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Knowledge-based adaptable scheduler for SaaS providers in cloud computing
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DOI：10.1186/s13673-015-0031-4.png)
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Abstract
En 中文
Software as a Service (SaaS) in Cloud Computing offers reliable access to software applications for end users over the Internet without direct investment in infrastructure and software. SaaS providers utilize resources of internal datacenters or rent resources from a public Infrastructure as a Service (IaaS) provider in order to serve their customers. Internal hosting can increase cost of administration and maintenance, whereas hiring from an IaaS provider can impact quality of service due to its variable performance. To surmount these challenges, we propose a knowledge-based admission control along with scheduling algorithms for SaaS providers to effectively utilize public Cloud resources in order to maximize profit by minimizing cost and improving customers' satisfaction level. In the proposed model, the admission control is based on Service Level Agreement (SLA) and uses different strategies to decide upon accepting user requests for that minimal performance impact, avoiding SLA penalties that are giving higher profit. However, because the admission control can make decisions optimally, there is a need of machine learning methods to predict the strategies. In order to model prediction of sequence of strategies, a customized decision tree algorithm has been used. In addition, we conducted several experiments to analyze which solution in which scenario fit better to maximize SaaS provider's profit. Results obtained through our simulation shows that our proposed algorithm provides significant improvement (up to 38.4 % cost saving) compared to the previous research works.
Keywords:
Cloud computing
Service Level Agreement (SLA)
Admission control
Software as a service
Scalability of application services
Knowledge-based
Data mining
Decision tree algorithm
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