Cluster computing has emerged as a new paradigm for solving large-scale problems. To enhance QoS and provide performance guarantees in cluster computing environments, various real-time scheduling algorithms and workload models have been investigated. Computational loads that can be arbitrarily divided into independent pieces represent many real-world applications. Divisible load theory (DLT) provides insight into distribution strategies for such computations. However, the problem of providing performance guarantees to divisible load applications has not yet been systematically studied. This paper investigates such algorithms for a cluster environment. Design parameters that affect the performance of these algorithms and scenarios when the choice of these parameters have significant effects are studied. A novel algorithmic approach integrating DLT and EDF (earliest deadline first) scheduling is proposed. For comparison, we also propose a heuristic algorithm. Intensive experimental results show that the application of DLT to real-time cluster-based scheduling leads to significantly better scheduling approaches.