Resource Availability-Aware Advance Reservation for Parallel Jobs with Deadlines
Bo Li, Yijian Pei, Bin Shen, Hao Wu, Min He, Jundong Yang

TL;DR
This paper introduces a slot-based data structure and scheduling policies to efficiently allocate resources for parallel advance reservation jobs with deadlines in multiprocessor systems, improving acceptance rates and reducing job slowdown.
Contribution
It proposes a novel resource organization data structure and suite of scheduling policies tailored for parallel AR jobs with deadlines, validated through simulation.
Findings
PE-Worst-Fit algorithm achieves highest acceptance rate
First-Fit algorithm results in lowest average slowdown
Scheduling performance depends on job size, load, and flexibility
Abstract
Advance reservation is important to guarantee the quality of services of jobs by allowing exclusive access to resources over a defined time interval on resources. It is a challenge for the scheduler to organize available resources efficiently and to allocate them for parallel AR jobs with deadline constraint appropriately. This paper provides a slot-based data structure to organize available resources of multiprocessor systems in a way that enables efficient search and update operations, and formulates a suite of scheduling policies to allocate resources for dynamically arriving AR requests. The performance of the scheduling algorithms were investigated by simulations with different job sizes and durations, system loads and scheduling flexibilities. Simulation results show that job sizes and durations, system load and the flexibility of scheduling will impact the performance metrics of…
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Taxonomy
TopicsDistributed and Parallel Computing Systems · Parallel Computing and Optimization Techniques · Cloud Computing and Resource Management
