Application-aware deadline constraint job scheduling mechanism on large-scale computational grid

Tang, Xiaoyong; Liao, Xiaoyi

doi:10.1371/journal.pone.0207596

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…An Application-aware Deadline Constraint Job Scheduling Mechanism On A Large-Scale Computational Grid was proposed to overcome earlier models’ challenges due to the heterogeneity, dynamics of resources, and diversity of application requirements [ 34 ]. Since the scheduling task had remained a challenging task, a Hybrid Heuristic of Variable Neighborhood Descent and Great Deluge Algorithm for efficient task scheduling in grid computing was proposed.…”

Section: Related Workmentioning

confidence: 99%

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Yousif

Alqhtani

Bashir

et al. 2022

Sensors

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The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

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Section: Related Workmentioning

confidence: 99%

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Yousif

Alqhtani

Bashir

et al. 2022

Sensors

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show abstract

“…Task scheduling is a challenging issue in high performance computing 27,46‐50 . For data‐dependent task graph, each task has some input data or output data 4,5 .…”

Section: Related Workmentioning

confidence: 99%

Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

Deng

Shen

Cao

et al. 2021

Concurrency and Computation

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Energy consumption has become one of the main bottlenecks that limit the performance improvement of heterogeneous multiprocessor systems. In a heterogeneous distributed shared‐memory multiprocessor system (HDSMS), each processor can access all the memories, and each data can be stored in different memories. This article aims at addressing the problem of task scheduling and data allocation (TSDA) on HDSMS. To minimize the total energy consumption under a time constraint for TSDA, we propose two algorithms: the extended tree assignment for task scheduling incorporating data allocation (ETATS‐DA) and critical path task scheduling and data allocation (CPTSDA). The ETATS‐DA algorithm first utilizes the extended tree assignment to search the near optimal solution for task assignment, and then allocates data to memory based on the result of assignment. The CPTSDA algorithm considers TSDA jointly on a critical path simultaneously. Our proposed algorithms perform coherent data allocation under the consideration of best task scheduling by running two different heuristic strategies, respectively, and taking the best result as the final result. We conduct a large number of simulation experiments to test the performance of our algorithms, and the results validate the higher performance of our methods compared with the state‐of‐the‐art algorithms.

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“…In recent years, a large number of scheduling algorithms that combine time and other system qualities of service (QoS), such as reliability, budget, deadline, energy consumption, have been emerged in the field of heterogeneous computing systems [16]- [19], [34], [39], [40]. Dogan and Özgüner [40] analyzed the task execution reliability on heterogeneous computing systems, and combined this into applications dynamic level to implemented the scheduling algorithm.…”

Section: Related Workmentioning

confidence: 99%

CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

Tang

2020

IEEE Access

Self Cite

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Nowadays, heterogeneous computing systems have proven to be a good solution for processing computation intensive high-performance applications. The main challenges for such large-scale systems are energy consumption, computing node CPU-GPU utilization dynamic variability, and so on. In response to these challenges, this study first provides heterogeneous computing systems architecture and parallel application job model. Then, we build system computing node CPU-GPU utilization model and analyze job execution energy consumption. We also deduce the optimal CPU-GPU utilization and job deadline scheduling constraint. Third, we propose a systems CPU-GPU utilization aware and energy-efficient heuristic greedy strategy (UEJS) to solve this job scheduling problem. To improve the algorithm global optimization ability, we design a hybrid particle swarm optimization algorithm (H-PSO), which incorporates the heuristic greedy strategy into the bio-inspired search optimization technique. The rigorous experimental results clearly demonstrate that our proposed H-PSO outperforms heuristic greedy strategy, Max-EAMin, and Genetic Algorithm in terms of the average energy consumption of jobs and system job rejection rate. In particular, H-PSO is better than UEJS by 36.5%, Max-EAMin by 36.3%, and GA by 46.7% in term of the job average energy consumption for heterogeneous system with high workload.

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Application-aware deadline constraint job scheduling mechanism on large-scale computational grid

Cited by 11 publications

References 35 publications

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

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