乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:15:22Z","timestamp":1740147322196,"version":"3.37.3"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2018,11,26]],"date-time":"2018-11-26T00:00:00Z","timestamp":1543190400000},"content-version":"vor","delay-in-days":149,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Auton. Adapt. Syst."],"published-print":{"date-parts":[[2018,6,30]]},"abstract":"Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of Datacenter Networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, network traffic includes long-lived (elephant) and short-lived (mice) flows with partitioned\/aggregated traffic patterns. Although SDN-based approaches can efficiently allocate networking resources for such flows, the overhead due to network reconfiguration can be significant. With limited capacity of Ternary Content-Addressable Memory (TCAM) deployed in an OpenFlow enabled switch, it is crucial to determine which forwarding rules should remain in the flow table and which rules should be processed by the SDN controller in case of a table-miss on the SDN switch. This is needed in order to obtain the flow entries that satisfy the goal of reducing the long-term control plane overhead introduced between the controller and the switches. To achieve this goal, we propose a machine learning technique that utilizes two variations of Reinforcement Learning (RL) algorithms\u2014the first of which is a traditional RL-based algorithm, while the other is deep reinforcement learning-based. Emulation results using the RL algorithm show around 60% improvement in reducing the long-term control plane overhead and around 14% improvement in the table-hit ratio compared to the Multiple Bloom Filters (MBF) method, given a fixed size flow table of 4KB.<\/jats:p>","DOI":"10.1145\/3281032","type":"journal-article","created":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T13:18:59Z","timestamp":1543324739000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["SDN Flow Entry Management Using Reinforcement Learning"],"prefix":"10.1145","volume":"13","author":[{"given":"Ting-Yu","family":"Mu","sequence":"first","affiliation":[{"name":"Western Michigan University, Kalamazoo, MI"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0903-1204","authenticated-orcid":false,"given":"Ala","family":"Al-Fuqaha","sequence":"additional","affiliation":[{"name":"Western Michigan University, Kalamazoo, MI"}]},{"given":"Khaled","family":"Shuaib","sequence":"additional","affiliation":[{"name":"United Arab Emirates University, Al Ain - Abu Dhabi, United Arab Emirates"}]},{"given":"Farag M.","family":"Sallabi","sequence":"additional","affiliation":[{"name":"United Arab Emirates University, Al Ain - Abu Dhabi, United Arab Emirates"}]},{"given":"Junaid","family":"Qadir","sequence":"additional","affiliation":[{"name":"Information Technology University, Lahore Pakistan"}]}],"member":"320","published-online":{"date-parts":[[2018,11,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1282427.1282382"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1355734.1355746"},{"volume-title":"February","year":"2017","key":"e_1_2_1_3_1"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1644893.1644918"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1879141.1879175"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CloudNet.2013.6710553"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1868447.1868466"},{"volume-title":"February","year":"2017","key":"e_1_2_1_8_1"},{"volume-title":"Proceedings of the 2016 IEEE NetSoft Conference and Workshops (NetSoft). 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