{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T12:59:32Z","timestamp":1740142772264,"version":"3.37.3"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Data center networks employ multipath topologies for delivering high bisection bandwidth. Load balancing through these multiple paths reduces latency and improves throughput. Network fabric-based schemes need customized hardware and cannot be implemented on commodity hardware. The existing end-host-based solutions have shown performance better than equal-cost multi-path (ECMP) but they use active probing and uneven congestion signal (e.g. coarse-grained RTT or ECN) that degrades performance especially in asymmetric topologies. Therefore, most of these end-host solutions cannot accurately sense congestion. End-to-end latency can effectively provide accurate path congestion information and smart probing with the help of congestion window can reduce network overhead. In this paper, we propose an edge-based congestion-aware load-balancing solution with smart probing. We combine ECN and end-to-end latency to get more accurate congestion metric and with probabilistic approach we avoid congestion at the best available path. Furthermore, we employ congestion window trend to perform smart probing that will reduce network overhead by performing probing only when required. The proposed scheme is implemented in network simulator ns-3 and flow completion time is taken as the key performance metric. The results show that the proposed scheme not only outperformed ECMP, Clove and LetFlow but also improved the performance over Hermes and CONGA at least 10\u201330% by employing (i) an accurate congestion signal that effectively detects congestion and asymmetries, and (ii) smart probing to reduce network overhead.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac135","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T09:37:32Z","timestamp":1666604252000},"page":"2908-2920","source":"Crossref","is-referenced-by-count":0,"title":["Edge-Based Congestion-Aware Datacenter Load Balancing with Smart Probing"],"prefix":"10.1093","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1948-0318","authenticated-orcid":false,"given":"Areeb Ahmed","family":"Bhutta","sequence":"first","affiliation":[{"name":"Virtual University of Pakistan Department of Computer Science and Information Technology, , Lahore 54000 , Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2060-7523","authenticated-orcid":false,"given":"Hasnain","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Virtual University of Pakistan Department of Computer Science and Information Technology, , Lahore 54000 , Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"2023121811453481700_ref1","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1126\/science.aba3758","article-title":"Recalibrating global data center energy-use estimates","volume":"367","author":"Masanet","year":"2020","journal-title":"Science"},{"key":"2023121811453481700_ref2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MNET.2013.6574661","article-title":"Survey on transport control in data center networks","volume":"27","author":"Zhang","year":"2013","journal-title":"IEEE Netw."},{"key":"2023121811453481700_ref3","first-page":"89","volume-title":"Proc. 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