On maximizing service-level-agreement profits
Proceedings of the 3rd ACM conference on Electronic Commerce
Preserving QoS of e-commerce sites through self-tuning: a performance model approach
Proceedings of the 3rd ACM conference on Electronic Commerce
A resource allocation model for QoS management
RTSS '97 Proceedings of the 18th IEEE Real-Time Systems Symposium
Scalable Resource Allocation for Multi-Processor QoS Optimization
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
SLA based profit optimization in autonomic computing systems
Proceedings of the 2nd international conference on Service oriented computing
Utility Functions in Autonomic Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Resource Allocation for Autonomic Data Centers using Analytic Performance Models
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
ICWS '05 Proceedings of the IEEE International Conference on Web Services
Provisioning servers in the application tier for e-commerce systems
ACM Transactions on Internet Technology (TOIT)
Managing Cancellations and No-Shows of Reservations with Overbooking to Increase Resource Revenue
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
Future Generation Computer Systems
Dynamic resource allocation for shared data centers using online measurements
IWQoS'03 Proceedings of the 11th international conference on Quality of service
The Characteristics of Cloud Computing
ICPPW '10 Proceedings of the 2010 39th International Conference on Parallel Processing Workshops
Multi-objective optimization of data flows in a multi-cloud environment
Proceedings of the Second Workshop on Data Analytics in the Cloud
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Compared with the traditional computing models such as grid computing and cluster computing, a key advantage of Cloud computing is that it provides a practical business model for customers to use remote resources. However, it is challenging for Cloud providers to allocate the pooled computing resources dynamically among the differentiated customers so as to maximize their revenue. It is not an easy task to transform the customer-oriented service metrics into operating level metrics, and control the Cloud resources adaptively based on Service Level Agreement (SLA). This paper addresses the problem of maximizing the provider's revenue through SLA-based dynamic resource allocation as SLA plays a vital role in Cloud computing to bridge service providers and customers. We formalize the resource allocation problem using Queuing Theory and propose optimal solutions for the problem considering various Quality of Service (QoS) parameters such as pricing mechanisms, arrival rates, service rates and available resources. The experimental results, both with the synthetic dataset and with traced dadataset, show that our algorithms outperform related work.