| dc.description.abstract | Nowadays, Web Services (WS) play an important role in the dissemination and distributed processing of large amounts of data that become available on the Web. In many cases, it is essential to retrieve and process such data in blocks, in order to benefit from pipelined parallelism and reduced communication costs. This article deals with the problem of minimizing at runtime, in a self-managing way, the total response time of a call to a database exposed to a volatile environment, like the Grid, as a WS. Typically, in this scenario, response time exhibits a concave, nonlinear behavior depending on the client-controlled size of the individual requests comprising a fixed size task. In addition, no accurate profiling or internal state information is available, and the optimum point is volatile. This situation is encountered in several systems, such as WS Management Systems (WSMS) for DBMS-like data management over wide area service-based networks, and the widely spread OGSA-DAI WS for accessing and integrating traditional DBMS. The main challenges in this problem apart from the unavailability of a model, include the presence of noise, which incurs local minima, the volatility of the environment, which results in moving optimum operating point, and the requirements for fast convergence to the optimal size of the request from the side of the client rather than of the server, and for low overshooting. Two solutions are presented in this work, which fall into the broader areas of runtime optimization and switching extremum control. They incorporate heuristics to avoid local optimal points, and address all the aforementioned challenges. The effectiveness of the solutions is verified via both empirical evaluation in real cases and simulations, which show that significant performance benefits can be provided rendering obsolete the need for detailed profiling of the WS. © 2008 ACM. | en |
