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Code Optim."],"published-print":{"date-parts":[[2018,9,30]]},"abstract":"<jats:p>Modern data centers consolidate workloads to increase server utilization and reduce total cost of ownership, and cope with scaling limitations. However, server resource sharing introduces performance interference across applications and, consequently, increases performance volatility, which negatively affects user experience. Thus, a challenging problem is to increase server utilization while maintaining application QoS.<\/jats:p>\n          <jats:p>\n            In this article, we present\n            <jats:italic>QuMan<\/jats:italic>\n            , a server resource manager that uses application isolation and profiling to increase server utilization while controlling degradation of application QoS. Previous solutions, either estimate interference across applications and then restrict colocation to \u201ccompatible\u201d applications, or assume that application requirements are known. Instead,\n            <jats:italic>QuMan<\/jats:italic>\n            estimates the required resources of applications. It uses an isolation mechanism to create properly-sized resource slices for applications, and arbitrarily colocates applications.\n            <jats:italic>QuMan<\/jats:italic>\n            \u2019s mechanisms can be used with a variety of admission control policies, and we explore the potential of two such policies: (1) A policy that allows users to specify a minimum performance threshold and (2) an automated policy, which operates without user input and is based on a new combined QoS-utilization metric. We implement\n            <jats:italic>QuMan<\/jats:italic>\n            on top of Linux servers, and we evaluate its effectiveness using containers and real applications. Our single-node results show that\n            <jats:italic>QuMan<\/jats:italic>\n            balances highly effectively the tradeoff between server utilization and application performance, as it achieves 80% server utilization while the performance of each application does not drop below 80% the respective standalone performance. We also deploy\n            <jats:italic>QuMan<\/jats:italic>\n            on a cluster of 100 AWS instances that are managed by a modified version of the Sparrow scheduler [37] and, we observe a 48% increase in application performance on a highly utilized cluster, compared to the performance of the same cluster under the same load when it is managed by native Sparrow or Apache Mesos.\n          <\/jats:p>","DOI":"10.1145\/3210560","type":"journal-article","created":{"date-parts":[[2018,8,30]],"date-time":"2018-08-30T13:45:11Z","timestamp":1535636711000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["<i>QuMan<\/i>"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9431-5721","authenticated-orcid":false,"given":"Yannis","family":"Sfakianakis","sequence":"first","affiliation":[{"name":"Foundation for Research and Technology--Hellas (FORTH), Heraklion, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christos","family":"Kozanitis","sequence":"additional","affiliation":[{"name":"Foundation for Research and Technology--Hellas (FORTH), Heraklion, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christos","family":"Kozyrakis","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angelos","family":"Bilas","sequence":"additional","affiliation":[{"name":"Foundation for Research and Technology--Hellas (FORTH), Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,8,28]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Launching a Spark\/Shark Cluster on EC2. 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