%0 Journal Article %1 wei2019reinforcement %A Wei, Yi %A Kudenko, Daniel %A Liu, Shijun %A Pan, Li %A Wu, Lei %A Meng, Xiangxu %D 2019 %E Xie, Guangming %J Mathematical Problems in Engineering %K myown %P 11 %T A Reinforcement Learning Based Auto-Scaling Approach for SaaS Providers in Dynamic Cloud Environment %U https://doi.org/10.1155/2019/5080647 %V 2019 %X Cloud computing is an emerging paradigm which provides a flexible and diversified trading market for Infrastructure-as-a-Service (IaaS) providers, Software-as-a-Service (SaaS) providers, and cloud-based application customers. Taking the perspective of SaaS providers, they offer various SaaS services using rental cloud resources supplied by IaaS providers to their end users. In order to maximize their utility, the best behavioural strategy is to reduce renting expenses as much as possible while providing sufficient processing capacity to meet customer demands. In reality, public IaaS providers such as Amazon offer different types of virtual machine (VM) instances with different pricing models. Moreover, service requests from customers always change as time goes by. In such heterogeneous and changing environments, how to realize application auto-scaling becomes increasingly significant for SaaS providers. In this paper, we first formulate this problem and then propose a Q-learning based self-adaptive renting plan generation approach to help SaaS providers make efficient IaaS facilities adjustment decisions dynamically. Through a series of experiments and simulation, we evaluate the auto-scaling approach under different market conditions and compare it with two other resource allocation strategies. Experimental results show that our approach could automatically generate optimal renting policies for the SaaS provider in the long run."/><meta name="citation_pdf_url" content="http://downloads.hindawi.com/journals/mpe/2019/5080647.pdf"/><meta name="citation_xml_url" content="http://downloads.hindawi.com/journals/mpe/2019/5080647.xml"/><meta name="citation_fulltext_html_url" content="https://new.hindawi.com/journals/mpe/2019/5080647/"/><meta name="citation_doi" content="https://doi.org/10.1155/2019/5080647"/><meta name="citation_online_date" content="2019/02/03"/><meta name="dcterms.issued" content="2019/02/03"/><meta name="dc.creator" content="Wei, Yi"/><meta name="dc.creator" content="Kudenko, Daniel"/><meta name="dc.creator" content="Liu, Shijun"/><meta name="dc.creator" content="Pan, Li"/><meta name="dc.creator" content="Wu, Lei"/><meta name="dc.creator" content="Meng, Xiangxu"/><meta name="dc.title" content=Ä Reinforcement Learning Based Auto-Scaling Approach for SaaS Providers in Dynamic Cloud Environment"/><meta name="dc.description" content="Cloud computing is an emerging paradigm which provides a flexible and diversified trading market for Infrastructure-as-a-Service (IaaS) providers, Software-as-a-Service (SaaS) providers, and cloud-based application customers. Taking the perspective of SaaS providers, they offer various SaaS services using rental cloud resources supplied by IaaS providers to their end users. In order to maximize their utility, the best behavioural strategy is to reduce renting expenses as much as possible while providing sufficient processing capacity to meet customer demands. In reality, public IaaS providers such as Amazon offer different types of virtual machine (VM) instances with different pricing models. Moreover, service requests from customers always change as time goes by. In such heterogeneous and changing environments, how to realize application auto-scaling becomes increasingly significant for SaaS providers. In this paper, we first formulate this problem and then propose a Q-learning based self-adaptive renting plan generation approach to help SaaS providers make efficient IaaS facilities adjustment decisions dynamically. Through a series of experiments and simulation, we evaluate the auto-scaling approach under different market conditions and compare it with two other resource allocation strategies. Experimental results show that our approach could automatically generate optimal renting policies for the SaaS provider in the long run."/><meta name="dc.publisher" content="Hindawi"/><meta name="dc.format" content="text/html"/><meta name="dc.language" content="en"/><meta name="dc.identifier" content="https://doi.org/10.1155/2019/5080647"/><meta name="dc.type" content="Research Article"/><meta name="dc.date" content="2019/02/03"/><meta name="dcterms.issued" content="2019/02/03"/><meta name="prism.publicationDate" content="2019/02/03"/><meta name="prism.volume" content="2019"/><meta name="prism.number" content=""/><meta name="prism.startingPage" content=""/><meta name="prism.section" content="Research Article"/><meta name="prism.doi" content="https://doi.org/10.1155/2019/5080647"/><meta name="citation_issn" content="1024-123X"/><meta name="prism.issn" content="1024-123X"/><meta name="dc.source" content="Mathematical Problems in Engineering"/><meta name="citation_journal_title" content="Mathematical Problems in Engineering"/><meta name="prism.publicationName" content="Mathematical Problems in Engineering"/><link rel="stylesheet" href="//fonts.googleapis.com/css?family=IBM+Plex+Sans:400,400i,500,500i,700,700i"/><link rel="shortcut icon" type="image/x-icon" href="https://static-01.hindawi.com/next_assets/WWQLtdVHTkpIy3ivTphyo/_next/static/static/favicon.ico"/><meta name=Äuthor" content="Hindawi"/><meta name="robots" content="noindex"/><meta name="google-site-verification" content=ÄxEuDsL7vXGOxRe53-uFhOk2ODN0bbXMeuBy6Pfq4ww"/><script src="https://cdn.cookielaw.org/scripttemplates/otSDKStub.js" type="text/javascript" charset=ÜTF-8" data-domain-script="790048b6-f010-4ac2-b5f6-108356336b09"></script><script type="text/javascript">function OptanonWrapper()</script><link href="https://static-01.hindawi.com/articles/mpe/volume-2019/5080647/5080647-style.css" rel="stylesheet" class="next-head"/><link href="https://cdnjs.cloudflare.com/ajax/libs/jquery.basictable/1.0.9/basictable.css" rel="stylesheet" media="none" class="next-head"/><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/OwlCarousel2/2.3.4/assets/owl.carousel.min.css" media="none" class="next-head"/><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/OwlCarousel2/2.3.4/assets/owl.theme.default.min.css" media="none" class="next-head

@article{wei2019reinforcement, abstract = {Cloud computing is an emerging paradigm which provides a flexible and diversified trading market for Infrastructure-as-a-Service (IaaS) providers, Software-as-a-Service (SaaS) providers, and cloud-based application customers. Taking the perspective of SaaS providers, they offer various SaaS services using rental cloud resources supplied by IaaS providers to their end users. In order to maximize their utility, the best behavioural strategy is to reduce renting expenses as much as possible while providing sufficient processing capacity to meet customer demands. In reality, public IaaS providers such as Amazon offer different types of virtual machine (VM) instances with different pricing models. Moreover, service requests from customers always change as time goes by. In such heterogeneous and changing environments, how to realize application auto-scaling becomes increasingly significant for SaaS providers. In this paper, we first formulate this problem and then propose a Q-learning based self-adaptive renting plan generation approach to help SaaS providers make efficient IaaS facilities adjustment decisions dynamically. Through a series of experiments and simulation, we evaluate the auto-scaling approach under different market conditions and compare it with two other resource allocation strategies. Experimental results show that our approach could automatically generate optimal renting policies for the SaaS provider in the long run."/><meta name="citation_pdf_url" content="http://downloads.hindawi.com/journals/mpe/2019/5080647.pdf"/><meta name="citation_xml_url" content="http://downloads.hindawi.com/journals/mpe/2019/5080647.xml"/><meta name="citation_fulltext_html_url" content="https://new.hindawi.com/journals/mpe/2019/5080647/"/><meta name="citation_doi" content="https://doi.org/10.1155/2019/5080647"/><meta name="citation_online_date" content="2019/02/03"/><meta name="dcterms.issued" content="2019/02/03"/><meta name="dc.creator" content="Wei, Yi"/><meta name="dc.creator" content="Kudenko, Daniel"/><meta name="dc.creator" content="Liu, Shijun"/><meta name="dc.creator" content="Pan, Li"/><meta name="dc.creator" content="Wu, Lei"/><meta name="dc.creator" content="Meng, Xiangxu"/><meta name="dc.title" content="A Reinforcement Learning Based Auto-Scaling Approach for SaaS Providers in Dynamic Cloud Environment"/><meta name="dc.description" content="Cloud computing is an emerging paradigm which provides a flexible and diversified trading market for Infrastructure-as-a-Service (IaaS) providers, Software-as-a-Service (SaaS) providers, and cloud-based application customers. Taking the perspective of SaaS providers, they offer various SaaS services using rental cloud resources supplied by IaaS providers to their end users. In order to maximize their utility, the best behavioural strategy is to reduce renting expenses as much as possible while providing sufficient processing capacity to meet customer demands. In reality, public IaaS providers such as Amazon offer different types of virtual machine (VM) instances with different pricing models. Moreover, service requests from customers always change as time goes by. In such heterogeneous and changing environments, how to realize application auto-scaling becomes increasingly significant for SaaS providers. In this paper, we first formulate this problem and then propose a Q-learning based self-adaptive renting plan generation approach to help SaaS providers make efficient IaaS facilities adjustment decisions dynamically. Through a series of experiments and simulation, we evaluate the auto-scaling approach under different market conditions and compare it with two other resource allocation strategies. Experimental results show that our approach could automatically generate optimal renting policies for the SaaS provider in the long run."/><meta name="dc.publisher" content="Hindawi"/><meta name="dc.format" content="text/html"/><meta name="dc.language" content="en"/><meta name="dc.identifier" content="https://doi.org/10.1155/2019/5080647"/><meta name="dc.type" content="Research Article"/><meta name="dc.date" content="2019/02/03"/><meta name="dcterms.issued" content="2019/02/03"/><meta name="prism.publicationDate" content="2019/02/03"/><meta name="prism.volume" content="2019"/><meta name="prism.number" content=""/><meta name="prism.startingPage" content=""/><meta name="prism.section" content="Research Article"/><meta name="prism.doi" content="https://doi.org/10.1155/2019/5080647"/><meta name="citation_issn" content="1024-123X"/><meta name="prism.issn" content="1024-123X"/><meta name="dc.source" content="Mathematical Problems in Engineering"/><meta name="citation_journal_title" content="Mathematical Problems in Engineering"/><meta name="prism.publicationName" content="Mathematical Problems in Engineering"/><link rel="stylesheet" href="//fonts.googleapis.com/css?family=IBM+Plex+Sans:400,400i,500,500i,700,700i"/><link rel="shortcut icon" type="image/x-icon" href="https://static-01.hindawi.com/next_assets/WWQLtdVHTkpIy3ivTphyo/_next/static/static/favicon.ico"/><meta name="Author" content="Hindawi"/><meta name="robots" content="noindex"/><meta name="google-site-verification" content="AxEuDsL7vXGOxRe53-uFhOk2ODN0bbXMeuBy6Pfq4ww"/><script src="https://cdn.cookielaw.org/scripttemplates/otSDKStub.js" type="text/javascript" charset="UTF-8" data-domain-script="790048b6-f010-4ac2-b5f6-108356336b09"></script><script type="text/javascript">function OptanonWrapper()</script><link href="https://static-01.hindawi.com/articles/mpe/volume-2019/5080647/5080647-style.css" rel="stylesheet" class="next-head"/><link href="https://cdnjs.cloudflare.com/ajax/libs/jquery.basictable/1.0.9/basictable.css" rel="stylesheet" media="none" class="next-head"/><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/OwlCarousel2/2.3.4/assets/owl.carousel.min.css" media="none" class="next-head"/><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/OwlCarousel2/2.3.4/assets/owl.theme.default.min.css" media="none" class="next-head}, added-at = {2020-01-21T12:37:52.000+0100}, author = {Wei, Yi and Kudenko, Daniel and Liu, Shijun and Pan, Li and Wu, Lei and Meng, Xiangxu}, biburl = {https://www.bibsonomy.org/bibtex/29d7c879e985c0ee6682eb4a1c465f514/kudenko}, description = {A Reinforcement Learning Based Auto-Scaling Approach for SaaS Providers in Dynamic Cloud Environment}, editor = {Xie, Guangming}, interhash = {2dad2066c53629407166111a8fcd5632}, intrahash = {9d7c879e985c0ee6682eb4a1c465f514}, journal = {Mathematical Problems in Engineering}, keywords = {myown}, pages = 11, timestamp = {2020-01-21T12:54:50.000+0100}, title = {A Reinforcement Learning Based Auto-Scaling Approach for SaaS Providers in Dynamic Cloud Environment}, url = {https://doi.org/10.1155/2019/5080647}, volume = 2019, year = 2019 }