Multi-Class Task Offloading Optimization in Mobile Edge Computing

The proliferation of mobile devices and the increasing demand for portable services have led to a surge in computationally intensive and time-sensitive applications, necessitating efficient computation offloading in Mobile Edge Computing. Existing research often overlooks the distinct response time requirements of different applications and their mutual interference during queuing. This paper addresses these gaps by optimizing offloading strategies with multiple response time constraints, focusing on the queuing impact of various tasks. We model edge servers as M/M/1 queuing systems and develop a set of mathematical models. Using algorithms based on Karush-Kuhn-Tucker conditions, we derive optimal offloading strategies to minimize system power consumption. Our approach significantly reduces system power consumption in practical applications, enhancing resource utilization for service providers. Our work introduces a comprehensive and realistic model of task offloading, considering multiple task types, their distinct average response time requirements, and their mutual interference during queuing. This advancement ensures efficient and effective offloading strategies, addressing the complexity of multiple tasks and varying response time constraints.