Introduction to algorithms
Automatic control systems (6th ed.)
Automatic control systems (6th ed.)
Generating representative Web workloads for network and server performance evaluation
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Stochastic Complexity in Statistical Inquiry Theory
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Capacity Planning: An Essential Tool for Managing Web Services
IT Professional
Server Capacity Planning for Web Traffic Workload
IEEE Transactions on Knowledge and Data Engineering
The Vision of Autonomic Computing
Computer
Feedback Control of Computing Systems
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Performance by Design: Computer Capacity Planning By Example
Performance by Design: Computer Capacity Planning By Example
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
Utility Functions in Autonomic Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Modeling and Tracking of Transaction Flow Dynamics for Fault Detection in Complex Systems
IEEE Transactions on Dependable and Secure Computing
Analytic modeling of multitier Internet applications
ACM Transactions on the Web (TWEB)
Power provisioning for a warehouse-sized computer
Proceedings of the 34th annual international symposium on Computer architecture
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
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Exploiting nonstationarity for performance prediction
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Efficient and Scalable Algorithms for Inferring Likely Invariants in Distributed Systems
IEEE Transactions on Knowledge and Data Engineering
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Discovering Likely Invariants of Distributed Transaction Systems for Autonomic System Management
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Towards Commercialization of Utility-based Resource Allocation
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Utilization analysis of servers in a data centre
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Capacity planning for vertical search engines: an approach based on coloured petri nets
PETRI NETS'12 Proceedings of the 33rd international conference on Application and Theory of Petri Nets
Modelling Search Engines Performance Using Coloured Petri Nets
Fundamenta Informaticae - Application and Theory of Petri Nets and Concurrency, 2012
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The capacity needs of online services are mainly determined by the volume of user loads. For large-scale distributed systems running such services, it is quite difficult to match the capacities of various system components. In this paper, a novel and systematic approach is proposed to profile services for resource optimization and capacity planning. We collect resource consumption related measurements from various components across distributed systems and further search for constant relationships between these measurements. If such relationships always hold under various workloads along time, we consider them as invariants of the underlying system. After extracting many invariants from the system, given any volume of user loads, we can follow these invariant relationships sequentially to estimate the capacity needs of individual components. By comparing the current resource configurations against the estimated capacity needs, we can discover the weakest points that may deteriorate system performance. Operators can consult such analytical results to optimize resource assignments and remove potential performance bottlenecks. In this paper, we propose several algorithms to support capacity analysis and guide operator's capacity planning tasks. Our algorithms are evaluated with real systems and experimental results are also included to demonstrate the effectiveness of our approach.