Computer network

Biology

Server

Distributed computing

Queue

Ecology

Mobile edge computing

Energy consumption

Computer science

Real-time computing

Wireless Networks

Mobile Edge Computing (MEC) has evolved into a key technology that can leverage resources of computing, storage and network deployed at the proximity of the Mobile Devices (MDs). How to offload delay-sensitive and energy-constraint tasks is of research importance. In this paper, considering time-correlated traffic and Virtual Machine (VM) repair, we come up with a method to evaluate the task offloading scheme in a MEC system. Applying a Markovian Arrival Process (MAP) to describe the task arrivals in a MEC system, the local computing offloading and the MEC offloading are modeled as a MAP/M/1 queue and a MAP/M/N/N+K queue with VM repair, respectively. By exploiting the matrix-geometric solution approach and the Gauss–Seidel approach, we give the average delay, the energy consumption level and computation resource availability. Next, we provide numerical results to investigate the influence of the offload rate on the response performance and the energy efficiency with different correlation coefficients and the influence of the repair rate and the service rate of a VM on the availability. In addition, an improved Sine and Cosine Algorithm (SCA) is developed to acquire the optimal offload rate with a delay-energy tradeoff.

MAP based modeling method and performance study of a task offloading scheme with time-correlated traffic and VM repair in MEC systems