In this paper, we study the problem of task reallocation for load-balancing in distributed data processing models that tackle vast amount of data. In this context, we propose a novel strategy based on cooperative agents used to optimise the rescheduling of tasks for multiple jobs submitted by users in order to be executed as soon as possible. It allows an agent to determine locally the next task to process and the next task to delegate according to its knowledge, its own belief base and its peer modelling. The novelty of our strategy lies in the ability of agents to identify opportunities and limiting factor agents, and afterwards to reallocate some of the tasks. Our contribution is that, thanks to concurrent bilateral negotiations, tasks are continuously reallocated according to the local perception and the peer modelling of agents. In order to evaluate the responsiveness of our approach, we implement a prototype testbed and our experimentation reveals that our strategy reaches a flowtime which is close to the one reached by the classical heuristic approach and significantly reduces the rescheduling time.