Resource sharing in heterogeneous and dynamic environments like Grid or Cloud requires mapping of tasks to resources for utilization of space capacity of the lightly loaded resources. To make the resource utilization more effective, we need to know their workload in advance. The load of these resources changes dynamically in Grid/Cloud environments. It is helpful for job scheduling algorithms, if one can predict the load of these resources in advance. The efficiency of job scheduling/resource sharing algorithms depends on how accurately these algorithms predict the resource's load. In this paper, we have considered the static and dynamic versions of prediction algorithms Homeo-static, Tendency-based, Step-Ahead based algorithms for predicting the host CPU load. These prediction algorithms predict the CPU load of the resource for future interval of the time based on the resource previous load history. The experimentation is done using these algorithms and results show that the dynamic Tendency based prediction algorithm gives better prediction accuracy compared to other algorithms. © International Science Press.