Energy and delay-ware massive task scheduling in fog-cloud computing system

In this paper, we consider the computation offloading optimization problem with heterogeneous resources in a fog-cloud computing system. The problem is common in many real-time and mobile applications, where tasks are massive and computation-intensive, and the computing resources could involve both fog devices and cloud platforms. The challenges lie in proposing effective, efficient and robust algorithms with the objectives of minimizing both the total delay and the energy consumption. A bi-objective task scheduling model is formulated, in which the queuing models for the delay estimation and the energy consumption models for heterogeneous resources are introduced. A Pareto-optimization-based Massive Task Scheduling Framework is proposed to schedule massive tasks within one time unit. It starts from a non-dominated solution set obtained by the energy-ware and the transmission delay-aware local search procedures. A tree-based local search method is proposed to further improve the non-dominated solutions. The proposed algorithm is compared to four classical algorithms for the similar problems. Their performances are evaluated by the Pareto-optimization metrics on multiple aspects. Experimental results demonstrate the effectiveness and robustness of the proposal for the problem under study.