Abstract
We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.
Highlights
Cloud computing presents an interesting technology that facilitates the execution of scientific and commercial applications
The scheduler must be able to schedule workflow tasks so as to maximize the provider profits by minimizing energy consumption while preserving the users QoS preferences. We achieve this by using an iterative method called Multi-objective Discrete Particle Swarm Optimization (MODPSO) combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique
We provide some definitions of the Pareto concepts used in Multi-objective Optimization Problem (MOP) as follows:: (i) Pareto dominance
Summary
Cloud computing presents an interesting technology that facilitates the execution of scientific and commercial applications It provides, on demand, flexible and scalable services to customers through a pay per use basis. These scientific workflows typically involve complex data of different sizes and long term computer simulations They need high computation power and the availability of large infrastructures that grid and more recently cloud computing environments provide with different QoS levels. The scheduler must be able to schedule workflow tasks so as to maximize the provider profits by minimizing energy consumption while preserving the users QoS preferences We achieve this by using an iterative method called Multi-objective Discrete Particle Swarm Optimization (MODPSO) combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique.
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