Abstract:
Mobile Edge Computing is a state-of-art technology which is being used to providereal-time environment by
computing and responding in shorter timelines to the IoT generated requests. The task computation requests being sent to
these servers is called task offloading, which is a highly complex process. The decision to offload a task for remote
computation is done with the aim of receiving responses within few instant but these server in turn gets heavily loaded
with thousands of computation requests as each server is connected to a number of IoT devices. This may result in situations
like imbalanced workloads and resource starvation. The occurrence of this situation is caused due to adoption of full task
offloading policy targeted IoT environment. Many works have already been observed in order to improve this offloading
approach but it remains a complex issue. In this research study it is being tried to propose a latency minimizing procedure
with optimal task splitting method. This will not only prevent resource starvation but also reduce total incurring latency
and lead to quick responses. The proposed work will facilitate parallel remote and local computationof task and thus
reducing the total computation time with optimal set of resources. The proposed model has been validated using hypothesis
testing including Shapiro-wilk, One-wayANOVA Test, F-Test two-sample Z-test, Multiple Linear Regression Test and
was successfully found to be efficient in minimizing latency with the use of partial offloading policy and have resulted in
optimal resource allocation when compared to other traditionally existing offloading policies.
