{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:12:38Z","timestamp":1750219958582,"version":"3.41.0"},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMETRICS Perform. Eval. Rev."],"published-print":{"date-parts":[[2023,4,26]]},"abstract":"<jats:p>The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedulers have been proposed in recent years that outperform alternative approaches by providing scalable and effective resource management for dynamic workloads. However, state-of-the-art schedulers rely on advanced DNNs with high computational requirements, implying high scheduling costs. In non-stationary contexts, the most sophisticated schedulers may not always be required, and it may be sufficient to rely on low-cost schedulers to temporarily save operational costs. In this work, we propose MetaNet, a surrogate model that predicts the operational costs and scheduling overheads of a large number of DNNbased schedulers and chooses one on-the-fly to jointly optimize job scheduling and execution costs. This facilitates improvements in execution costs, energy usage and service level agreement violations of up to 11%, 43% and 13% compared to the state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3595244.3595255","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T10:27:14Z","timestamp":1682591234000},"page":"29-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning to Dynamically Select Cost Optimal Schedulers in Cloud Computing Environments"],"prefix":"10.1145","volume":"50","author":[{"given":"Shreshth","family":"Tuli","sequence":"first","affiliation":[{"name":"Imperial College London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuliano","family":"Casale","sequence":"additional","affiliation":[{"name":"Imperial College London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas R.","family":"Jennings","sequence":"additional","affiliation":[{"name":"Imperial College London &amp; Loughborough University, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.4018\/IJCAC.2020040101"},{"issue":"1","key":"e_1_2_1_2_1","article-title":"IoT-based big data storage systems in cloud computing: perspectives and challenges","volume":"4","author":"Hongming Cai","year":"2016","unstructured":"Hongming Cai et al . \" IoT-based big data storage systems in cloud computing: perspectives and challenges \". 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In: Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies. 2021, pp. 154--167."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-018-2868-6"},{"key":"e_1_2_1_9_1","volume-title":"AAAI.","author":"Stuckey Peter J","year":"2020","unstructured":"Peter J Stuckey \" Dynamic programming for predict+ optimise \". In: AAAI. Vol. 34 . 02. 2020 . Peter J Stuckey et al. \"Dynamic programming for predict+ optimise\". In: AAAI. Vol. 34. 02. 2020."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3136672"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3087349"},{"key":"e_1_2_1_12_1","volume-title":"AAAI.","author":"Wilder Bryan","year":"2019","unstructured":"Bryan Wilder , Bistra Dilkina , and Milind Tambe . \" Melding the data-decisions pipeline : Decision-focused learning for combinatorial optimization \". In: AAAI. Vol. 33 . 01. 2019 . 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