Search

Task scheduling is an emerging challenge in cloud platforms and is considered a critical application utilized by the cloud service providers and end users. The main challenge faced by the task scheduler is to identify the optimal resources for the input task. In this research, a Sine Cosine-based Elephant Herding Optimization (SCEHO) algorithm is incorporated with the Improved Particle Swarm Optimization (IPSO) algorithm for enhancing the task scheduling behavior by utilizing parameters like load balancing and resource allocation. The conventional EHO and PSO algorithms are improved utilizing a sine cosine-based clan-updating operator and human group optimizer that improve the algorithm’s exploration and exploitation abilities and avoid being trapped in the local optima problem. The efficacy of the SCEHO-IPSO algorithm is analyzed by using performance measures like cost, execution time, makespan, latency, and memory storage. The numerical investigation indicates that the SCEHO-IPSO algorithm has a minimum memory storage of 309 kb, a latency of 1510 ms, and an execution time of 612 ms on the Kafka platform, and the obtained results reveal that the SCEHO-IPSO algorithm outperformed other conventional optimization algorithms. The SCEHO-IPSO algorithm converges faster than the other algorithms in the large search spaces, and it is appropriate for large scheduling issues.

In wireless sensor networks, clustering is said to be the most noteworthy technique for increasing the lifetime of network that directly leads a better routing mechanism. This approach involves grouping of sensor nodes to clusters and choosing the appropriate cluster heads for each cluster. In fact, cluster heads gathers data from corresponding nodes in cluster and transmits those aggregated data to base station. However, the major issue in this is the selection of the appropriate cluster head. Till now, many research works have been carried out for solving this issue by considering different constraints. This paper introduces a new cluster‐based routing model by selecting the optimal cluster head. Moreover, a novel algorithm known as grey wolf updated whale optimization algorithm is introduced. Here, a new multi‐objective function is defined with respect to different constraints like distance, delay, security and energy, respectively. Finally, the performance of security aware clustering with grey wolf updated whale optimization algorithm is evaluated and validated over other conventional works with respect to alive node analysis, throughput and normalized network energy, respectively. [ABSTRACT FROM AUTHOR]