Coalition theory based task scheduling algorithm using DLFC-NN model

Resource management and job scheduling are essential in today's cloud computing world. Due to task scheduling and users' diverse submission of large-scale requests, co-located VM instances negatively impacted the performance of leased VM instances. This workload further led to resource rivalry across co-located VMs. In order to address the aforementioned problems, numerous strategies have been presented, however, they fail to take the asynchronous nature of the cloud environment into account. To address this issue, a novel “CTA using DLFC-NN model” is proposed. This proposed approach combines the coalition theory and DLFC-NN techniques by including IRT-OPTICS for task size clustering, digital metrology based on ionized information (DMBII) for defect detection in virtue machines (VM), and the dynamic levy flight hamster optimization algorithm for processing time optimization of the clusters. However, the implementation of task scheduling in an online environment is limited by a number of presumptions or oversimplifications made by current scheduling systems. As a result, a unique coalition theory is applied to efficiently schedule activities. In addition, the DLFC-NN model is used to reduce resource consumption, span time, and be highly accurate and energy-efficient when working on both online and offline jobs. Nevertheless, while optimizing the clusters' overall execution time, earlier approaches only decreased the make-span time for task scheduling. However, the DLFC-NN model solves the computation problem by using a fully weighted bipartite graph and the pseudo method to determine the fitness of the least makespan time. The enhanced methodology used in this study reduces the scheduling cost and minimizes job completion times according to different task counts when compared to the existing techniques.     

Study of chemically reactive and thermally radiative Casson nanofluid flow past a stretching sheet with a heat source

Nanoparticle (NP) delivery is an exciting and rapidly developing field that adequately takes care of thermal radiation in blood flow and is likely to have bearing on the therapeutic procedure of hyperthermia, blood flow, and heat transfer in capillaries. The NP parameters such as size, shape, and surface characteristics can be regulated to improve nano-drug delivery efficiency in biological systems. The NPs outperform traditional drug delivery processes in drug carrying capacity and controlled release. The current article investigates the boundary layer flow and heat transfer of thermally radiative Casson nanofluid (NF) over a stretching sheet with chemical reaction and internal heat source. In our study, Cu and Al₂O₃ are taken as NPs in a suitable base fluid. The problem is analyzed by using similarity transformations and is solved with MATLAB's built-in solver bvp4c. The effects of pertinent parameters characterizing the flow model are presented through graphs and tables. The important findings of the investigation are noted as: the use of metallic oxide is more beneficial to attain higher temperature within a few layers close to the bounding surface; the appearance of convexity and concavity in the concentration profile attributed to flow instability, and the constructive and destructive heterogeneous reactions at the bounding surface have distinct roles to modify the NF flow in the boundary layer.