With the increasing diversity and complexity of mobile applications, an application typically needs to execute multiple dependent tasks to achieve its functionality. Computation offloading in multi-access edge computing aims to improve user experience, such as reducing makespan and terminal energy consumption, by offloading some tasks to the designated edge server. Task dependencies impose constraints on the execution order of the tasks, which complicates the offloading decisions. Besides, transmission rates exhibit fluctuations in real-world scenarios due to the mobility of users, and they pose new challenges to the problem of dependent task offloading. To minimize the terminal energy consumption under the given deadline, a joint optimization problem of dependent task offloading and local computing resource allocation with time-varying transmission rates is investigated. Since the proposed problem is NP-hard, we decompose it into two subproblems to reduce the complexity and deal with the coupling of decision variables. Specifically, we first solve the subproblem of dependent task offloading by generating special task sets to implement divide and conquer with a fixed local processing speed. Then, we employ Karush-Kuhn-Tucker (KKT) method to solve the subproblem of local resource allocation with the offloading solution derived from the first subproblem while ensuring that the makespan constraint is met. The proposed scheme makes dynamic decisions on dependent task offloading and resource allocation according to the fluctuations of transmission rates, and dynamically adjusts the solution accordingly to achieve better performance. Experimental results show that our solution outperforms baseline methods, reducing makespan by 27–31%, terminal energy consumption by 70–77%, while achieving 25–65% higher service success ratio on average.

Abstract Mobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience for the user. However, it is difficult to obtain the optimal migration policy in real time due to the huge state space. Considering delay‐sensitive data‐intensive applications run by mobile terminals with limited battery power, an efficient service migration policy should be able to make a good tradeoff among service cost, service delay and terminal energy consumption. Here, an online Monte Carlo‐based service migration (MCSM) policy is proposed to minimize service cost under constraints of deadline and terminal energy consumption. A penalty mechanism is designed to update reward when partial or all constraints are not meet. State‐action value estimation and policy improvement are triggered only on the completion of each episode. Each episode is traversed reversely to calculate the average cumulative reward so as to improve policy. Experimental results show that the proposed approach can improve service success ratio and reduce average service cost compared to the existing service migration policies.

Due to the potential of revolutionizing a variety of human-centric services, human digital twin (HDT) is envisioned to become an important part of our daily life. The HDT applications need to frequently collect and process data obtained from individuals and their environment, analyzing each physical twin while updating its corresponding virtual twin, which will consume a large amount of computing, storage and sensing resources cumulatively. Meanwhile, running HDT applications, such as emotion recognition, naturally contains the executions of several dependent tasks. Considering the resource limitations of mobile terminals, we enable dependent task offloading to mitigate terminal load and reduce the latency of HDT applications. Specifically, this paper proposes an energy and cost-aware offloading algorithm for dependent tasks with edge-cloud collaboration to empower HDT applications. We show that the problem of dependent task offloading under constraints of service cost and terminal energy consumption is NP-hard. The complexity of task interdependency makes the offloading decision under dual constraints even more challenging. The proposed offloading algorithm firstly generates task paths based on task interdependency and computation load, deriving the initial solution. Then, task reassignment and CPU frequency scaling methods are utilized to further optimize the obtained solution. Simulation results illustrate that our approach can achieve better performance in terms of makespan and service success ratio compared to the existing approaches.