The Vision of Autonomic Computing
Computer
Stream-Packing: Resource Allocation in Web Server Farms with a QoS Guarantee
HiPC '01 Proceedings of the 8th International Conference on High Performance Computing
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Resource Allocation for Autonomic Data Centers using Analytic Performance Models
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
A scalable application placement controller for enterprise data centers
Proceedings of the 16th international conference on World Wide Web
VTDC '06 Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing
Journal of Systems and Software
Resource Provisioning Options for Large-Scale Scientific Workflows
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
Entropy: a consolidation manager for clusters
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
SLA-Aware Virtual Resource Management for Cloud Infrastructures
CIT '09 Proceedings of the 2009 Ninth IEEE International Conference on Computer and Information Technology - Volume 02
Shares and utilities based power consolidation in virtualized server environments
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
A Multi-agent Approach for Semantic Resource Allocation
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
SCC '11 Proceedings of the 2011 IEEE International Conference on Services Computing
Optimal Resource Allocation in a Virtualized Software Aging Platform with Software Rejuvenation
ISSRE '11 Proceedings of the 2011 IEEE 22nd International Symposium on Software Reliability Engineering
Autonomic computing: an overview
UPP'04 Proceedings of the 2004 international conference on Unconventional Programming Paradigms
Dynamic application placement under service and memory constraints
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
BioRAC: biologically inspired resilient autonomic cloud
Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research
An energy aware framework for virtual machine placement in cloud federated data centres
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
OPTIMIS: A holistic approach to cloud service provisioning
Future Generation Computer Systems
SLA-based Optimization of Power and Migration Cost in Cloud Computing
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Concurrency and Computation: Practice & Experience
SmartScale: Automatic Application Scaling in Enterprise Clouds
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
Minimum Cost Maximum Flow Algorithm for Dynamic Resource Allocation in Clouds
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
Generalized resource allocation for the cloud
Proceedings of the Third ACM Symposium on Cloud Computing
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The elasticity afforded by cloud computing allows consumers to dynamically request and relinquish computing and storage resources and pay for them on a pay-per-use basis. Cloud computing providers rely on virtualization techniques to manage the dynamic nature of their infrastructure allowing consumers to dynamically allocate and deallocate virtual machines of different capacities. Cloud providers need to optimally decide the best allocation of virtual machines to physical machines as the demand varies dynamically. When making such decisions, cloud providers can migrate VMs already allocated and/or use external cloud providers. This paper considers the problem in which the cloud provider wants to maximize its revenue, subject to capacity, availability SLA, and VM migration constraints. The paper presents a heuristic solution, called Near Optimal (NOPT), to this NP-hard problem and discusses the results of its experimental evaluation in comparison with a best fit (BF) allocation strategy. The results show that NOPT provides a 45% improvement in average revenue when compared with BF for the parameters used in the experiment. Moreover, the NOPT algorithm maintained the availability close to one for all classes of users while BF exhibited a lower availability and even failed to meet the availability SLA at times.