2022/12/19 by Atharva Tekawade, Tekawade, Atharva, Suman Banerjee +1
Computer Science · Decision Sciences · #Cloud Computing and Resource Management #Computers and Society (cs.CY) #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2212.09274
openalex publication_date 2022/12/19 · openalex created_date 2023/01/04 · openalex updated_date 2026/07/28
Many scientific workflows can be modeled as a Directed Acyclic Graph (henceforth mentioned as DAG) where the nodes represent individual tasks, and the directed edges represent data and control flow dependency between two tasks. Due to the large volume of data, multiprocessor systems are often used to execute these workflows. Hence, scheduling the tasks of a workflow to achieve certain goals (such as minimizing the makespan, energy, or maximizing reliability, processor utilization, etc.) remains an active area of research in embedded systems. In this paper, we propose a workflow scheduling algorithm to minimize the makespan and energy for a given reliability constraint. If the reliability constraint is higher, we further propose Energy Aware Fault Tolerant Scheduling (henceforth mentioned as EAFTS) based on active replication. Additionally, given that the allocation of task nodes to processors is known, we develop a frequency allocation algorithm that assigns frequencies to the processors. Mathematically we show that our algorithms can work for any satisfiable reliability constraint. We analyze the proposed solution approaches to understand their time requirements. Experiments with real-world Workflows show that our algorithms, MERT and EAFTS, outperform the state-of-art approaches. In particular, we observe that MERT gives 3.12% lesser energy consumption and 14.14% lesser makespan on average. In the fault-tolerant setting, our method EAFTS gives 11.11% lesser energy consumption on average when compared with the state-of-art approaches.