https://doi.org/10.46719/npsc20202823

Kavitha Ganesan and Angelin Peace Preethi. The Possibility of Resource Scheduling For a Collaborative Cloud Computing Using Failure Workflow Control in IAAS Cloud. Neural Parallel and Scientific Computations 28 (2020), No. 2, 95-105.

ABSTRACT:
Cloud computing is a pledge that the implementation of low-cost workflow processes has become a prototype computing infrastructure that you supply resources. Despite the need for scientific workflows and access to community-level resources, they usually require a collaborative cloud environment that creates multiple data centers. Combining Artificial Bee Colony algorithm (ABC), and Hybrid Genetic Algorithm (HGA) geographically dispersed data center inputs and intermediate data sets can cause unbearable waiting periods that interrupt the execution of the addresses. Large-scale data-intensive scientific workflows and cloud workflow execution cost. In the proposed system, Failure-Aware Workflow Control Resource Scheduling (FAWCRS) is understanding the Importance and Simulation of FAWCRS Content Natures to Monitor the Importance of Diversity Analyze test results on the impact of tracking diversity. Failure traces and defect detection and reduction of average completion time to accurately achieve predictable accuracy, lost time, and an incorrect number of rearrangements. It effectively improves the availability, reliability, and quality of intermediate data in the Scientific Workflow. Solve this system planning problem on infrastructure on a Service (IaaS) platform. A collaborative planning algorithm is officially an efficient algorithm to reduce processing time all workflows within its deadline, ultimately reducing the time at each job processing. Describing the date and associated performance in the typical workflow in the simulation process is one of the best performance-related algorithms.

Keywords: Cloud computing, scientific workflow scheduling, collaborative cloud, infrastructure as a service, large scale scientific workflow