Intelligent Cloud Workflow Management and Scheduling Method for Big Data Applications

Abstract

With the application and comprehensive development of big data, the need for effective research on cloud workflow management and scheduling is becoming more and more urgent. However, there are currently suitable methods for effective analysis. In order to find out how to effectively manage and schedule smart cloud workflows, the article studies big data from different aspects and draws the following conclusions: Compared with the original JStorm system, the average response time is shortened by up to 58.26%, and the average is shortened. 23.18%; CPU resource utilization increased by 17.96%, an average increase of 11.39%; memory utilization increased by 88.7%, an average increase of 71.16%. In optimizing the dynamic combination of web services, the overall performance of MOACO algorithm and CCA algorithm is better than GA algorithm, and the average performance of MOACO algorithm is better than CCA algorithm. The paper also proposes a cloud workflow scheduling strategy based on intelligent algorithms and adjusting the perceived cloud service resource combination strategy to realize two-layer scheduling of cloud workflow tasks. We have studied three representative intelligent algorithms (ACO algorithm, PSO algorithm and GA algorithm) and designed and improved them for scheduling optimization. It can be clearly seen that in the same scenario, in different test cases the optimal values of the different algorithms vary greatly. However, the optimal solution curve is substantially consistent with the trend of the mean curve.

Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization

“…Experimental evaluation is carried out using factors such as number of migrations, energy usage, resource optimization time and resource optimization accuracy with respect to number of user requests and iterations. To perform fair comparison simulation number of user requests and iterations are performed with the proposed TWLR-IQ method and two existing methods, Fuzzybased Multidimensional Resource Scheduling and Queuing Network (F-MRSQN) [1] and D-JStorm [2].…”

“…Initially we start by evaluating the resource optimization time across different methods, TWLR-IQ, F-MRSQN [1] and D-JStorm [2] utilizing random workload traces as it is the easiest type of workload to begin our experiments without affecting operation. Fig.…”

“…To address this issue, an Inter Quartile Range is introduced in the TWLR-IQ method that efficiently predicts overloading hosts, therefore, minimizing the number of migrations. Cloud workflow scheduling strategy employing intelligent algorithm called D-JStorm, a dynamic resource scheduling was proposed in [2]. In addition, a strategy for aligning the fusion of recognized cloud service resources to attain scheduling of cloud workflow tasks in a two-tier manner was designed.…”

Apply IOT technology to practice a pandemic prevention body temperature measurement system: A case study of response measures for COVID-19

“…Not only can cloud computing be used for search engines and email that were developed in the early stage of the Internet, but also can be use in all-round application services such as artificial intelligence and satellite navigation. 29…”

Cloud based Resource Scheduling Methodology for Data-Intensive Smart Cities and Industrial Applications

“…The information concentrated data intensive frameworks have become a functioning exploration domain because of the usage of large information in the current innovative technological world. Different researchers inspected the utilization of huge information innovation using the big data analytics in the field of medical care, biomedical and informatics [16,17]. In the last few decades, researchers in the do-main of healthcare are utilizing the enormous information-based approaches for imagining new procedures and revelations for resource allocation.…”