Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Asymptotic analysis of multiclass closed queueing networks: common bottleneck
Performance Evaluation
Resource Allocation for Autonomic Data Centers using Analytic Performance Models
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Java Modelling Tools: an Open Source Suite for Queueing Network Modelling andWorkload Analysis
QEST '06 Proceedings of the 3rd international conference on the Quantitative Evaluation of Systems
Adaptive control of virtualized resources in utility computing environments
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Data Consolidation: A Task Scheduling and Data Migration Technique for Grid Networks
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
Probabilistic performance modeling of virtualized resource allocation
Proceedings of the 7th international conference on Autonomic computing
Workload-aware database monitoring and consolidation
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
The application of FSP models in automatic optimization of software deployment
ASMTA'11 Proceedings of the 18th international conference on Analytical and stochastic modeling techniques and applications
Performance models for virtualized applications
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
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The users of actual computing infrastructures allowing the resource provision (such as clouds) are often asked to decide about the proper amount of equipment (virtual machines, VMs) required to execute their requests while satisfying a set of performance objectives. These types of decisions are particularly difficult since the direct correlation between the resources allocated and the performance offered is influenced by a number of factors such as the characteristic of the different class of requests, the capacity of the resources, the workload sharing the same physical hardware, the dynamic variation of the mix of requests of the different classes in concurrent execution. In this paper we derive the impact on several performance indexes by two popular techniques, namely, consolidation and replication, adopted in virtual computing infrastructures. In particular we present an analytical model to determine the best consolidation or replication options that matches given performance objectives specified through a set of constraints.