Intelligent cloud workflow management and scheduling method for big data applications

Abstract

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

Scheduling of workflow jobs based on twostep clustering and lowest job weight

“…Compared DJS to job scheduling based on Modified Harris Hawks Optimization and Simulated Annealing Algorithm, 65 the analysis found that the proposed algorithm in Reference 65 needs intensive resources for searching to select the suitable job for a large scheduling computing system. While, Hu et al 66 proposed a workflow model based on three optimization algorithms, namely ant colony optimization, practical swarm optimization, and genetic optimization. The results found that different results can be achieved from different optimization algorithms for the same scenario.…”

“…The results found that different results can be achieved from different optimization algorithms for the same scenario. Compared the DJS algorithm to the model, 66 the analysis showed that the scheduler showed intensive memory utilization to find the optimal job.…”

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 allround application services such as artificial intelligence and satellite navigation. 29 With the advent of cloud computing and the improvement of hardware and software, Internet service providers have divided their services into three categories: Cloud deployment can be broadly divided into three types: public, private, and hybrid. Public cloud builds an open cloud service platform for third-party cloud providers.…”

Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems

“…These aspects are needed to be effectually considered for effective resource allocation and task scheduling which is very important for high performance computing and also plays a vital role in embedded systems (Min-Allah et al, 2020). The basic task scheduling methodology for cloud computing process in depicted in Figure 1 (Hu et al, 2020).…”