This paper considers the solution of large-scale real-time optimization problems in the absence of precise knowledge of network connectivity in a distributed environment. The knowledge of the communication network is assumed to be unknown and the agents are faced with the task of ensuring that the system's unknown overall cost is minimized. Each agent can measure two unknown cost functions referred to as the local optimization cost and the local disagreement cost respectively. To tackle this problem, a distributed proportionalintegral extremum seeking control technique is proposed, one that solves both problems simultaneously. Included is a simulation example that shows the effectiveness of this technique.