## 3 Materials and Method  
Figure 1 shows the complete proposed model for improving fault tolerance and load balance based on the fractal transform. The first step is to estimate grid computing service (GCS) parameters, including cost, job queue, task schedule, cluster size, grid size, and the number of resources, then mapping this grid structure into a distributed R-tree index structure enhanced by the entropy method to reduce the completion time of the decision-maker. Finally, the threshold machine is used to choose the route path based on load balance as well as the fault tolerance device. The migrant controller is used to increase fault tolerance and self-stabilizing control is used to increase cumulative state load balancing. Each consumer submits their computational and hardware specifications to the GCS. The GCS can respond by submitting the results when the job execution is done.  
In the GCS, jobs pass through four stages, which can be summarized as follows: (1) task submission phase: grid users can submit their jobs through the available web browsers. This makes the work submission process simple and open to any number of clients. (2) Task allocation phase: when the GCS receives work, it searches for the resources available (computers or processors) and assigns the necessary resources for the task. (3) Task execution phase: until the services available are committed to the assignment, the task is planned to be carried out on that computer location. (4) Results collection phase: when the jobs have been done, the GCS will alert the users of the results of their work. The proposed model would examine network parameters; GCS estimates by evaluating the top-down view of the grid model, which includes the local grid manager (LGM), site manager (SM), and processing elements (PEs) [34]. In this hierarchy, incorporating or deleting SMs or PEs becomes very versatile and tends to render the proposed grid computing service model open and scalable.  
The LGM's task is to assess information regarding active resources depends on its SMs. LGMs also participate in grid-specific tasks and load balancing. New SMs can enter the GCS by sending a message to join the nearest LGM parent. Each SM is capable of managing a dynamically configured pool of processing units (computers as well as processors) (i.e., processing elements can join the pool anytime). A new computing element should be registered within the SM. The SM's function is to gather information regarding active input nodes in its pool. The details gathered contains CPU speed and other hardware measurements. Any SM is also responsible for allocating incoming jobs to any processor core in its pool using a defined load balancing. Any public or private PC or workstation can enter the grid system by signing up with any SM and offering grid users their computing resources.  
As a computational unit enters the grid, it begins the GCS framework that will submit any details about its capabilities, such as processor power, to the SM. Any LGM is the grid model's web server. Using the web browser, customers assign their computer science jobs to an associated LGM. According to the load balancing relevant data, the LGM will pass the published jobs to a suitable SM. The SM, in turn, distributes these computing jobs according to available site high availability information to a selected execution processing element.