Improved Power of Two Choices for Fat-Tree Routing
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AbstractThe fat-tree networking topology have gained prominence in various parallel and distributed systems such as high-performance computing clusters and data centers. To support high throughput and low latency applications, effective load-balancing schemes are in great demand. However, the commonly deployed scheme, equal-cost multipath, suffers from severe hash collisions that lead to poor performance. Recently, another realization of randomized load balancing, DRILL, has been proposed, which adopts the two-choice algorithm to achieve in-network local-to-switch load balancing. Although DRILL can well balance uplink traffic, it shows some limitations on alleviating downlink contentions. Motivated by this observation, we propose a thresholded two-choice (TTC) scheme, which modifies the two-choice algorithm such that it can balance both uplink and downlink traffic. To balance downlink traffic, TTC sets a default path for every source-destination pair using the D-mod-k scheme. The rationale for this is that D-mod-k minimizes the level of path collision over downlinks for any permutation in a fat-tree network. To balance uplink traffic, TTC makes dynamic path decisions using an algorithm that is based on the two-choice idea. To better leverage default paths in downlink load balancing, it is desirable that TTC routes the majority of traffic onto the default paths. To this end, we introduce a new thresholding mechanism to the two-choice algorithm, which contributes to a better overall performance. Our analysis shows that the introduced thresholding mechanism does not significantly affect the uplink load balancing performance. Moreover, our numerical study indicates that TTC can achieve a better system-wide performance than DRILL.
All Author(s) ListSuzhen Wang, Jingjing Luo, Wing Shing Wong
Journal nameIEEE Transactions on Network and Service Management
Year2018
Month12
Volume Number15
Issue Number4
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1706 - 1719
ISSN1932-4537
LanguagesEnglish-United States

Last updated on 2021-04-05 at 00:25