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This paper presents an algorithm called Failure-Aware Workflow Scheduling (FAWS). The proposed algorithm discussed in this paper schedules parallel applications on homogeneous systems without sacrificing the two conflicting objectives: reliability and makespan. The proposed algorithm handles unexpected failure causes rescheduling of the failed task to available resources. In order to analyse the performance of the FAWS algorithm, it will be compared with the popular scheduling algorithm namely Heterogeneous Earliest Finish Time (or HEFT) and Critical Path (CP). A simulation-driven analysis based on realistic workflow application was demonstrated using DAG graph as a continuation of the Layered Workflow Scheduling Algorithm (LWFS). The FAWS algorithm aims to minimize the makespan, increases reliability and therefore boosts the performance of the whole system. A workflow generator was developed to generate large task graphs randomly and scheduled the parallel applications. Based on the simulation results, the proposed algorithm has improved the overall workflow scheduling effectiveness in comparison with existing algorithms.

© Springer Nature Singapore Pte Ltd. 2019. Big data has been touted as the next frontier for innovation, competition, and productivity growth. The promising and rewarding benefits of big data have attracted government agencies and businesses worldwide to extract valuable new insights from big data by reengineering their business and information processes. Although big data can provide valuable information to make intelligent decisions and obtain insights, it comes with many new challenges that must be addressed in order to fully leverage the opportunities provided by Big Data. This paper provides an overview of the opportunities and challenges of big workflow within the context of workflow applications and systems. By applying big workflow in decision making process, organizations are able to visualize data differently and become more valuable.

In the last few years, workflows are becoming richer and more complex. Workflow scheduling management system to be robust, flexible with multicriteria scheduling algorithms. It needs to satisfy the Quality of Service (QoS) parameters. However, QoS parameters and workflow system objectives are often contradictory. In our analysis, we derived an efficient strategy to minimize the overall processing time for scheduling workflows modelled by using Directed Acyclic Graph (DAG). We studied the problem of workflow scheduling that lead to optimizing makespan and reliability. The proposed algorithm handles unsuccessful job execution or resource failure by dynamically scheduling workflows to available resources. Based on the experiments results, our proposed Failure-Aware Workflow Scheduling (FAWS) Algorithm can significantly optimize the makespan and minimize the reliability by rescheduling the failed task to the unused resources. The effectiveness of the FAWS algorithm was validated based on a simulation-driven analysis based on the workflow application.

Workflow applications require workflow processing in which workflow tasks are processed based on their dependencies. With the emergency of complex distributed systems such as grids and clouds, efficient workflow scheduling (WFS) algorithms have become the core components of the workflow management systems (WfMS). Thus, WFS that allocates each task in the workflow to a relevant resource with the aim of improving system performance and end user satisfaction is fundamentally important. In this paper, we propose a new workflow scheduling algorithm called Layered Workflow Scheduling Algorithm (LWFS) for scheduling workflow applications. We studied the efficacy of the LWFS scheduling experimentally and compared its performance with approaches including Improved Critical Path using Descendant Prediction (ICPDP), Highest Level First with Estimated Time (HLFET), Modified Critical Path (MCP) and Earliest Time First (ETF). The results of the experiments show that the proposed approach outperforms other approaches.

Growing evidence shows that in obtaining high performance, a well-managed time-constrained workflow scheduling is needed. Efficient workflow scheduling is critical for achieving high performance especially in heterogeneous computing system. However, it is a great challenge to improve performance and to optimize several objectives simultaneously. We propose a workflow scheduling algorithm that minimizes the makespan of the workflow application modeled by a Directed Acyclic Graph (DAG). The new proposed scheduling algorithm is named Multi Dependency Joint (MDJ) Algorithm. The performance of MDJ is compared with existing algorithms such as, Highest Level First with Estimated Time (HLFET), Modified Critical Path (MCP) and Earliest Time First (ETF). As a result, the experiments show that our proposed MDJ algorithm outperforms HLEFT, MCP, and EFT with a 7% lower overall completion time.