A Robust and Efficient Computational Offloading and Task Scheduling Model in Mobile Cloud Computing

Mobile cloud computing (MCC) has the potential to greatly enhance the computational capabilities of smart mobile devices (SMDs) and reduce their energy consumption by offloading compute-intensive workloads from SMDs onto the resource-rich cloud. Energy-efficient compute offloading remains a problem, especially when applications are under strict time constraints. In response to this issue, this research proposes an Efficient Computational Offloading and Task Scheduling model (ECOTS) strategy to cut down on power use and minimize the time it takes to finish an application. First, we formalize the ECOTS issue as a reduction of energy consumption under the constraints of a task-dependency criterion and an absolute time limit. To solve the issue of offloading computing from mobile devices, we present it as an optimization problem. We then offer two efficient offloading strategies to address the issue. Numerical experiments are used to determine how effective the suggested algorithms are. Finally, we demonstrate using data from a working testbed that the ECOTS method can effectively reduce energy expenditures by properly adjusting the CPU clock speed of SMDs in remote computation and by improving the transmission energy for wireless range circumstances in the cloud.