Theoretical Analysis of an Adaptive Periodic Multi Installment Scheduling With Result Retrieval for SAR Image Processing

Processing a large-scale Synthetic Aperture Radar (SAR) image dataset on a distributed computing infrastructure poses a challenging problem. Large-scale load distribution strategies like multi-installment scheduling (MIS) assume that the size of the result is negligible compared to the input workloa...

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Veröffentlicht in:IEEE transactions on parallel and distributed systems Jg. 33; H. 12; S. 4672 - 4683
Hauptverfasser: Chinnappan, Gokul Madathupalyam, Veeravalli, Bharadwaj
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Completion time
Computational modeling
Computer networks
Data processing
Datasets
Distributed databases
Distributed processing
front-end processors
heterogeneous cluster
Image processing
load distribution
Load modeling
multi-installment scheduling
Numerical methods
Particle swarm optimization
Processor scheduling
Program processors
Radar imaging
Radar polarimetry
Retrieval
Run time (computers)
SAR image
Schedules
Scheduling
Synthetic aperture radar
Topology
Virtual environments
ISSN:1045-9219, 1558-2183
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Zusammenfassung:Processing a large-scale Synthetic Aperture Radar (SAR) image dataset on a distributed computing infrastructure poses a challenging problem. Large-scale load distribution strategies like multi-installment scheduling (MIS) assume that the size of the result is negligible compared to the input workloads and hence ignore it in their design. Similarly, numerical methods like particle swarm optimization and their variants are not practical for real-time applications, given their run-time complexities. As both the results retrieval and completion time are crucial for SAR image data processing, in this article, we attempt to provide a thorough theoretical analysis of an adaptive MIS that includes the result retrieval phase. We use the periodic nature of the internal installments to keep the strategy simple and fine-tune the last installment to avoid any idle times in the processors. We derive a closed-form solution for the load fractions and hence, the overall processing time, schedule feasibility criteria, and certain other properties that lead to adaptive scheduling. Finally, we validate our theoretical findings through rigorous simulation studies using a loosely connected virtual machines (VMs) topology for the SAR dataset.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2022.3194542