{"title":"CCO -云成本优化器","authors":"A. Yehoshua, I. Kolchinsky, A. Schuster","doi":"10.1145/3579370.3594746","DOIUrl":null,"url":null,"abstract":"Cloud computing can be complex, but optimal management of it doesn't have to be. In this paper, we present the design and implementation of a scalable multi-Cloud Cost Optimizer (CCO) that calculates the optimal deployment scheme for a given workload on public or hybrid clouds. The goal of CCO is to reduce monetary costs while taking into account the specifications of the workload, including resource requirements and constraints. By using a combination of meta-heuristics, CCO addresses the combinatorial complexity of the problem and currently supports AWS and Azure. The CCO tool [1], can be accessed through a web UI or API and supports on-demand and spot instances. For broad discussion refer to [2].","PeriodicalId":180024,"journal":{"name":"Proceedings of the 16th ACM International Conference on Systems and Storage","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CCO - Cloud Cost Optimizer\",\"authors\":\"A. Yehoshua, I. Kolchinsky, A. Schuster\",\"doi\":\"10.1145/3579370.3594746\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cloud computing can be complex, but optimal management of it doesn't have to be. In this paper, we present the design and implementation of a scalable multi-Cloud Cost Optimizer (CCO) that calculates the optimal deployment scheme for a given workload on public or hybrid clouds. The goal of CCO is to reduce monetary costs while taking into account the specifications of the workload, including resource requirements and constraints. By using a combination of meta-heuristics, CCO addresses the combinatorial complexity of the problem and currently supports AWS and Azure. The CCO tool [1], can be accessed through a web UI or API and supports on-demand and spot instances. For broad discussion refer to [2].\",\"PeriodicalId\":180024,\"journal\":{\"name\":\"Proceedings of the 16th ACM International Conference on Systems and Storage\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 16th ACM International Conference on Systems and Storage\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3579370.3594746\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th ACM International Conference on Systems and Storage","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3579370.3594746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cloud computing can be complex, but optimal management of it doesn't have to be. In this paper, we present the design and implementation of a scalable multi-Cloud Cost Optimizer (CCO) that calculates the optimal deployment scheme for a given workload on public or hybrid clouds. The goal of CCO is to reduce monetary costs while taking into account the specifications of the workload, including resource requirements and constraints. By using a combination of meta-heuristics, CCO addresses the combinatorial complexity of the problem and currently supports AWS and Azure. The CCO tool [1], can be accessed through a web UI or API and supports on-demand and spot instances. For broad discussion refer to [2].
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