The rapid proliferation of latency-sensitive Internet of Things (IoT) applications boosts the frequency of offloading compute-intensive tasks from IoT users to mobile edge computing (MEC) due to the limitation resources of IoT devices. It is inevitably for IoT users to compete for the computing resources of the MEC, especially when the computation tasks are dependent and have hard deadline constraints. However, most existing dependent task offloading schemes may not well consider the resource competition issues among IoT users, and possibly lead to limited system performance in multiuser scenario. To address this issue, we intend to design an auction-based dependent task-offloading mechanism to improve the efficiency of task offloading for multiple IoT users. First, we formulate the dependent task offloading as a valuation maximization problem in the trade of computing resources satisfying users’ latency requirements, which has been proved to be NP-hard. Then, by jointly considering the task graph structure and the current status of the MEC, we propose a truthful auction mechanism, named greedy winner selection strategy, in which a heuristic dependent task assignment for winners is designed to improve the efficiency of the task offloading. By conducting extensive simulations, we validate that the performance of the proposed dependent task offloading strategy is superior to existing competition algorithms, in terms of total valuations, average makespans, and success rates.

With the proliferation of versatile mobile applications, offloading compute-intensive tasks to the MEC/Cloud becomes a dramatic technique due to the limited resources and high user experience requirements at mobile devices. However, most existing works design their task offloading schemes without considering the dependence of tasks and the orchestration of the MEC and Cloud, and thus may limit the system performance. In this paper, we propose a dependent task offloading framework for multiple mobile applications, named COFE, where mobile devices can offload their compute-intensive tasks with dependent constraints to the MEC-Cloud system. It can assign the offloaded tasks to the MEC and Cloud adaptively to improve the user experience. Based on COFE, we formulate the task offloading problem as an average makespan minimization problem, which is proved to be NP-hard. Then, we propose a heuristic ranking-based algorithm to assign the offloaded tasks according to their bottom levels. Theoretical analysis proves the stability of the system under the proposed algorithm and extensive simulations validate that the proposed algorithm can significantly reduce the average makespan and deadline violation probabilities of offloaded applications.

Cloud has become an important platform for executing numerous deadline-constrained scientific applications generally represented by workflow models. It provides scientists a simple and cost-efficient method of running workflows on their rental Virtual Machines (VMs) anytime and anywhere. Since pay-as-you-go is a dominating pricing solution in clouds, extensive research efforts have been devoted to minimizing the monetary cost of executing workflows by designing tailored VM allocation mechanisms. However, most of them assume that the task execution time in clouds is static and can be estimated in advance, which is impractical in real scenarios due to performance fluctuation of VMs. In this paper, we propose an onliNe multi-workflOwSchedulingFramework, named NOSF, to schedule deadline-constrained workflows with random arrivals and uncertain task execution time. In NOSF, workflow scheduling process consists of three phases, including workflow preprocessing, VM allocation and feedback process. Built upon the new framework, a deadline-aware heuristic algorithm is then developed to elastically provision suitable VMs for workflow execution, with the objective of minimizing the rental cost and improving resource utilization. Simulation results demonstrate that the proposed algorithm significantly outperforms two state-of-the-art algorithms in terms of reducing VM rental costs and deadline violation probability, as well as improving the resource utilization efficiency.