An edge priority‐based clustering algorithm for multiprocessor environments

Abstract

Summary In multiprocessor environments, the scheduling algorithms play a significant role in maximizing system performance. In this paper, we propose a clustering‐based task scheduling algorithm called Edge Priority Scheduling (EPS) for multiprocessor environments. The proposed algorithm extends the idea of edge zeroing heuristic and uses the concept of edge priority to minimize the makespan of the task graph. The complexity of the EPS algorithm is O(|V||E|(|V| + |E|)), where |E| represents the number of edges and |V| denotes the number of nodes in the task graph. The experiments are performed for random task graphs and the task graphs generated from some representative real‐world applications such as Gaussian Elimination and Fast Fourier Transform. The performance of the EPS algorithm is compared with six well‐known algorithms such as EZ (Edge Zeroing), LC (Linear Clustering), CPPS (Cluster Pair Priority Scheduling), DCCL (Dynamic Computation Communication Load), RDCC (Randomized Dynamic Computation Communication), and LOCAL. The results show that the EPS algorithm outperforms the compared algorithms in terms of the normalized schedule length and speedup.

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

“…Whenever a fault occurs in the system, the model reschedules the task in nodes having the highest reliability instead of providing a replica of the task to all nodes. Resubmission in the presented models helps in reducing the makespan 69 and resource usage. Patra et al 70 projected an adaptive fault‐tolerant model based on replication and resubmission in cloud computing.…”

A survey on energy‐efficient workflow scheduling algorithms in cloud computing

“…Further, if a DAG has more than one entry and exit task, then a dummy entry task is added at the start and similarly, a dummy exit task at the end is added to the DAG to convert it into a DAG with only one entry and exit task. 35,36…”

“…From the graph, it can be seen that if a task has no parent task, then it is called an entry task (here task $$$ {t}_1 $$$), whereas a task with no child task is called an exit task (here task $$$ {t}_7 $$$). Further, if a DAG has more than one entry and exit task, then a dummy entry task is added at the start and similarly, a dummy exit task at the end is added to the DAG to convert it into a DAG with only one entry and exit task 35,36 …”

A Fault‐tolerant model for tuple space coordination in distributed environments

“…Nowadays, distributed systems perform complex computations in various domains such as astronomy, weather forecasting, satellite data analysis, bioinformatics applications, social computing, and medical data analysis 1 . There are many variants of distributed systems like multiprocessor computing system, 2 cloud computing, 3 fog computing, 4 edge computing, 5 cluster system, 6 and heterogeneous computing system 7 . The distributed system may experience different failures and act as a fault‐tolerant system that recovers from partial failures and continues operating without interruption.…”