Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling.

In recent years, the world has seen incremental growth in online activities owing to which the volume of data in cloud servers has also been increasing exponentially. With rapidly increasing data, load on cloud servers has increased in the cloud computing environment. With rapidly evolving technology, various cloud-based systems were developed to enhance the user experience. But, the increased online activities around the globe have also increased data load on the cloud-based systems. To maintain the efficiency and performance of the applications hosted in cloud servers, task scheduling has become very important. The task scheduling process helps in reducing the makespan time and average cost by scheduling the tasks to virtual machines (VMs). The task scheduling depends on assigning tasks to VMs to process the incoming tasks. The task scheduling should follow some algorithm for assigning tasks to VMs. Many researchers have proposed different scheduling algorithms for task scheduling in the cloud computing environment. In this article, an advanced form of the shuffled frog optimization algorithm, which works on the nature and behavior of frogs searching for food, has been proposed. The authors have introduced a new algorithm to shuffle the position of frogs in memeplex to obtain the best result. By using this optimization technique, the cost function of the central processing unit, makespan, and fitness function were calculated. The fitness function is the sum of the budget cost function and the makespan time. The proposed method helps in reducing the makespan time as well as the average cost by scheduling the tasks to VMs effectively. Finally, the performance of the proposed advanced shuffled frog optimization method is compared with existing task scheduling methods such as whale optimization-based scheduler (W-Scheduler), sliced particle swarm optimization (SPSO-SA), inverted ant colony optimization algorithm, and static learning particle swarm optimization (SLPSO-SA) in terms of average cost and metric makespan. Experimentally, it was concluded that the proposed advanced frog optimization algorithm can schedule tasks to the VMs more effectively as compared with other scheduling methods with a makespan of 6, average cost of 4, and fitness of 10.

COVID-19 driven changes in the air quality; a study of major cities in the Indian state of Uttar Pradesh.

Amid the COVID-19 pandemic, there has been an unprecedented cessation of outdoor anthropogenic activities leading to a significant improvement of the environment across the world. However, the positive impacts on the environment are not expected to last long as countries have started to gradually come out of lockdown and engage in aggressive measures to regain the pre-COVID-19 levels of economic activity. The present study provides for an assessment of air quality changes during the period of lockdown and unlocking across 9 major cities in the Indian state of Uttar Pradesh, including three cities (Ghaziabad, Noida, and Greater Noida) in the national capital region, which have frequently been included among the most polluted cities in the world. The pollutant load in a vertical column of air during March-July 2020 has been analyzed and compared with the corresponding period's pollution load in 2019. In addition, a detailed analysis of the ground-level changes in pollution load for Ghaziabad, Noida, and Greater Noida is also presented, along with the changes in local meteorology. A significant reduction in the total column density of NO2, CO and ground-level pollution load of PM10, PM2.5, NO2, and SO2 have been observed. In contrast, an increase in total column density of SO2 across all the cities (except Kanpur) and ground-level concentration of CO (in Noida and Greater Noida) and O3 (in Noida) was evident. The improvement in air quality (with respect to particulate matter) can primarily be attributed to the restrictions on construction and demolition activities, reduced re-suspension of roadside dust, and the restrictions on the movement of vehicles. A significant decline in the average summer temperature was recorded, and it can plausibly be attributed to lower radiative forcing due to reduced pollutant load in the atmosphere.