Probabilistic Trans-Algorithmic search for automated network management and configuration

Online configuration of large-scale systems such as networks require parameter optimization to be done within a limited amount of time. This time limit is even more pressing when configuration is needed as a recovery response to a failure in the system. To quickly configure such systems in an online manner, we propose a Probabilistic Trans-Algorithmic Search (PTAS) framework which leverages multiple optimization search algorithms in an iterative manner. Essentially, PTAS applies a search algorithm to find out how to best distribute available experiment budget among multiple optimization search algorithms. Specifically, PTAS allocates experiment budget to each available search algorithm and observes each algorithm's performance on the system-at-hand. PTAS then probabilistically reallocates the experiment budget for the next round proportional to an algorithm's performance. This “roulette wheel” approach probabilistically favors the more successful algorithm in the next round. Following each round, the PTAS framework “transfers” the best found result(s) among the individual algorithms, making our framework “trans-algorithmic”. PTAS thus aims to systematize how to “search for the best search”. We show the performance of PTAS on well-known benchmark objective functions including scenarios where the objective function changes in the middle of the optimization process. To illustrate applicability of our framework to automated network management, we apply PTAS on the problem of optimizing link weights of an intra-domain routing protocol on a topology obtained from Rocketfuel dataset.