No abstract

Internet of Things (IoT) is predicted to permeate all areas of the physical world, particularly homes and urban settings, in the next years. Cloud‐based IoT is a network of things that can be managed and inspected to create various intelligent systems over the internet. The primary technological difficulty in service computing is swiftly integrating diverse services to serve cross‐organizational business activities. It is one of the famous NP‐hard problems; therefore, this study proposes a novel service composition technique termed multiobjective particle swarm optimization and crowding distance (MOPSO‐CD) approach to solve this problem. The main issue with the MOPSO method is that the search is conducted very quickly, resulting in an incorrect response. To address this issue, we integrate MOPSO with the CD approach to provide an efficient composition service in cloud‐based IoT. The proposed method is simulated using Matlab, and the performance is compared against the performance of three other multi‐objective algorithms. The findings revealed that the proposed method outperforms different algorithms regarding availability, reliability, response time, latency, and energy consumption.

Cloud computing (CC) provides dynamic hiring of server abilities as scalable virtualized services to end-users. However, data center hosting wastes massive amounts of energy resulting in high operational costs and carbon footprints. Also, virtualization is one of CC's main features, and physical resources are delivered by virtual machine (VM).Therefore, in the present article, a new method is provided to improve the VM energy consumption and execution time in the VM migration problem using a hybrid optimization algorithm. Since this issue is one of the famous NP-hard problems, a method is proposed in this article works based on genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The hybrid algorithm uses a GA to dominate PSO algorithms' constraints, such as weak convergence and stymie in global optima. The CloudSim simulator is employed to show the efficiency of the method compared to others. Using this method will keep the proficiency and power performance of the data centers at the same level. The results showed that energy consumption in the proposed method is better than the other three methods and has been improved by an average of 23.19%. Also, the results showed that execution time is better than the other three methods and has been improved by an average of 29.01%.