Data centers (DC) host a large number of servers, computing devices and computing infrastructure, which incur significant electricity / energy. This also results in huge amount of heat produced, which if not addressed can lead to overheating of computing devices in the DC. In addition, temperature mismanagement can lead to thermal imbalance within the DC environment, which may result in the creation of hotspots. The energy consumed during the life of a hotspot is greater than the energy saved during computation. Hence, the thermal imbalance impacts on the efficiency of the cooling mechanism installed inside the DC, which can result in high energy consumption. One popular strategy to minimize energy consumption is to optimize resource allocation within the DC. However, existing scheduling strategies do not consider the ambient effect of the surrounding nodes at the time of job allocation. Moreover, thermal-aware resource scheduling as an optimization problem is a topic that is relatively understudied in the literature. Therefore, in this research, we propose a novel Game-based Thermal-Aware Resource Allocation (GTARA) strategy to reduce the thermal imbalances within the DC. Specifically, we use cooperative game theory with a Nash-bargaining solution concept to model the resource allocation as an optimization problem, where the user jobs are assigned to the computing nodes based on their thermal profiles and their potential effect on the surrounding nodes. This allows us to improve the thermal balance and avoid the hotspots. We then demonstrate the effectiveness of GTARA, TACS, TASA, and FCFS, in terms of minimizing thermal imbalance and the hotspots.