Makespan and Energy Efficient Workflow Scheduling in Cloud Computing

In the current digital era, organizations can operate, expand and innovate more efficiently by exploiting cloud services which offer scalability and cost-effectiveness. Cloud services can be accessed from various data centers located across the world that house the required IT infrastructure. As cloud data centers use large quantities of electricity, energy conservation becomes essential to lowering operating costs and minimizing the environmental effect of these facilities. Further, users demand reduction in execution time for submitted tasks. This research work proposes a PEGO algorithm for scheduling workflows in cloud considering reduction in makespan and energy consumption. PEGO is a hybrid algorithm of PEFT and GWO in which PEFT seeds are used for generating the initial population of GWO which outperforms GWO and is capable of optimizing makespan and energy consumption. The proposed strategy is assessed using Workflowsim to perform simulations on three benchmark scientific workflows. The findings show that the suggested PEGO algorithm outperforms GWO and is capable of optimizing makespan and energy consumption. PEGO shows quicker convergence and higher-quality solutions due to the incorporation of PEFT seeds while generating the initial population. The usage of crossover operators and a local search strategy also played significant role in this direction by improving its exploration and exploitation capabilities, respectively.