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Cloud Computing Environment (CCE) has gained considerable attention in recent years because of scalability, flexibility, and cost-effectiveness. Workflow scheduling, a critical aspect of CCE, involves assigning tasks of a workflow to suitable resources to optimize various performance metrics. Load balancing plays an important role in achieving efficient resource utilization and reducing execution time in workflow scheduling. There are many scheduling algorithms are developed and Min-Min is out of them that uses independent tasks. However, the original Min-Min heuristic does not consider the load distribution among resources, which can lead to imbalanced resource utilization and increased execution time.To address this limitation, we introduce a modified Min-Min heuristic that incorporates load-balancing principles. Taking into consideration both task completion time and resource load, the method aims to achieve optimal load distribution and minimize the overall execution time of the workflow.To evaluate the effectiveness of the proposed load-balancing method, extensive simulations are performed using benchmark workflow datasets such as randomly generated workflows and Montage workflows. The results show that the modified Min-Min heuristic outperforms as compared to heuristics HEFT and PETS in terms of load balancing, makespan, speedup, efficiency,and resource utilization. The proposed method achieves more balanced resource allocation, reduces the completion time of the workflow, and improves overall system performance. The present study contributes to the area of workflow scheduling in CCE by presenting a load-balancing method that enhances the efficiency of resource allocation. The findings emphasize the importance of considering load-balancing principles in task scheduling to optimize performance in cloud computing environments. The proposed method can serve as a valuable tool for practitioners and researchers involved in workflow scheduling in CCE, offering improved resource utilization and reduced execution time. [ABSTRACT FROM AUTHOR]

At present, the cloud computing environment (CCE) has emerged as one of the significant technologies in communication, computing, and the Internet. It facilitates on-demand services of different types based on pay-per-use access such as platforms, applications and infrastructure. Because of its growing reputation, the massive requests need to be served in an efficient way which gives the researcher a challenging problem known as task scheduling. These requests are handled by method of efficient allocation of resources. In the process of resource allocation, task scheduling is accomplished where there is a dependency between tasks, which is a Directed Acyclic Graph (DAG) scheduling. DAG is one of the most important scheduling due to wide range of its applicable in different areas such as environmental technology, resources, and energy optimization. NP-complete is a renowned concern, so various models deals with NP-complete that have been suggested in the literature. However, as the Quality of Service (QoS)-aware services in the CCEplatform have turned into an attractive and prevalent way to provide computing resources emerges as a novel critical issue. Therefore, the key aim of this manuscript is to develop a novel DAG scheduling model for optimizing the QoS parameters in the CCEplatform and validation of this can be done with the help of extensive simulation technique. Each simulated result is compared with the existing results, and it is found that newly developed algorithm performs better in comparison to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art. [ABSTRACT FROM AUTHOR]

The major objective of any task-scheduling algorithm in parallel computing is to minimize the overall scheduling length. In this research paper, we present a task-scheduling algorithm in parallel computing called the Critical Path and b-level Based Task Scheduling algorithm. This algorithm is based on a new attribute, which is called the CPB attribute, which is based on two wellknown priority attributes: Critical Path and B-Level. This algorithm uses entry tasks as duplicates in all of the available processors. The major objective of any task-scheduling algorithm is to minimize the overall scheduling length. Here, an application program in task scheduling is represented by a directed acyclic graph. We consider all processors to be homogenous in nature and fully connected. The Critical Path and b-level Based Task Scheduling algorithm gives a minimum scheduling length when compared to heuristic task-scheduling algorithms like the HLFET, MCP, DLS, and ETF. We have also done an analysis of the proposed algorithm and heuristic algorithms based on other priority attributes: Speedup, Efficiency, Scheduling Length Ratio, Load Balancing, and Cost. [ABSTRACT FROM AUTHOR]