A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment
Cloud computing has emerged as a high-performance computing environment with a large pool of abstracted, virtualized, flexible, and on-demand resources and services. Scheduling of scientific workflows in a distributed environment is a well-known NP-complete problem and therefore intractable with exact solutions. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The aim of this study is to optimize multi-objective scheduling of
... c workflows in a cloud computing environment based on the proposed metaheuristic-based algorithm, Hybrid Bio-inspired Metaheuristic for Multi-objective Optimization (HBMMO). The strong global exploration ability of the nature-inspired metaheuristic Symbiotic Organisms Search (SOS) is enhanced by involving an efficient list-scheduling heuristic, Predict Earliest Finish Time (PEFT), in the proposed algorithm to obtain better convergence and diversity of the approximate Pareto front in terms of reduced makespan, minimized cost, and efficient load balance of the Virtual Machines (VMs). The experiments using different scientific workflow applications highlight the effectiveness, practicality, and better performance of the proposed algorithm. solutions in the objective space is called the Pareto front  . Many existing studies deal with cloud workflow scheduling as a single or bi-objective optimization problem without considering some important requirements of the users or the providers. Therefore, it is highly desirable to formulate scheduling of the workflow applications as a MOP taking into account the requirements from the user and the service provider. For example, the cloud workflow scheduler might wish to consider user's Quality of Service (QoS) objectives, such as makespan and cost, as well as provider's objectives, such as efficient load balancing over the Virtual Machines (VMs). Predict Earliest Finish Time (PEFT)  is an efficient heuristic in terms of makespan proposed for task scheduling in heterogeneous systems. This heuristic assign priorities to tasks and schedules them in a priority order to the known-best VM. However, list-based heuristics are only locally optimal. Therefore, a metaheuristic approach can be very effective to achieve better optimization solutions for workflow scheduling in the cloud. However, each metaheuristic algorithm has its own merits and demerits. Therefore, hybrid approaches have shown to produce better results [6,7] as they combine heuristic rules with metaheuristic algorithms and have attracted much attention in recent years to solve multi-objective workflow scheduling problems in the cloud. Symbiotic Organisms Search (SOS)  was proposed as a nature-inspired metaheuristic optimization algorithm that was inspired by the interactive behavior between organisms in an ecosystem to live together and survive. SOS is a simply structured, powerful, easy to use, and robust algorithm for solving global optimization problems. The SOS algorithm has strong global exploration, faster convergence capability, and requires only common controlling parameters, such as population size and initialization. Recently, a discrete version of SOS  was proposed for scheduling a bag of tasks in the cloud environment. This paper proposes a hybrid metaheuristic for multi-objective workflow scheduling in a cloud based on the list-based heuristic algorithm PEFT and the discrete version of the metaheuristic algorithm SOS to achieve optimum convergence and diversity of the Pareto front. The two conflicting objectives of the proposed scheme Hybrid Bio-inspired Metaheuristic for Multi-objective Optimization (HBMMO) are to minimize makespan and to reduce cost along with the efficient utilization of the VMs. Therefore, the proposed multi-objective approach based on a Pareto optimal non-dominated solution considers the users' as well as providers' requirements for workflow scheduling in the cloud. The remaining sections of the paper are organized as follows. Section 2 discusses the background and investigates related work in the recent literature. Section 3 presents the system model and the problem formulation of the proposed method. After that, Section 4 describes the proposed algorithm. Then, the results of a simulation and its analysis are discussed in Section 5. Finally, Section 6 presents the main conclusions of the study.