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The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostly, such a process is to be decoupled into several input/output pairings (loops), so that a single controller can be assigned for each loop. In the frame of this research, the Brain Emotional Learning Based Intelligent Controller (BELBIC) forms the control structure for each decoupled loop. The paper's main objective is to develop a parameterization technique for decoupling and control schemes, which ensures robust control behavior. In this regard, the novel optimization technique Bacterial Swarm Optimization (BSO) is utilized for the minimization of summation of the integral time-weighted squared errors (ITSEs) for all control loops. This optimization technique constitutes a hybrid between two techniques, which are the Particle Swarm and Bacterial Foraging algorithms. According to the simulation results, this hybridized technique ensures low mathematical burdens and high decoupling and control accuracy. Moreover, the behavior analysis of the proposed BELBIC shows a remarkable improvement in the time domain behavior and robustness over the conventional PID controller.

In the last decade, containers have become a superior alternative to hypervisor-based virtualization. Containerization has revolutionized data centers from being an infrastructure-oriented to be application oriented. Modern cloud consumption patterns such as flash crowds require a certain amount of elasticity that is realized with controlling the amount of provisioned resources autonomously. Cloud elasticity is significant as it influences the performance of utilized resources, service level commitment, and power consumption. In this paper, an infrastructure elasticity control algorithm for a containerized cloud is proposed. The proposed algorithm augments the load balancing criterion with elasticity control. Several experiments with various metrics are carried out to examine the performance of the proposed algorithm. The results demonstrate the superiority of the proposed algorithm and the effects of elasticity across various measures.INDEX TERMS Containers, cloud computing, elasticity control, containers migration, load balancing.