EESF: Energy-efficient scheduling framework for deadline-constrained workflows with computation speed estimation method in cloud

Abstract

Substantial amount of energy consumed by rapidly growing cloud data centers is a major hindrance to sustainable cloud computing. Therefore, this paper proposes a scheduling framework named EESF aiming at minimizing the energy consumption and makespan of workflow execution under deadline and dependency constraints. The novel aspects of the proposed EESF are outlined as follows: 1) it first estimates the computation speed requirements of the entire workflow application before beginning the execution. Then, it estimates the computation speed requirements of individual tasks dynamically during execution. 2) Different from existing approaches that mainly assign tasks to virtual machines (VMs) with lower energy consumption or use DVFS to lower the frequency or voltage of hosts/VMs leading to longer makespan, EESF considers the degree of dependency of the tasks along with estimated speed for task-VM assignment. 3) Based on the fact that scheduling dependent tasks on same VM is not always energy-efficient, a new concept of virtual task clustering is introduced to schedule the tasks with dependencies in an energy-efficient manner. 4) EESF deploys VMs dynamically as per the necessary computation speed requirements of the tasks to prevent over-provisioning/under-provisioning of computational power. 5) In general, task reassignment causes huge data transfer which also consumes energy, but EESF reassigns tasks to more-energy efficient VMs running on the same host, thereby zeroing the data transfer time. Experiments performed using four real-world scientific workflows and 10 random workflows illustrate that EESF reduces energy consumption by 6%-44% than related algorithms while significantly reducing the makespan.