Recently, modern businesses have started to transform into cloud computing platforms to deploy their workflow applications. However, scheduling workflow under resource allocation is significantly challenging due to the computational intensity of the workflow, the dependency between tasks, and the heterogeneity of cloud resources. During resource allocation, the cloud computing environment may encounter considerable problems in terms of execution time and execution cost, which may lead to disruptions in service quality given to users. Therefore, there is a necessity to reduce the makespan and the cost at the same time. Often, this is modeled as a multi-objective optimization problem. In this respect, the fundamental research issue we address in this paper is the potential trade-off between the makespan and the cost of virtual machine usage. We propose a HEFT-ACO approach, which is based on the heterogeneous earliest end time (HEFT), and the ant colony algorithm (ACO) to minimize them. Experimental simulations are performed on three types of realworld science workflows and take into account the properties of the Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than basic ACO, PEFT-ACO, and FR-MOS.

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These resources are virtualized using virtualization software to make them available to users as a service. In this environment, the migration of virtual machines (VMs) is a significant concern these days. This technique provided by virtualization technology impacts the performance of the cloud. When allocating resources, the distribution of VMs is unbalanced, and their migration from one server to another can increase energy consumption and network overhead, necessitating an improvement in VM migrations. This paper presents a machine learning model for migrating virtual machines. The goal is to improve the selection of virtual machines and migration processes by reducing energy consumption and the number of VMs migrations. Numerical results demonstrate that the proposed solution can significantly enhance the goals addressed.