Performance by Design: Computer Capacity Planning By Example

By D. Menascé, L. Dowdy, Virgílio A. F. Almeida. 2004

429 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/921b4adfd516c6f2815893ca53181870c9b928a6

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Citations in the corpus

An analytical model for multi-tier internet services and its applications (2005, background): “Bennani and Menasce model a multi-tier Internet service serving multiple types of transactions as a network of queues with customers belonging to multiple classes [Benani and Menasce 2005; Menasce et al. 2004; Menasce et al. 2005].”
A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers (2010, background): “A traditional analytical approach to support capacity planning is queuing theory, allowing for the determination of response time, service time, server utilization, and many other metrics essential for capacity planning with dedicated servers [10].”
Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments (2011, methodology): “We use analytical performance (queueing network system model) [5] and workload information to supply intelligent input about system requirements to an application provisioner, which improves the efficiency of the system.”
A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments (2014, methodology): “In order to save costs, some researchers [48, 83] came up with an idea called smart kill.”
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation (2006, methodology): “In the MIM/1 model, the mean response time R of a service center with a service rate p and an arrival rate X is given by R= 1 [20].”
Resource Allocation for Autonomic Data Centers using Analytic Performance Models (2005, methodology): “We replace the table-driven approach with predictive multiclass queuing network models [11].”
On the use of hybrid reinforcement learning for autonomic resource allocation (2007, methodology): “We leverage this observation in employing the well known Mean Value Analysis (MVA) formulation [5] to model the mean response time characteristics of an application.”