ABSTRACT With the increasing compute and data demands of intricate scientific applications, cloud computing has emerged as a key solution for executing scientific workflows while adhering to Quality of Service (QoS) parameters. Developing an efficient and cost‐effective solution, especially for large‐scale applications, continues to be an immensely challenging task. To handle this challenge, this research presents a scheduling algorithm, an opposition‐based chaotic enhanced African vulture workflow scheduler (OCEAVWS), to reduce the makespan and financial cost incurred in executing scientific workflows. The proposed approach initially employs opposition‐based learning (OBL) techniques to create a high‐quality initial population, utilizes a logistic chaotic map that assists in selecting phases in the African vulture optimization algorithm (AVOA), and is further enhanced by dimension learning hunting (DLH). The strength of the proposed method is assessed on the WorkflowSim tool on four different scientific workflows. The experimental results show substantial improvements, including an 18.03% reduction in makespan and an 8.07% decrease in financial costs. The evaluation metrics indicate that our approach achieves lower average relative deviation index (ARDI) values in all cases, lower S‐metric values in 83% of scenarios, and higher hypervolume in 92% of the evaluated scenarios. Statistical validation using Wilcoxon and Friedman tests confirms that OCEAVWS significantly outperforms five other state‐of‐the‐art multi‐objective optimization approaches, demonstrating its effectiveness in scientific workflow scheduling.

Summary A difficult problem in the service‐oriented computing paradigm is improving task scheduler policy or resource provisioning.In order to increase the performance of cloud applications, this article primarily focuses on tasks for resource mapping policy optimization. With the aim of reducing makespan and execution overhead and increasing the average resource utilization, we suggested an efficient independent task scheduler employing supervised neural networks in this paper. The suggested ANN‐based scheduler uses the status of the cloud environment and incoming tasks as inputs to determine the optimal computing resource for a given assignment as a result that assembles our goal. We proposed a novel algorithm in this paper that uses a hybrid methodology based on a swarm intelligence algorithm (PSO) in combination with a machine learning technique (ANN). PSO is used to prepare the train and test dataset for the neural network. Results clearly state that suggested work achieves significant improvement to considered algorithms in makespan (45%–55%), average VM utilization (15%–20%), and execution overhead(20%–30%).

Abstract Cloud computing is a promising platform for executing scientific workflow applications. Commonly, the scientific workflow applications are complex in size and computation intensive. Task scheduling in clouds is a familiar NP‐complete problem. Efficient workflow application scheduling is critical for meeting multi‐objectives in cloud environments. By reason of its pivotal role, this problem has been widely studied, and many approaches have been developed. Most of the algorithms focus on schedule makespan and execution cost. The cloud data centers consume a large amount of energy while running workflow applications due to a lack of efficient scheduling algorithms to perform the task to a virtual machine (VM) mapping. Hence, there is a significant need to address energy utilization in the cloud data center (CDC) for two reasons such as data center operational cost optimization and to improve the environment. This article presents an energy and cost‐aware scheduling (ECWS) approach with the objectives of decreasing energy utilization, execution cost, and maximizing utilization of the resources. The ECWS algorithm is a heterogeneous earliest finish time (HEFT) based energy‐efficient heuristic for cloud scheduler. The ECWS algorithm includes three sub‐algorithms like RE calculation, RE Threshold selection, and slack algorithm. The effectiveness of the proposed algorithm is evaluated on the WorkflowSim tool using distinct workloads from various scientific areas. The experimental outcome exhibit that the proposed algorithm achieved significant energy conservation, maximized resource utilization, and cost‐saving when compared with related well‐known algorithms.

With the unprecedented growth of Cloud Computing, data centers around the world have increased exponentially. The energy utilization in these data centers is becoming a concern, so the need for energy-efficient algorithms in the cloud has been on the top agenda for quite a while. Cloud providers use different energy management strategies to minimize energy utilization and to maximize ROI (Return On Investment) such as energy-efficient virtual machine (VM) placement. We have taken the problem for VM consolidation and worked on it for an energy-efficient algorithm. VM Consolidation uses live migrations of VMs during the execution of cloudlets so that underloaded physical servers can be switched off by transferring those VMs to other physical machines. VM consolidation in the cloud environment is a proven NP-hard problem. We have used Water Wave optimization (WWO) which is a meta-heuristic algorithm. Original WWO proposed for continuous space and so we have modified the parameters of the algorithm to apply for our problem of VM consolidation. Our approach produces a near-optimal solution using an objective function that minimizes energy consumption and increases the number of switched off servers.