Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization

The scientific workflows are high-level complex applications that demand more computing power. The cloud data center (CDC) remains one of the essential models of economic infrastructure for workflow applications. These CDCs consume a lot of electric power while running workflow applications. Hence, efficient energy-aware scheduling techniques are required to perform the task to a virtual machine (VM) mapping. The existing researches overlooked to join the workflow scheduling and VM consolidation which addresses resource utilization and energy consumption effectively. In this article, we propose an energy-aware algorithm for workflow scheduling in cloud computing with VM consolidation called EASVMC. The proposed EASVMC approach is modeled to address the multi-objectives such as energy consumption, resource utilization, and VM migrations. The EASVMC algorithm runs in two phases task scheduling and VM consolidation (VMC). In the first phase, the task with maximum execution length is mapped to the virtual machine that will perform it with the minimum energy. The second phase contains VM consolidation is a prominent NP-hard problem. The VMC phase categorizes the physical hosts into the normal load, under-loaded and overloaded hosts based on CPU utilization. Double threshold values are used for this purpose. VMs from underloaded and overloaded hosts are migrated to normally loaded hosts. For the VMC phase, we used a nature inspired meta-heuristic approach called the Water Wave Optimization (WWO) algorithm, which finds a suitable migration plan to reduce the energy consumption by increasing the overall resource utilization and switch off idle hosts after migrating its VMs to a suitable target host. The efficiency of our proposed method evaluated using the WorkflowSim simulation tool with five different real-world scientific workloads. The experimental results show that the EASVMC approach surpassed the similar works in stated objectives irrespective of diverse workloads.