Cloud computing is considered suitable for organizations thanks to its flexibility and the provision of digital services via the Internet. The cloud provides nearly limitless computing resources on demand without any upfront costs or long-term contracts, enabling organizations to meet their computing needs more economically. Furthermore, cloud computing provides higher security, scalability, and reliability levels than traditional computing solutions. The efficiency of the platform affects factors such as Quality of Service (QoS), congestion, lifetime, energy consumption, dependability, and scalability. Load balancing refers to managing traffic flow to spread it across several channels. Asymmetric network traffic results in increased traffic processing, more congestion on specific routes, and fewer packets delivered. The paper focuses on analyzing the use of the meta-optimization algorithm based on the principles of natural selection to solve the imbalance of loads in cloud systems. To sum up, it offers a detailed literature review on the essential meta-heuristic algorithms for load balancing in cloud computing. The study also assesses and analyses meta-heuristic algorithm performance in load balancing, as revealed by past studies, experiments, and case studies. Key performance indicators encompass response time, throughput, resource utilization, and scalability, and they are used to assess how these algorithms impact load balance efficiency.

Maintaining security in communication networks has long been a major concern. This issue has become increasingly crucial due to the emergence of new communication architectures like the Internet of Things (IoT) and the advancement and complexity of infiltration techniques. For usage in networks based on the Internet of Things, previous intrusion detection systems (IDSs), which often use a centralized design to identify threats, are now ineffective. For the resolution of these issues, this study presents a novel and cooperative approach to IoT intrusion detection that may be useful in resolving certain current security issues. The suggested approach chooses the most important attributes that best describe the communication between objects by using Black Hole Optimization (BHO). Additionally, a novel method for describing the network’s matrix-based communication properties is put forward. The inputs of the suggested intrusion detection model consist of these two feature sets. The suggested technique splits the network into a number of subnets using the software-defined network (SDN). Monitoring of each subnet is done by a controller node, which uses a parallel combination of convolutional neural networks (PCNN) to determine the presence of security threats in the traffic passing through its subnet. The proposed method also uses the majority voting approach for the cooperation of controller nodes in order to more accurately detect attacks. The findings demonstrate that, in comparison to the prior approaches, the suggested cooperative strategy can detect assaults in the NSLKDD and NSW-NB15 datasets with an accuracy of 99.89 and 97.72 percent, respectively. This is a minimum 0.6 percent improvement.

A numerical simulation on the heat transport augmentation and flow drag behavior of spirally corrugated pipes was performed. The simulation was conducted on the basis of the experimental results documented in the published literature. The influence of the thread height and pitch on the hydraulic–thermal performance as well as the mechanism of the convection heat transport development inside the spirally corrugated pipe were explored. It was discovered that the convection heat transport performance elevates in the Reynolds number region of 4000~13,000 as the thread height rises or the Reynolds number enlarges, but it declines when the thread pitch extends. The convection heat transport performance marked by the Nusselt number of the spirally corrugated pipe could reach 2.77 times that of the plain pipe, while the flow resistance coefficients of spirally corrugated pipes are 89~324% above that of the plain pipe. It enlarges with the rise in thread height but declines with the extension of the thread pitch. It also reduces when the Reynolds number enlarges. The factors of overall heat transmission performance for all the spirally corrugated pipes are above 1.00, and they increase in the Reynolds number region of 4000~7000 and then decrease in the Reynolds number region of 7000 to 13,000. The secondary flow at the cross-sections and the vortex between two adjacent corrugated grooves are the basic causes of the promotion of convection heat transport inside the spirally corrugated pipes. The secondary flow near the pipe wall both disrupts the border layer and boosts the radial interfusion of the fluid. In addition, the existence of vortexes makes the secondary flow act on the convection heat transmission continuously and positively in the region close to the pipe wall.