Abstract
Scheduling extensive scientific applications that are deadline-aware (usually referred to as workflow) is a difficult task. This research provides a virtual machine (VM) placement and scheduling approach for effectively scheduling process tasks in the cloud environment while maintaining dependency and deadline constraints. The suggested model’s aim is to reduce the application’s energy consumption and total execution time while taking into account dependency and deadline limitations. To select the VM for the tasks and dynamically deploy/undeploy the VM on the hosts based on the jobs’ requirements, an energy-efficient VM placement (EVMP) algorithm is presented. Demonstrate that the proposed approach outperforms the existing PESVMC (power-efficient scheduling and VM consolidation) algorithm.
Highlights
Large-scale complex scientific applications/workflow are executed and analyzed in the multi-disciplinary area of research such as astronomy and physics [1]
The performance of the energyefficient virtual machines (VMs) placement (EVMP) algorithm is evaluated based on the Average Resource Utilization (ARU), total energy consumption, and workflow makespan
The proposed EVMP algorithm has reduced the energy consumption by applying Dynamic Voltage and Frequency Scaling (DVFS) for the VMs/hosts which are not performing any work or idle computing resources, and software techniques for VMs and hosts which are idle beyond the preestablished threshold time
Summary
Large-scale complex scientific applications/workflow are executed and analyzed in the multi-disciplinary area of research such as astronomy and physics [1]. A directed acyclic graph (DAG) is used to represent workflows These workflows often have disparate requirements (such as storage and CPU) and constraints (dependency) that need to be accounted during their execution. An energyefficient scheduling algorithm can be used to manage the resources that are required by the task while executing these scientific workflow tasks. The energy-aware scheduling algorithm must be selected which can provision a proper resource from the offered resources which are efficient enough to complete the workflow tasks within their deadline constriction, and it can decrease the energy consumption. To minimize the energy consumption Dynamic Power Management (DPM) [11, 12], Dynamic Voltage and Frequency Scaling (DVFS) [12–15], resource consolidation with migration techniques [6, 16], virtualization [6], and green policies [17], technologies are used. During the scheduling of tasks, server overloading is prevented by monitoring the server status [18]
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