Demand response is considered as a valuable functionality of the power grid and its potential impacts continue expanding with grid modernization. Colocation data centers (simply called colocation) are recognized as a notably promising resource for demand response due to their high power demand and remarkable potential in demand management. A major challenge of colocation demand response is the split incentive, that is, colocation operators desire demand response for financial compensation while tenants may not embrace demand response due to lack of incentives. Another key challenge is caused by renewable energy co-located with data centers. Demand response mechanisms overlooking the uncertainty of renewable would cause much inefficiency in terms of energy saving and economic aspects. Existing work considers the two challenges separately in the context of data centers. By contrast, this work jointly addresses them and specially studies mechanism design for colocation data centers in presence of co-located renewable. We propose a hierarchical demand response scheme, which is based on a new two-level market mechanism that results in a win-win situation for both parties, i.e., tenants who choose to reduce power demand obtain financial rewards from the operator, while the operator receives financial compensation from the electric power company due to its tenants' demand reduction. At each demand response period, the colocation operator solicits bids (amount of energy reduction) from tenants and tenants who choose to participate responds to the operator with their bids. The proposed mechanism provably converges to a unique equilibrium solution, and at the equilibrium, neither the operator or tenants can improve their individual economic performance by changing their own strategies. Further, we present a stochastic optimization based algorithm, which uses predictions of the co-located renewable to determine the colocation operator's best strategy. At the equilibrium, the algorithm has a provable economic performance guarantee in terms of the prediction error. We finally evaluate the designed mechanism via detailed simulations and the results show the efficacy and validate the theoretical analysis for the mechanism.