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Data-Driven Task Offloading Method for Resource-Constrained Terminals via Unified Resource Model
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AI
DOI：10.1109/JIOT.2023.3235065.png)
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Abstract
En 中文
In recent years, with an increasing number of Internet of Things (IoT) devices, general cloud computing mode is hard to process large amounts of data with high Quality of Service (QoS). Edge computing is put forward to relieve the pressure of cloud servers, but most of them only focused on allocating tasks depending on cloud servers or edge servers with the virtualization technology. Resource-constrained smart mobile terminals (RC-SMTs) produce most of the data to be processed but some of them are usually not able to support even Docker technology. The cooperative computation capacity of RC-SMTs is potential but is often neglected by most researchers. However, there is little research focus on edge computing only among RC-SMTs without computing ability supported by servers. For this reason, this article proposes a framework named data-drive task offloading with a unified resource model (DDTO-URM) to manage the limited resource of IoT which enables the allocation of tasks constantly generated from the edge of the network. Then, a meta-heuristic algorithm called grouped crossover genetic algorithm (GCGA) is designed to obtain task offloading strategy under a resource-constrained environment. As a result, the computation capacity of the system is enhanced to cover the requirement by improving the utilization of RC-SMTs. Through the analysis of simulation, the proposed approach can deal with the problem of DDTO-URM better than benchmark algorithms under constraints, ensuring the real time and ultralightweight of the collaborative edge-computing system.
Keywords:
Task analysis
Servers
Internet of Things
Computational modeling
Resource management
Energy consumption
Cloud computing
Computation offloading
heuristic algorithms
Internet of Things (IoT)
mobile-edge computing (MEC)
resource management
Journal
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