Resource Management in the Autonomic Service-Oriented Architecture
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Service Consolidation with End-to-End Response Time Constraints
SEAA '08 Proceedings of the 2008 34th Euromicro Conference Software Engineering and Advanced Applications
Optimal deployment of eventually-serializable data services
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Cooling-aware workload placement with performance constraints
Performance Evaluation
Bin repacking scheduling in virtualized datacenters
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
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
Modeling response times in the Google ROADEF/EURO challenge
ACM SIGMETRICS Performance Evaluation Review
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In this paper, we present a constraint programming approach for the service consolidation problem that is being currently tackled by Neptuny, Milan. The problem is defined as: Given a data-center, a set of servers with a priori fixed costs, a set of services or applications with hourly resource utilizations, find an allocation of applications to servers while minimizing the data-center costs and satisfying constraints on the resource utilizations for each hour of the day profile and on rule-based constraints defined between services and servers and amongst different services. The service consolidation problem can be modelled as an Integer Linear Programming problem with 0–1 variables, however it is extremely difficult to handle large sized instances and the rule-based constraints. So a constraint programming approach using the Comet programming language is developed to assess the impact of the rule-based constraints in reducing the problem search space and to improve the solution quality and scalability. Computational results for realistic consolidation scenarios are presented, showing that the proposed approach is indeed promising.