Cloud computing provides robust solutions for managing complex workflow applications in high-performance computing. Minimizing workflow scheduling time while adhering to cost constraints represents a critical quality-of-service metric for cloud providers. A novel efficient workflow scheduling algorithm with low time complexity, termed MBCVSL, is proposed in this study. MBCVSL incorporates analysis of variance to develop an innovative cost pre-allocation strategy. The algorithm comprises three sequential stages: establishing a scheduling queue based on task execution priorities; distributing total cost constraints across individual tasks; and employing a dual allocation strategy. MBCVSL assigns critical path tasks to critical virtual machines and allocates remaining tasks to optimal virtual machines through an optimized selection process. Experimental results demonstrate that MBCVSL outperforms both MSLBL and ESBL algorithms in minimizing workflow makespan under execution cost constraints.