Modern heuristic techniques for combinatorial problems
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Business-oriented resource management policies for e-commerce servers
Performance Evaluation - Special issue on internet performance modelling
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The deployer's problem: configuring application servers for performance and reliability
Proceedings of the 25th International Conference on Software Engineering
A performance model for a BPI middleware
Proceedings of the 4th ACM conference on Electronic commerce
A recursive random search algorithm for large-scale network parameter configuration
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Queueing Model Based Network Server Performance Control
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Learning evaluation functions to improve optimization by local search
The Journal of Machine Learning Research
Online response time optimization of Apache web server
IWQoS'03 Proceedings of the 11th international conference on Quality of service
Finding probably better system configurations quickly
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
An improved society of hill-climbers and its application on batch process scheduling
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Performance Modeling and Evaluation of E-Business Systems
ANSS '06 Proceedings of the 39th annual Symposium on Simulation
Optimizing system configurations quickly by guessing at the performance
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Tuning mechanisms for two major parameters of Apache web servers
Software—Practice & Experience
Boosting the performance of computing systems through adaptive configuration tuning
Proceedings of the 2009 ACM symposium on Applied Computing
Caching in the TSP Search Space
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Experience transfer for the configuration tuning in large scale computing systems
ACM SIGMETRICS Performance Evaluation Review
Tuning database configuration parameters with iTuned
Proceedings of the VLDB Endowment
Combinatorial Optimization Using Electro-Optical Vector by Matrix Multiplication Architecture
OSC '09 Proceedings of the 2nd International Workshop on Optical SuperComputing
A novel and effective method for web system tuning based on feature selection
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
On the combined behavior of autonomous resource management agents
AIMS'10 Proceedings of the Mechanisms for autonomous management of networks and services, and 4th international conference on Autonomous infrastructure, management and security
A practical approach to automatic parameter-tuning of web servers
ASIAN'05 Proceedings of the 10th Asian Computing Science conference on Advances in computer science: data management on the web
Distributed resource allocation for stream data processing
HPCC'06 Proceedings of the Second international conference on High Performance Computing and Communications
URL: A unified reinforcement learning approach for autonomic cloud management
Journal of Parallel and Distributed Computing
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The overwhelming success of the Web as a mechanism for facilitating information retrieval and for conducting business transactions has ledto an increase in the deployment of complex enterprise applications. These applications typically run on Web Application Servers, which assume the burden of managing many tasks, such as concurrency, memory management, database access, etc., required by these applications. The performance of an Application Server depends heavily on appropriate configuration. Configuration is a difficult and error-prone task dueto the large number of configuration parameters and complex interactions between them. We formulate the problem of finding an optimal configuration for a given application as a black-box optimization problem. We propose a smart hill-climbing algorithm using ideas of importance sampling and Latin Hypercube Sampling (LHS). The algorithm is efficient in both searching and random sampling. It consists of estimating a local function, and then, hill-climbing in the steepest descent direction. The algorithm also learns from past searches and restarts in a smart and selective fashion using the idea of importance sampling. We have carried out extensive experiments with an on-line brokerage application running in a WebSphere environment. Empirical results demonstrate that our algorithm is more efficient than and superior to traditional heuristic methods.