Characterizing Effective Computing Capacity and Online Task Assignment in Heterogeneous Oilfield Cloud–Edge Systems
Year: 2026
Type: article
Abstract: Online allocation of oilfield analytics depends on which resources can execute each task and when those resources are available. We develop Effective-Capacity-aware Lyapunov Online Assignment (EC-LyOA), which links task-specific execution eligibility to a state-conditioned workload capacity region that accounts jointly for computation and shared communication demands. A base controller (EC-LyOA-B) prices these demands through resource-accounting debts; under bounded independent slot observations and strict resource slack, it achieves accounting-queue stability with an O(1/V) cost gap and an O(V) debt bound, and an explicit rejection option limits the long-run rejection rate to O(1/V) inside the capacity region. A guarded implementation (EC-LyOA-G) adds runtime memory and deadline reservations and is evaluated empirically. In trace-driven experiments, task releases are constructed from Petrobras 3W oil-well records, while execution and communication follow parameterized profiles. Concentrating demand in restricted service states reduces effective capacity, and an independence approximation overestimates capacity by 87.6% in the evaluated case. In a medium deployment, EC-LyOA-G operating points obtained by varying the control weight dominate MaxWeight, a CPU-only drift-plus-penalty controller and three adapted learning-based policies; at matched on-time completion (92.9% versus 93.0%), cost per on-time task is 5.8% lower than under the CPU-queue policy. Ablations identify memory reservations and deadline checks as decisive for valid completion. These results provide a quantitative basis for aligning service deployment, resource provisioning and online assignment with the workload each infrastructure can support.
Source: Applied Sciences
DOI: 10.3390/app162211168
Authors: Yan Song, Hailong Liu, Guangmin Hu
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7225773990
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