In large-scale computing clusters, when the server storing a task's input data does not have sufficient computing capacity, current job schedulers either schedule the task and transmit the input data to the closest server or let the task wait until the server has sufficient computing capacity, which generates network load or task delay. To handle this problem, load balancing methods are needed to reduce the number of overloaded servers due to computing workloads. However, current load balancing methods either do not consider the computing workload or assume that it is proportional to the number of data blocks in a server. Through trace analysis, we demonstrate the diversity of computing workloads of different tasks and the necessity of balancing the computing workloads among servers. Then, we propose a cost-efficient Computing load Aware and Long-View load balancing approach (CALV). In addition to the computing load awareness, CALV is also novel in that it achieves long-term load balance by migrating out data blocks from an overloaded server that contribute more computing workloads when the server is more overloaded and contribute less computing workloads when the server is more underloaded at different epochs during a time period. CALV also has a lazy data block transmission method to improve the load balanced state and avoid network load peak. Trace-driven experiments in simulation and a real computing cluster show that CALV outperforms other methods in terms of balancing the computing workloads and cost efficiency.