{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T13:32:38Z","timestamp":1730208758463,"version":"3.28.0"},"reference-count":29,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1109\/cloud.2018.00058","type":"proceedings-article","created":{"date-parts":[[2018,9,21]],"date-time":"2018-09-21T17:13:13Z","timestamp":1537549993000},"page":"409-416","source":"Crossref","is-referenced-by-count":38,"title":["Micky: A Cheaper Alternative for Selecting Cloud Instances"],"prefix":"10.1109","author":[{"given":"Chin-Jung","family":"Hsu","sequence":"first","affiliation":[]},{"given":"Vivek","family":"Nair","sequence":"additional","affiliation":[]},{"given":"Tim","family":"Menzies","sequence":"additional","affiliation":[]},{"given":"Vincent","family":"Freeh","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"journal-title":"Cowles Foundation Discussion Paper No 1551","article-title":"Bandit problems","year":"2006","author":"bergemann","key":"ref10"},{"journal-title":"ICML Tutorial on bandits","article-title":"Introduction to bandits: Algorithms and theory","year":"2011","author":"audibert","key":"ref11"},{"journal-title":"ICDCS","article-title":"Load prediction for energy-aware scheduling for cloud computing platforms","year":"2017","author":"dambreville","key":"ref12"},{"journal-title":"NSDI","article-title":"Pytheas: Enabling data-driven quality of experience optimization using group-based exploration-exploitation","year":"2017","author":"jiang","key":"ref13"},{"journal-title":"Aws ec2 document history","year":"0","key":"ref14"},{"journal-title":"WWW","article-title":"BOAT: Building Auto-Tuners with Structured Bayesian Optimization","year":"2017","author":"dalibard","key":"ref15"},{"journal-title":"Amazon Web Services","year":"0","key":"ref16"},{"journal-title":"SOSP","article-title":"Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms","year":"2017","author":"cortez","key":"ref17"},{"journal-title":"Open Performance Dataset","year":"0","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1613\/jair.301"},{"journal-title":"FSE","article-title":"Finding near-optimal configurations in product lines by random sampling","year":"2017","author":"oh","key":"ref28"},{"journal-title":"NSDI","article-title":"CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics","year":"2017","author":"alipourfard","key":"ref4"},{"journal-title":"Finding faster configurations using flash","year":"2018","author":"nair","key":"ref27"},{"journal-title":"NSDI","article-title":"Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics","year":"2016","author":"venkataraman","key":"ref3"},{"journal-title":"ArXiv e-prints","article-title":"Scout: An Experienced Guide to Find the Best Cloud Configuration","year":"2018","author":"hsu","key":"ref6"},{"journal-title":"FSE","article-title":"Using bad learners to find good configurations","year":"2017","author":"nair","key":"ref29"},{"journal-title":"ICDCS","article-title":"Arrow: Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM","year":"2018","author":"hsu","key":"ref5"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1145\/1773912.1773918","article-title":"Are clouds ready for large distributed applications?","volume":"44","author":"sripanidkulchai","year":"2010","journal-title":"ACM SIGOPS Operating Systems Review"},{"journal-title":"CLOUD","article-title":"Cloud migration: A case study of migrating an enterprise it system to iaas","year":"2010","author":"khajeh-hosseini","key":"ref7"},{"journal-title":"SRDS","article-title":"Inside-Out: Reliable Performance Prediction for Distributed Storage Systems in the Cloud","year":"2016","author":"hsu","key":"ref2"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1214\/aoap\/1177005588"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3127479.3131614"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/978-1-4612-5110-1_13","article-title":"Some aspects of the sequential design of experiments","author":"robbins","year":"1985","journal-title":"Herbert Robbins Selected Papers"},{"key":"ref22","first-page":"261","article-title":"Starfish: A self-tuning system for big data analytics","volume":"11","author":"herodotou","year":"2011","journal-title":"CIDR"},{"journal-title":"Google VM rightsizing service","year":"0","key":"ref21"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3127479.3127492"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3127479.3128605"},{"journal-title":"KDD","article-title":"Google vizier: A service for black-box optimization","year":"2017","author":"golovin","key":"ref26"},{"journal-title":"AISTATS","article-title":"Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets","year":"2017","author":"klein","key":"ref25"}],"event":{"name":"2018 IEEE 11th International Conference on Cloud Computing (CLOUD)","start":{"date-parts":[[2018,7,2]]},"location":"San Francisco, CA, USA","end":{"date-parts":[[2018,7,7]]}},"container-title":["2018 IEEE 11th International Conference on Cloud Computing (CLOUD)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8457546\/8457768\/08457826.pdf?arnumber=8457826","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T04:40:30Z","timestamp":1643172030000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8457826\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/cloud.2018.00058","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}