A scalable application placement controller for enterprise data centers
Proceedings of the 16th international conference on World Wide Web
Measurement-based characterization of a collection of on-line games
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
The Server Reassignment Problem for Load Balancing in Structured P2P Systems
IEEE Transactions on Parallel and Distributed Systems
Communications of the ACM
Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers
Future Generation Computer Systems
Modeling for Dynamic Cloud Scheduling Via Migration of Virtual Machines
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
OPTIMIS: A holistic approach to cloud service provisioning
Future Generation Computer Systems
Let the clouds compute: cost-efficient workload distribution in infrastructure clouds
GECON'12 Proceedings of the 9th international conference on Economics of Grids, Clouds, Systems, and Services
A cost analysis of cloud computing for education
GECON'12 Proceedings of the 9th international conference on Economics of Grids, Clouds, Systems, and Services
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
Cost-Optimal Cloud Service Placement under Dynamic Pricing Schemes
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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We present an approach to optimal virtual machine placement within datacenters for predicable and time-constrained load peaks. A method for optimal load balancing is developed, based on binary integer programming. For tradeoffs between quality of solution and computation time, we also introduce methods to pre-process the optimization problem before solving it. Upper bound based optimizations are used to reduce the time required to compute a final solution, enabling larger problems to be solved. For further scalability, we also present three approximation algorithms, based on heuristics and/or greedy formulations. The proposed algorithms are evaluated through simulations based on synthetic data sets. The evaluation suggests that our algorithms are feasible, and that these can be combined to achieve desired tradeoffs between quality of solution and execution time.