Delay-aware-dependent Task Scheduling and Computation Offloading Strategy for Internet of Vehicles
To improve the response speed of task-dependent applications in Internet of Vehicles(IoV),a delay-aware-dependent task scheduling and computational offloading strategy was investigated that included the following three design points.First,a vehicle-side task-dependency application model was constructed based on a directed acyclic graph(DAG),which characterizes in detail the dependencies between tasks in each application while constructing the total task DAG for multi-vehicle multi-application scenarios.Second,a local computational and offloading model was designed based on the partial offloading mode,which considers multiple delay terms such as queuing time,computational time,and result transmission time.Expressions for the execution waiting time and delay minimization optimization problem were also formulated.Third,based on the design principle of"completing more tasks in less time"and the characteristics of task dependency,the execution and waiting time priority indicators of tasks were designed.An improved heterogeneous earliest finish time task scheduling algorithm was then designed that fully considers these time priority indicators.Next,an optimal task scheduling order to improve the delay performance was obtained.Finally,to obtain the optimal offloading decision for each task,a Markov decision process was constructed for task calculation.A task offloading algorithm based on a deep deterministic policy gradient was designed,and the optimal computational offloading decision was obtained.Simulation experiments were conducted under different network settings.Results show that compared with existing delay minimization schemes,the proposed scheme has obvious delay performance advantages and is more suitable for IoV with strict low-delay requirements.
traffic engineeringinternet of vehicles(IoV)mobile edge computingtask-depend-ency applicationtask scheduling and allocationdeep deterministic policy gradient(DDPG)
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