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Modeling Sparse Store-and-Forward Bulk Data Transfers in Inter-Datacenter Networks With Multiple Congested Links | IEEE Journals & Magazine | IEEE Xplore

Modeling Sparse Store-and-Forward Bulk Data Transfers in Inter-Datacenter Networks With Multiple Congested Links


Abstract:

The increasing demand of bulk data transfer imposes significant challenges in inter-datacenter networks that span multiple domains. Storing data temporarily at domain bor...Show More

Abstract:

The increasing demand of bulk data transfer imposes significant challenges in inter-datacenter networks that span multiple domains. Storing data temporarily at domain borders and forward it at later off-peak hours (SnF) can reduce congestion and improve network utilization. However, performance gains from SnF transfer still requires further quantitative study. In this article, we model deadline-constrained SnF transfers in networks with congested inter-domain links, where traffic has slightly varying peak hour and diurnal patterns. A closed-form estimation for the two-links case is derived to quantify the performance gain of intermediate storage (IS) and edge storage (ES). Our study shows that if bulk data transfer requests arrive with equal probability in a day, increase the allowed waiting time will increase the successful transfer probability linearly. Increasing the aggregated network load, defined as the summed duration of the background traffic peak-hour and data transfer, will result in linear decrease in successful transfer probability, when the network is moderately loaded. Our research also shows that IS has significant advantages over ES under heavy network load, and the maximum benefit occurs when the allowed waiting time is two times the aggregated network load.
Published in: IEEE Transactions on Cloud Computing ( Volume: 11, Issue: 3, 01 July-Sept. 2023)
Page(s): 2725 - 2738
Date of Publication: 01 December 2022

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