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Cloud computing provides a wide variety of services, from small to big businesses, to individual consumers. Cloud computing's features entice users to migrate their operations from traditional platforms to cloud platforms. In comparison to traditional systems, cloud computing has an extremely powerful processing capacity. Requests for resources are considered tasks in the cloud, and appropriate resources are allocated depending on user needs. However, owing to high demand and volume of requests, cloud struggles to allocate resources. Task schedulers are employed in cloud computing to address these issues. Various task scheduling methods have been presented in several research publications, and the quest for a better scheduling model continues. In this paper, a task scheduling method based on a hybrid optimization algorithm is presented, which effectively schedules jobs with the least amount of waiting time. In addition to these, other parameters, such as the overall production time, execution time, waiting time, efficiency, and utilization are included in this study. The simulation results show that the proposed scheduling method is superior to conventional Ant Colony and Particle Swarm Optimization-based scheduling algorithms in terms of performance. [ABSTRACT FROM AUTHOR]

An efficient scheduling of dependent-tasks over edge-cloud is the key for achieving better utilization of computational resources and timely completion of interdependent tasks for scientific as well as defence-oriented applications. Many applications comprise several tasks that are dependent in nature and are required to be executed in a specific sequence within a minimum span of time. To handle the execution of such dependent-tasks on distributed computational resources is a challenging problem; as the number of tasks increase, the solution space comprising different task-resource mapping sequences also increase exponentially and it is very difficult to find the near optimal solutions in the search space. In this paper, we focus on two strategies for obtaining optimal solutions for scheduling the multiple dependent-tasks with the specified sequence in a parallel and distributed environment. In the first approach, a hybrid mechanism is proposed to efficiently search the scheduling solution space for multiple dependent tasks. The idea is to first find a schedule by heuristic algorithms and use these as initial solutions in the search space to obtain better solutions using unsupervised machine learning methods. In the second approach, each task-graph is partitioned into different clusters of sub-tasks, where each partitioned cluster is mapped onto the same resource. This strategy reduces the idle times on the resources, wherever possible, in comparison with the first approach. The innovative part of the proposed approaches is to schedule the multiple dependent-tasks in a parallel fashion rather than scheduling in sequential manner. The results show that the schedules obtained by our proposed approaches minimize the total execution time (TET) significantly as compared to other approaches considered in our research study. [ABSTRACT FROM AUTHOR]

This paper presents a software library as a research and educational tool for Multi-Skill Resource-Constrained Scheduling Problem. The following useful tools have been implemented in Java: instance Generator, solution validator, solution visualizer and example solvers: Greedy algorithm and Genetic Algorithm. All tools are supported by iMOPSE dataset which consists of 36 instances and additional 'small' 6 instances for educational purpose. In the paper, three test studies are described: (1) educational use of 6 'small' instances, (2) optimization of cost or duration of a schedule, and (3) simple bicritieria optimization of cost/duration of a final schedule. All described tools/examples are freely published on iMOPSE homepage. [ABSTRACT FROM AUTHOR]