mathematical models
Mobile computing
Multi-access edge computing
Optimization
Real time
Real-time systems
Search problems
Searching
Servers
ISSN:2168-6750, 2168-6750
Online Access:Get full text
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Summary:Cloud offloading is an important technique for Internet of Things systems, as it allows devices with limited capabilities to access the powerful resources in the cloud when executing their applications. However, relying solely on the remote cloud is problematic, as the long access time from the far distance to the server makes real-time applications impossible to be executed. Multi-access edge computing addresses this by deploying cloud servers near the devices. The issue then becomes how to allocate devices between either remote cloud and multi-access edge computing, based on the device requirements. In this paper, we propose a Breakout Local Search-based solution that, given our designed binary integer linear programming model of the offloading problem, finds a near-optimal configuration for allocating devices between the two cloud types. The proposal is based on iterating between exploiting the local optimum found so far and perturbation of the current solution to explore more the search space. A comparison study shows that our proposal is better than baseline and conventional algorithms, speeding up the total service delay of tasks by at least 30 ms.
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ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2025.3598369