## 5 Conclusions  
In a grid environment, many researchers have proposed various scheduling algorithms for improving the performance of the grid system. This paper began by studying and understanding several aspects of grid computing. In the literature survey, various algorithms and methods were identified and studied. Even though many researchers proposed various scheduling algorithms, it is found that there is no efficient and effective scheduling algorithm that gives a combined solution for many issues. This research proposed efficient and effective scheduling algorithms in which various issues, such as user satisfaction, load balancing, and fault tolerance, are addressed by considering the parameters such as failure rate, load state, user deadline, and resource utilization of the resources for scheduling.  
In general, the main contributions of this work can be highlighted as follows: (1) proposing a new adaptive model to improve fault tolerance and load balancing for the grid computing environment. This model depends on an advanced fractal transform that enhances the tree model structure of the grid computing environment to enhance the network performance parameters affected by fault tolerance and load balance equally. An estimate of the fault tolerance and load balance for the network parameters was calculated based on a fractal transform. (2) The grid computing routing protocol is enhanced by improving fault tolerance with load balance estimation in a novel 2-D figure of merit. The improvement of the fault tolerance estimation is carried out by reducing replication time and message cost and this results in an increase in the probability of job completion. On the other hand, reducing mean job response time results in an enhancement of the load balance estimation, and this in turn induces an increase in the number of jobs/s. The experimental results that were found through a simulator confirmed that the suggested models can be improved by up to 98% for the system and outperform the related work by an average of 26% for job arrival rate and 33% for fault tolerance. Furthermore, by utilizing different optimization algorithms for finding the optimal number of resources (system utilization), the suggested model decreases the number of resources by an average of 75%.  
Future work may include: (1) applying the proposed model to a real-time environment. (2) The security of the proposed work has not been considered, therefore, researchers may study the security aspects of this work. (3) Some other user requirements, such as cost for execution, may be considered. In addition to that, other passive failure handling mechanisms, such as checkpointing, may be considered. (4) The jobs may arrive in a random manner. So, the dynamicity of the jobs may be considered for testing the suggested models. (5) The proposed model is tested with 64 resources and a varied number of jobs of up to 1000. In the future, the number of resources and jobs may be increased and tested as an extension of the proposed models. (6) Exploring modeling other characteristics such as input/output (I/O) behavior, memory access pattern, cache effects, and seeking to build corresponding scheduling strategies that utilize these parameters to form efficient scheduling strategies.  
Conceptualization, S.M.D., M.B.K., and O.A.H.; Methodology, S.M.D., Q.M.Z., O.A.H.; Software, O.A.H., M.B.K., and Q.M.Z.; Validation, S.M.D., O.A.H., M.B.K., and Q.M.Z.; Formal analysis, S.M.D., M.B.K., and O.A.H.; Investigation, S.M.D., M.B.K., Q.M.Z., and O.A.H.; Resources, O.A.H., M.B.K., and Q.M.Z.; Data curation, O.A.H., M.B.K., and Q.M.Z.; Writing-original draft preparation, S.M.D., M.B.K., and O.A.H.; Writing-review and editing, S.M.D., Q.M.Z., and O.A.H.; Visualization, O.A.H., M.B.K., and Q.M.Z.; Supervision, S.M.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.