Dynamic provisioning with structure inspired selection and limitation of VMs based cost-time efficient workflow scheduling in the cloud

Abstract

Workflow scheduling in cloud computing environments is nowadays a hot topic as scientific workflows application are gradually taking advantage of commercial cloud assets. Common users’ quality of service (QoS) requirements are the respect of defined budget and deadline when executing their workflow job. Since execution cost minimization and completion time minimization are contradictory objectives, addressing such issue through trade-off function approaches have proved to be an efficient way. This paper presents the Cost-Time Trade-off efficient Workflow Scheduling with Dynamic provisioning (CTTWSDP) algorithm. CTTWSDP relies on dynamic VMs provisioning with a limited number of leased VMs, and a cost-time trade-off function over heterogeneous instances to determine the most viable schedule. CTTWSDP also proposed an improved Implicit Requested Instance Types Range (IRITR) evaluation, which is a scheduling concept introduced in our previous work. The IRITR evaluation aims at determining a range of VMs instance types that best suits the workflow execution, in order to avoid overbidding and underbidding that may lead to budget and deadline violation respectively. The results of simulations prove the effectiveness of the proposal. CTTWSDP achieves a 17.09–76.06% higher success rate when compared to four state-of-the-art algorithms. Furthermore, ANOVA along with Tukey–Kramer post-hoc tests have been conducted, revealing the effectiveness of CTTWSDP over three of the baseline algorithm, while for the fourth one the outperformance of CTTWSDP is not statistically significant. An analysis of the standard deviation of the success rate proves that CTTWSDP is more stable in its performance no matter the type and the workload of the workflow. With a standard deviation of 6.73, smaller than the ones obtained by the other algorithms from 18.66 to 34.10.