We address the problem of building an integrated meta-level framework for time deliberation and parameter control for a system solving a set of hard problems. The trade-off is between the solution qualities achieved for individual problems and the global outcome under the given time-quality constraints. Each problem is modeled as an anytime optimization algorithm whose quality-time performance varies with different control parameter settings. We use the proposed meta-level strategy for generating a deliberation schedule and adaptive cooling mechanism for anytime simulated annealing (ASA) solving hard task sets. Results on task sets comprising of the traveling salesman problem (TSP) instances demonstrate the efficacy of the proposed control strategies.