Autonomic QoS control in enterprise Grid environments using online simulation
Journal of Systems and Software
Towards Self-Aware Performance and Resource Management in Modern Service-Oriented Systems
SCC '10 Proceedings of the 2010 IEEE International Conference on Services Computing
Model-based self-adaptive resource allocation in virtualized environments
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Self-aware QoS management in virtualized infrastructures
Proceedings of the 8th ACM international conference on Autonomic computing
Automated Transformation of Component-Based Software Architecture Models to Queueing Petri Nets
MASCOTS '11 Proceedings of the 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems
Automated simulation-based capacity planning for enterprise data fabrics
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
Automated extraction of architecture-level performance models of distributed component-based systems
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
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Modern IT systems have highly distributed and dynamic architectures composed of loosely-coupled services typically deployed on virtualized infrastructures. Managing system resources in such environments to ensure acceptable end-to-end application Quality-of-Service (QoS) while at the same time optimizing resource utilization and energy efficiency is a challenge. The adoption of Cloud Computing technologies, including Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS), comes at the cost of increased system complexity and dynamicity. This makes it hard to provide QoS guarantees in terms of performance and availability, as well as resilience to attacks and operational failures [8]. Moreover, the consolidation of workloads translates into higher utilization of physical resources which makes the system much more vulnerable to threats resulting from unforeseen load fluctuations, hardware failures and network attacks.