{"title":"实现云存储提供商利润最大化的在线定价和资源调度","authors":"Kyungtae Lee;Yeongjin Kim","doi":"10.1109/TCC.2024.3450876","DOIUrl":null,"url":null,"abstract":"There is increasing competition among cloud object storage service (COSS) providers as the demand for COSSs grows. However, existing pricing models offered by commercial COSS providers fail to effectively adapt to changing client demand and resource supply. Consequently, many COSS providers are still grappling with operational challenges in maximizing their profits, such as pricing policy, load balancing, server scheduling, and energy management. In this paper, we propose a novel approach called time-dependent pricing and scheduling (\n<italic>TD-PnS</i>\n), which is based on the Lyapunov-drift-minus-profit technique. To maximize the profits of COSS providers, \n<italic>TD-PnS</i>\n enables joint and dynamic decision-making across several key factors that have been dealt with separately so far: \n<italic>(i)</i>\n service pricing, \n<italic>(ii)</i>\n CPU clock scaling and encoding scheduling, \n<italic>(iii)</i>\n network scheduling, and \n<italic>(iv)</i>\n energy storage management. We propose an enhanced version of \n<italic>TD-PnS</i>\n, called \n<italic>TD-PnS-Adv</i>\n, further to improve other aspects, such as system stabilization. Finally, through trace-driven simulations utilizing a real dataset, we demonstrate the superior performance of the proposed algorithms compared to existing algorithms and pricing models in terms of profit maximization.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1186-1199"},"PeriodicalIF":5.3000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Online Pricing and Resource Scheduling for Profit Maximization of Cloud Storage Providers\",\"authors\":\"Kyungtae Lee;Yeongjin Kim\",\"doi\":\"10.1109/TCC.2024.3450876\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There is increasing competition among cloud object storage service (COSS) providers as the demand for COSSs grows. However, existing pricing models offered by commercial COSS providers fail to effectively adapt to changing client demand and resource supply. Consequently, many COSS providers are still grappling with operational challenges in maximizing their profits, such as pricing policy, load balancing, server scheduling, and energy management. In this paper, we propose a novel approach called time-dependent pricing and scheduling (\\n<italic>TD-PnS</i>\\n), which is based on the Lyapunov-drift-minus-profit technique. To maximize the profits of COSS providers, \\n<italic>TD-PnS</i>\\n enables joint and dynamic decision-making across several key factors that have been dealt with separately so far: \\n<italic>(i)</i>\\n service pricing, \\n<italic>(ii)</i>\\n CPU clock scaling and encoding scheduling, \\n<italic>(iii)</i>\\n network scheduling, and \\n<italic>(iv)</i>\\n energy storage management. We propose an enhanced version of \\n<italic>TD-PnS</i>\\n, called \\n<italic>TD-PnS-Adv</i>\\n, further to improve other aspects, such as system stabilization. Finally, through trace-driven simulations utilizing a real dataset, we demonstrate the superior performance of the proposed algorithms compared to existing algorithms and pricing models in terms of profit maximization.\",\"PeriodicalId\":13202,\"journal\":{\"name\":\"IEEE Transactions on Cloud Computing\",\"volume\":\"12 4\",\"pages\":\"1186-1199\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Cloud Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10654575/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cloud Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10654575/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Online Pricing and Resource Scheduling for Profit Maximization of Cloud Storage Providers
There is increasing competition among cloud object storage service (COSS) providers as the demand for COSSs grows. However, existing pricing models offered by commercial COSS providers fail to effectively adapt to changing client demand and resource supply. Consequently, many COSS providers are still grappling with operational challenges in maximizing their profits, such as pricing policy, load balancing, server scheduling, and energy management. In this paper, we propose a novel approach called time-dependent pricing and scheduling (
TD-PnS
), which is based on the Lyapunov-drift-minus-profit technique. To maximize the profits of COSS providers,
TD-PnS
enables joint and dynamic decision-making across several key factors that have been dealt with separately so far:
(i)
service pricing,
(ii)
CPU clock scaling and encoding scheduling,
(iii)
network scheduling, and
(iv)
energy storage management. We propose an enhanced version of
TD-PnS
, called
TD-PnS-Adv
, further to improve other aspects, such as system stabilization. Finally, through trace-driven simulations utilizing a real dataset, we demonstrate the superior performance of the proposed algorithms compared to existing algorithms and pricing models in terms of profit maximization.
期刊介绍:
The IEEE Transactions on Cloud Computing (TCC) is dedicated to the multidisciplinary field of cloud computing. It is committed to the publication of articles that present innovative research ideas, application results, and case studies in cloud computing, focusing on key technical issues related to theory, algorithms, systems, applications, and performance.