OptiDJS+: A Next-Generation Enhanced Dynamic Johnson Sequencing Algorithm for Efficient Resource Scheduling in Distributed Overloading within Cloud Computing Environment

Abstract

The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, we introduce OptiDJS+, a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. It accomplishes this by utilizing real-time monitoring and machine learning. It takes factors like load balance and make-up into account. We outline the design philosophies, implementation specifics, and empirical assessments of OptiDJS+ in this work. Through rigorous testing and benchmarking against cutting-edge scheduling algorithms, we show the better performance and resilience of OptiDJS+ in terms of reaction times, resource utilization, and scalability. The outcomes underline its success in reducing resource contention and raising service quality generally in cloud computing environments. In contexts where there is distributed overloading, OptiDJS+ offers a significant advancement in the search for effective resource scheduling solutions. Its versatility, optimization skills, and improved decision-making procedures make it a viable tool for tackling the resource allocation issues that cloud service providers and consumers encounter daily. We think that OptiDJS+ opens the way for more dependable and effective cloud computing ecosystems, assisting in the full realization of cloud technologies’ promises across a range of application areas. In order to use the OptiDJS+ Johnson sequencing algorithm for cloud computing task scheduling, we provide a two-step procedure. After examining the links between the jobs, we generate a Gantt chart. The Gantt chart graph is then changed into a two-machine OptiDJS+ Johnson sequencing problem by assigning tasks to servers. The OptiDJS+ dynamic Johnson sequencing approach is then used to minimize the time span and find the best sequence of operations on each server. Through extensive simulations and testing, we evaluate the performance of our proposed OptiDJS+ dynamic Johnson sequencing approach with two servers to that of current scheduling techniques. The results demonstrate that our technique greatly improves performance in terms of makespan reduction and resource utilization. The recommended approach also demonstrates its ability to scale and is effective at resolving challenging work scheduling problems in cloud computing environments.

Empowering Sustainable Industrial and Service Systems through AI-Enhanced Cloud Resource Optimization

“…To further substantiate our claims, we compare our methods with existing studies as shown in Table 3. This table contrasts our AI-driven approach with other methodologies, such as the human-monitored trial-error approach proposed by Lee et al (2017) [2] and the dynamic Johnson sequencing algorithm by Banerjee et al (2023) [3], emphasizing the unique benefits of real-time AI optimization without human intervention.…”

No idle flow shop scheduling models for optimization of machine rental costs with processing and separated setup times

“…Banerjee et al (26) proposed the OptiDJS+ dynamic Johnson sequencing strategy, which utilises two servers, as an alternative to traditional scheduling techniques. The authors demonstrated that the proposed approach significantly enhances the reduction of the makespan and increases resource utilisation.…”