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... are in the above spirit, attempting to find near-optimal or best-possible solutions instead of global optima. These include simulated annealing [14], random recursive search [18], genetic algorithms =-=[6]-=-, Tabu search [5], and hillclimbing [15, 1]. The first three are generally applicable as they require little aprioriknowledge of the problem. When the objective function has an explicit form, Hill-cli...

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... find near-optimal or best-possible solutions instead of global optima. These include simulated annealing [14], random recursive search [18], genetic algorithms [6], Tabu search [5], and hillclimbing =-=[15, 1]-=-. The first three are generally applicable as they require little aprioriknowledge of the problem. When the objective function has an explicit form, Hill-climbing could quickly reach an optimal point ...

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...reached. The way in which the temperature is decreased is known as the cooling schedule. When the cooling schedule is controlled appropriately, the algorithm is guaranteed to achieve a global optimum =-=[4]. -=-Despite its many successful applications, using simulated annealing efficiently is a bit of an art — convergence can be slow. Recursive random search [18] utilizes pure random sampling. The algorith...

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...uristic commonly used to solve global optimization problems, especially in the presence of many false minima. It was motivated by the annealing process for a material to reach the thermal equilibrium =-=[7]-=-. A simulated annealing optimization starts with a Metropolis Monte Carlo simulation at a high temperature. This means that a relatively large percentage of the random steps that result in an increase...

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... of efficiency”. Since the dimension of the problem we are dealing with is usually high, naive sample methods can become very expensive. We therefore rely on the Latin Hypercube Sampling (LHS) schem=-=e [10]-=-. LHS is considered to be an extremely efficient space-filling sampling strategy for handling high dimensions. It is considered more powerful than pure random Monte Carlo sampling. The basic idea of L...

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...o better evaluate and understand the performance of different algorithms, we assume the black-box function has an explicit form. We use one of the standard benchmark functions, the Rastrigin function =-=[13], which is defi-=-ned as: N� f(x) =N · β + (x 2 i − β · cos(2πxi)). (4.1) i=1 Figure 4 provides a 3D view of the Rastrigin function with N =2 and β =0.8 on the range [−1, 1] × [−1, 1]. Note that this ben...

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...an approach that combines queueing theory and control theory for response time regulation, the approach can be used to handle a limited and small number of parameters. Using feedback control systems, =-=[2, 9]-=- studied the problem of regulating system performance within specified QoS value. The approach works well for a small number of tuning parameters with some linear dependency assumptions. We formulate ...

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...provides a better coverage of the parameter space and allows a significant reduction in the sample size to achieve a given level of confidence without compromising the overall quality of the analysis =-=[3]-=-. Given the above advantages of LHS, we use LHS in our Smart Hill-Climbing algorithm whenever there is a need to do random sampling. In the following sections, we shall show that by using LHS instead ...

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... runs. There has been little previous work on the optimal configuration for Web application servers. In the context of HTTP servers, which have less complex interactions than Web Application Servers, =-=[11]-=- describes an Apache implementation that manages web server resources based on maximizing revenue. This approach requires substantial modifications to the Apache resource management schemes. [8] uses ...

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... find near-optimal or best-possible solutions instead of global optima. These include simulated annealing [14], random recursive search [18], genetic algorithms [6], Tabu search [5], and hillclimbing =-=[15, 1]-=-. The first three are generally applicable as they require little aprioriknowledge of the problem. When the objective function has an explicit form, Hill-climbing could quickly reach an optimal point ...

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... spirit, attempting to find near-optimal or best-possible solutions instead of global optima. These include simulated annealing [14], random recursive search [18], genetic algorithms [6], Tabu search =-=[5]-=-, and hillclimbing [15, 1]. The first three are generally applicable as they require little aprioriknowledge of the problem. When the objective function has an explicit form, Hill-climbing could quick...

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...s. [8] uses layered-queueing modeling to model business process applications. The method basically requires thorough knowledge about the software architecture and can be expensive and time-consuming. =-=[16]-=- describes an approach that combines queueing theory and control theory for response time regulation, the approach can be used to handle a limited and small number of parameters. Using feedback contro...

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...uristic search algorithms are in the above spirit, attempting to find near-optimal or best-possible solutions instead of global optima. These include simulated annealing [14], random recursive search =-=[18]-=-, genetic algorithms [6], Tabu search [5], and hillclimbing [15, 1]. The first three are generally applicable as they require little aprioriknowledge of the problem. When the objective function has an...

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...an approach that combines queueing theory and control theory for response time regulation, the approach can be used to handle a limited and small number of parameters. Using feedback control systems, =-=[2, 9]-=- studied the problem of regulating system performance within specified QoS value. The approach works well for a small number of tuning parameters with some linear dependency assumptions. We formulate ...

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...oper must configure an application server so that it can manage, for example, the concurrency of an enterprise application appropriately. In general, configuration is a difficult and error-prone task =-=[12] d-=-ue to the large number of configuration parameters and complex interactions between them — an application server may have more than a hundred parameters that can be modified. Examples of the paramet...

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...ers, [11] describes an Apache implementation that manages web server resources based on maximizing revenue. This approach requires substantial modifications to the Apache resource management schemes. =-=[8]-=- uses layered-queueing modeling to model business process applications. The method basically requires thorough knowledge about the software architecture and can be expensive and time-consuming. [16] d...

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...number of experiments. Many heuristic search algorithms are in the above spirit, attempting to find near-optimal or best-possible solutions instead of global optima. These include simulated annealing =-=[14]-=-, random recursive search [18], genetic algorithms [6], Tabu search [5], and hillclimbing [15, 1]. The first three are generally applicable as they require little aprioriknowledge of the problem. When...