Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Parallelising the mean value analysis algorithm
Transactions of the Society for Computer Simulation International - Special issue on parallel and distributed simulation
Mean-Value Analysis of Closed Multichain Queuing Networks
Journal of the ACM (JACM)
Capacity Planning for Web Services: metrics, models, and methods
Capacity Planning for Web Services: metrics, models, and methods
Capacity Planning for Internet Services
Capacity Planning for Internet Services
Resource-Sharing and Service Deployment in Virtual Data Centers
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Brief announcement: Cataclysm: handling extreme overloads in internet services
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
An analytical model for multi-tier internet services and its applications
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Dynamic Provisioning of Multi-tier Internet Applications
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Modeling 3-Tiered Web Applications
MASCOTS '05 Proceedings of the 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Performance modeling and system management for multi-component online services
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
Model-based resource provisioning in a web service utility
USITS'03 Proceedings of the 4th conference on USENIX Symposium on Internet Technologies and Systems - Volume 4
Detecting performance anomalies in global applications
WORLDS'05 Proceedings of the 2nd conference on Real, Large Distributed Systems - Volume 2
Exploiting nonstationarity for performance prediction
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Dynamic resource allocation for shared data centers using online measurements
IWQoS'03 Proceedings of the 11th international conference on Quality of service
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Predictive modelling of SAP ERP applications: challenges and solutions
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
SLA-driven planning and optimization of enterprise applications
Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
Resource allocation algorithms for virtualized service hosting platforms
Journal of Parallel and Distributed Computing
When average is not average: large response time fluctuations in n-tier systems
Proceedings of the 9th international conference on Autonomic computing
Heavy-traffic revenue maximization in parallel multiclass queues
Performance Evaluation
| Hi-index | 0.00 |
Service providers and their customers agree on certain quality of service guarantees through Service Level Agreements (SLA). An SLA contains one or more Service Level Objectives (SLO)s that describe the agreed-upon quality requirements at the service level. Translating these SLOs into lower-level policies that can then be used for design and monitoring purposes is a difficult problem. Usually domain experts are involved in this translation that often necessitates application of domain knowledge to this problem. In this article, we propose an approach that combines performance modeling with regression analysis to solve this problem. We demonstrate that our approach is practical and that it can be applied to different n-tier services. Our experiments show that for a typical 3-tier e-commerce application in a virtualized environment, the SLA can be met while improving CPU utilization by up to 3 times.