Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures

By Gueyoung Jung, M. Hiltunen, Kaustubh R. Joshi, R. Schlichting, Calton Pu. IEEE International Conference on Distributed Computing Systems, 2010

356 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Resource Management in Clouds: Survey and Research Challenges (2015, methodology): “, [120, 160]) propose hierarchical organization of controllers.”
Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints (2013, background): “…Offline Algorithm (OPT) Input: A system state history Input: M, the maximum allowed OTF Output: A VM migration time 1: while history is not empty do 2: if OTF of history M then 3: return the time of the last history state 4: else 5: drop the last state from history 6: end if 7: end while Theorem 1.”
Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey (2014, background): “Jung et al. (2010) present Mistral, a holistic optimization system that balances power consumption, application performance, and transient power/performance costs due to adaptation actions and decision making in a single unified framework.”
Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments (2011, unknown): “[20] propose the Mistral system, which per-”
On Patterns for Decentralized Control in Self-Adaptive Systems (2010, background): “Another example of the application of the hierarchical pattern can be found in [27].”