An Intelligent Cloud Workflow Scheduling System With Time Estimation and Adaptive Ant Colony Optimization

The introduction of workflow in cloud computing has afforded a new and efficient way to tackle large-scale applications. As an NP-hard problem, how to schedule cloud workflows effectively and economically with deadline constraints and different kinds of tasks and resources is extraordinarily challenging. To solve this constrained problem, this paper intends to develop an intelligent scheduling system from the perspective of users to reduce expenditure of workflow, subject to the deadline and other execution constraints. A new estimation model of the task execution time is designed according to virtual machine settings in real public clouds and execution data from practical workflows. Based on the new model, an adaptive ant colony optimization algorithm is proposed to meet the quality of service and orchestrate tasks. The adaptiveness of the algorithm is embodied in two aspects. First, an adaptive solution construction method is designed that each solution is built with a dynamically changing resource pool, thus the search space of the algorithm is narrowed down and the execution time is decreased. Second, two heuristics with self-adaptive weight are introduced to adaptively meet different deadline settings. Simulating results on four types of workflows show that the proposed approach is effective and competitive.