A Budget-Constrained Energy-Efficient Scheduling Algorithm on Cloud-Edge Collaborative Workflows

With the development of Internet of Things applications, traditional cloud computing cannot meet the new application requirements, and the mode of cloud edge collaborative processing is becoming more and more important. Cloud computing workflow scheduling algorithm seldom considers the heterogeneity of cloud edge devices, so it is difficult to achieve the balance of completion time, energy consumption, and cost. Cloud-edge collaborative workflow scheduling can reasonably allocate tasks according to the characteristics of dependencies and heterogeneous resources, which can solve the imbalance between performance and cost as well as between performance and energy consumption. Therefore, we propose a heterogeneous earliest completion time scheduling algorithm (W2DG-HEFT) based on a greedy rotation strategy. Considering the differences in computing performance, transmission delay, energy consumption, and cost of cloud edge computing nodes, the candidate queues selected by cloud and edge 2D weights are adjusted and balanced. The HEFT algorithm, which adopts energy consumption awareness and greedy rotation strategy, allocates the appropriate cloud or edge computing nodes according to the task priority in the workflow. Through simulation platform experiment, compared with GPR-HEFT algorithm, when the cost threshold is 2-3 USD per hour, the scheduling completion time on the workflow of 100 tasks is reduced by about 5.4% on average. Compared with MOACS, PSO, and GA, the scheduling completion time on the workflow of 1000 tasks is reduced by 8.28% on average, and the average comprehensive cost is reduced by 23% on the condition of satisfying the time constraints.