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FOG and Cloud computing has established itself as an important component of the computational domain, providing a wide range of server proficiencies as virtualized scalable services. Cloud datacenters utilize VM consolidation to consolidate VMs to a smaller number of physical servers in order to enhance resource utilization and energy efficiency. Incorrect VM placement, on the other hand, might result in frequent VM migrations and continual on-off switching on physical machines (PMs), lowering service quality and increasing energy usage. To address this issue, we present a VM consolidation strategy based on Sailfish Optimization that is both effective and efficient. The performance evaluation of the proposed method observed a significant reduction in power consumption, SLA violation and execution time up to 25%, 22% and 6%, respectively, and increased resource utilization up to 17% as compared to existing models.

Cloud computing is a computing technology that is expeditiously evolving. Cloud is a type of distributed computing system that provides a scalable computational resource on demand including storage, processing power and applications as a service via Internet. Cloud computing, with the assistance of virtualization, allows for transparent data and service sharing across cloud users, as well as access to thousands of machines in a single event. Virtual machine (VM) allocation is a difficult job in virtualization that is governed as an important aspect of VM migration. This process is performed to discover the optimum way to place VMs on physical machines (PMs) since it has clear implications for resource usage, energy efficiency, and performance of several applications, among other things. Hence an efficient VM placement problem is required. This paper presents a VM allocation technique based on the elephant herd optimization scheme. The proposed method is evaluated using real-time workload traces and the empirical results show that the proposed method reduces energy consumption, and maximizes resource utilization when compared to the existing methods.

A schema database functions as a repository for interconnected data points, facilitating comprehension of data structures by organizing information into tables with rows and columns. These databases utilize established connections to arrange data, with attribute values linking related tuples. This integrated approach to data management and distributed processing enables schema databases to maintain models even when the working set size surpasses available RAM. However, challenges such as data quality, storage, scarcity of data science professionals, data validation, and sourcing from diverse origins persist. Notably, while schema databases excel at reviewing transactions, they often fall short in updating them effectively. To address these issues, a Chimp-based radial basis neural model (CbRBNM) is employed. Initially, the Schemaless database was considered and integrated into the Python system. Subsequently, compression functions were applied to both schema and schema-less databases to optimize relational data size by eliminating redundant files. Performance validation involved calculating compression parameters, with the proposed method achieving memory usage of 383.37Mb, a computation time of 0.455s, a training time of 167.5ms, and a compression rate of 5.60%. Extensive testing demonstrates that CbRBNM yields a favorable compression ratio and enables direct searching on compressed data, thereby enhancing query performance.

The Fog computing is rising as a dominant and modern computing model to deliver Internet of Things (IoT) computations, which is an addition to the cloud computing standard to get it probable to perform the IoT requests in the network of edge. In those above independent and dispersed environment, resource allocation is vital. Therefore, scheduling will be a test to enhance potency and allot resources properly to the tasks. This paper offers a distinct task scheduling algorithm in the fog computing environment that tries to depreciate the makespan and maximize resource utilization. This algorithm catalogues the task based on the mean Suffrage value. The suggested algorithm gives much resource utilization and diminishes makespan. Our offered algorithm is compared with different alive scheduling for performance investigation, and test results confirm that our algorithm has a more significant resource utilization rate and low makespan than other familiar algorithms.

Two groups of molecularly imprinted NO adsorbents were prepared by molecularly self-assembly procedures, in which methanoic acid, acetic acid, ethanedioic acid were used as templates; acrylic acid (AA) and acryl amide (AAM) were used as functional monomers, respectively. The pore size distribution and the special surface area of the adsorbents varied with different functional monomers and templates regularly. NO adsorption capacities all decreased with the increasingly molecular diameter of templates, and when the template was the same, the adsorbents in group 1 had better NO adsorption capacities than those in group 2 due to the stronger hydrogen bond interaction between the functional monomer and NO molecule. The adsorption stability was also tested and both of MIPa1 in group 1 and MIPb1 in group 2 could maintain steady adsorption capacity during 50 times of adsorption–desorption cycle.