Cloud computing is an on-demand service that can be accessed by a user according to his requirements through the Internet. Multiple users can request any amount of services, so scheduling of those services is a crucial task in cloud computing. Scheduling is a way of assigning the work to a computer resource. We have multiple tasks at a time that are waiting to be allotted to multiple computer resources. Various optimization algorithms have been used to do task scheduling so that total execution cost is minimized. In this paper, we have implemented Jaya optimization algorithm for workflow scheduling and have compared it with four nature-inspired algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), honey bee, and cat swarm optimization (CSO), keeping the fitness function same for all of them using CloudSim. Previously, work has been done on PSO, GA, ACO, honey bee, and CSO using different criteria. The results are compared on the basis of execution cost and makespan of the algorithm on both an independent set of tasks and a set of tasks that follow a workflow schedule. Benchmark functions such as Montage, CyberShake, Inspiral, and Sipht are used for workflow scheduling. It has been observed that Jaya outperforms the other algorithms as it produces similar results in the least amount of time as it converges very quickly.

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A regression algorithm estimates the value of the target (response) as a function of the predictors for each case in the build data. These relationships between predictors and target are summarized in a model, which can then be applied to a different data set in which the target values are unknown.In this paper, we have discussed the formulation of linear regression technique, along with that linear regression algorithm have been designed, further test data are taken to prove the linear regression algorithm.

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