Run-time Models for Self-managing Systems and Applications [electronic resource] / edited by Danilo Ardagna, Li Zhang.

Stochastic Analysis and Optimization of�Multiserver Systems -- On the Selection of Models for Runtime Prediction of System Resources -- Estimating Model Parameters of Adaptive Software Systems in Real-Time -- A Control-Theoretic Approach for�the�Combined Management of�Quality-of-Service and�Energy in Service Centers -- The Emergence of Load Balancing in�Distributed Systems: the SelfLet Approach -- Run Time Models in Adaptive Service Infrastructure -- On the Modeling and Management of Cloud Data Analytics.

This edited volume focuses on the adoption of run-time models for the design and management of autonomic systems. Traditionally, performance models have a central role in the design of computer systems. Models are used at design-time to support the capacity planning of the physical infrastructure and to analyze the effects and trade-offs of different architectural choices. Models may also be used at run-time to assess the compliance of the running system with respect to design-time models, to measure the real system performance parameters to fill the gap between design and run-time. Models at run-time can also assess the compliance of service level agreements and trigger autonomic systems re-configuration. Run-time models are receiving great interest, since, e.g., power management of CPUs and resource management in virtualized systems can be actuated at very fine grain time scales. In such situations, traditional performance techniques evaluating the systems steady state may provide only a rough estimate of system behavior and are not effective to react to workload fluctuations. This book includes advanced techniques and solutions for the run-time estimation of autonomic systems performance, the analysis of transient conditions and their application in advanced prototype environments.