Intelligence infrastructure: architecture discussion: performance, availability and management
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Dynamic VM migration: assessing its risks & rewards using a benchmark
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Server consolidation with migration control for virtualized data centers
Future Generation Computer Systems
Maximum migration time guarantees in dynamic server consolidation for virtualized data centers
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Energy-aware capacity scaling in virtualized environments with performance guarantees
Performance Evaluation
Gossip-based resource allocation for green computing in large clouds
Proceedings of the 7th International Conference on Network and Services Management
A swarm-inspired data center consolidation methodology
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Modeling response times in the Google ROADEF/EURO challenge
ACM SIGMETRICS Performance Evaluation Review
A multi-objective ant colony system algorithm for virtual machine placement in cloud computing
Journal of Computer and System Sciences
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account.