document: A smart hill-climbing algorithm for application server configuration 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 .
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