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Task Offloading Scheduling for Mobile Edge Computing Networks With Incomplete Edge Information
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DOI：10.1109/JIOT.2026.3676785.png)
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Abstract
En 中文
Mobile edge computing (MEC) networks have attracted significant attention for enabling users to offload computation-intensive tasks to edge servers. Task offloading scheduling is a critical challenge, especially when complete information about tasks and edge servers is only partially accessible in practice. In general, each edge server can only obtain its own information but has no access to the complete real-time information of other edge servers, resulting in information incompleteness. To address this issue, this article investigates the problem of energy minimization through offloading with incomplete edge information (EMO-IEI). Specifically, to address the uncertainty of real-time computing resources caused by incomplete edge information (IEI), we adopt the exact convex regularization (ECR) method to estimate resource availability based on known expectations and variances. Utilizing these estimations, we reformulate the problem as a collapsing multiknapsack problem and propose the GAP-ESM algorithm for efficient solution. Theoretical analysis validate that the GAP-ESM algorithm achieves an approximation ratio of $(1+\kappa /(\kappa - \rho \kappa - \rho))$ , where $\kappa $ is system parameter associated with the energy requirements of computing tasks, and $\rho $ is a tunable design parameter balancing approximation quality and computational complexity. Extensive simulations demonstrate that the proposed GAP-ESM algorithm outperforms baseline schemes in terms of overall energy consumption and task completion rate.
Keywords:
Incomplete edge information (IEI)
mobile edge computing (MEC)
task offloading scheduling
Journal
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8.9
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1.4W
Citations:
7.8W