multi-objective differential evolution algorithm (dea)

In recent years, the cloud computing model has received a lot of attention due to its high scalability, reliability, information sharing and low cost compared to separate machines. In the cloud environment, scheduling and optimal allocation of tasks affects the effective use of system resources. Currently, common methods for scheduling in the cloud computing environment are performed using traditional methods such as Min-Min and meta-heuristic methods such as ant colony optimization algorithm (ACO). The above methods focused on optimizing one goal and do not estimate multiple goals at the same time. The main purpose of this research is to consider several objectives (total execution time, service level agreement and energy consumption) in cloud data centers with scheduling and optimal allocation of tasks. In this research, multi-objective differential evolution algorithm (DEA) is used due to its simple structure features and less adjustable parameters. In the proposed method, a new approach based on DEA to solve the problem of allocation in cloud space is presented which we try to be effective in improving resource efficiency and considering goals such as time, migration and energy by defining a multi-objective function and considering mutation and crossover vectors. The proposed method has been evaluated through a CloudSim simulator by testing the workload of more than a thousand virtual machines on Planet Lab. The results of simulation show that the proposed method in comparison with IqrMc, LrMmt and FA algorithms, in energy consumption by an average of 23%, number of migrations by an average of 29%, total execution time by an average of 29% and service level agreement violation (SLAV) by an average of 1% has been improved. In this case, use of the proposed approach in cloud centers will lead to better and appropriate services to customers of these centers in various fields such as education, engineering, manufacturing, services, etc.