Cooperative Coevolutionary Genetic Programming Hyper-Heuristic for Budget Constrained Dynamic Multi-workflow Scheduling in Cloud Computing

Abstract

Dynamic Multi-workflow Scheduling (DMWS) in cloud computing is a well-known combinatorial optimisation problem. It is a great challenge to tackle this problem by scheduling multiple workflows submitted at different times and meet user-defined quality of service objectives. Scheduling with user-defined budget constraints is becoming increasingly important due to cloud dynamics associated with on-demand provisioning, instance types, and pricing. To address the Budget-Constrained Dynamic Multi-workflow Scheduling (BC-DMWS) problem, a novel Cooperative Coevolution Genetic Programming (CCGP) approach is proposed. Two heuristic rules, namely VM Selection/Creation Rule (VMR) and Budget Alert Rule (BAR), are learned automatically by CCGP. VMR is used to allocate ready tasks to either existing or newly rented VM instances, while BAR makes decisions to downgrade VM instances so as to meet the budget constraint. Experiments show significant performance and success rate improvement compared to state-of-the-art algorithms.