A novel quality-based computation offloading framework for edge cloud-supported internet of things

Abstract

The cloud and the Internet of Things (IoT) provide optimal service responses at the user's request through edge devices. Resource sharing and computations are distributed between the users and edge devices, reducing the complex processing instances. In such computations, the pipelined process due to one-to-many resource sharing increases the service lags. For addressing this issue, this article presents a Service-Intensive Computation Offloading Framework (SICOF). The proposed framework guarantees service quality preventing lag in resource-oriented computations. This process is forwarded based on support vector classification for pipelined and distributed computations. The modifications in classifier intervals ensure distributed computations with offloading. In the offloading process, the service-free edge and cloud platform devices are identified, and the pipeline is split for validation. The classification outputs generate allocated request processing or service distribution, achieving a high response ratio. The service quality-based results are verified using the metrics service delay, computation time, process distribution ratio, and service lag. Simulation result shows that the proposed framework SICOF achieves reduce service delay, computation time, and service lag.