Deadline-Aware Task Offloading With Concurrency in Serverless Edge Computing

Minh-Tuong Nguyen

doi:10.1109/JIOT.2026.3665108

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Deadline-Aware Task Offloading With Concurrency in Serverless Edge Computing

2026-02-16

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DOI：10.1109/JIOT.2026.3665108

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Abstract

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Serverless edge computing enables low-latency Internet of Things (IoT) services but faces scalability challenges due to complex concurrency and resource management. While existing approaches address function initialization and edge–cloud offloading, they often overlook the joint optimization of serverless concurrency and physical-layer resources, leading to potential service degradation and increased costs. To tackle this, we propose OPLA, a novel cross-layer framework for joint latency and concurrency optimization, designed to minimize end-to-end latency while optimizing concurrent serverless functions. OPLA models interactions between physical-layer resources (e.g., bandwidth, transmission power, offloading ratios) and application-layer concurrency decisions. The formulated problem is a highly nonconvex mixed-integer nonlinear program (MINLP), which we prove to be at least NP-complete in certain cases. To approximate its optimal solution efficiently, we propose an iterative exploration–exploitation procedure (EEP). The exploration phase, which is embarrassingly parallelizable, balances solution quality and efficiency with single-parameter tuning. The exploitation phase is just a simple successive convex approximation (SCA) to OPLA. Moreover, we also develop a presolve–postsolve heuristic with deterministic rounding to ensure feasibility for OPLA. Numerical results demonstrate that EEP consistently achieves solutions within a 6% optimality gap relative to a global solver across a wide range of network scales and workloads, confirming its effectiveness and scalability for real-world serverless edge deployments.

Keywords:

Exploration–exploitation

serverless edge computing

serverless function

task offloading