## 2 Related Work  
[PERSON] (2005) [14] suggested a grid load balancing approach utilizing artificial intelligence to accomplish efficient workload and resource management. A mixture of smart agents and multi-agent methods is implemented in local grid resource scheduling and global grid load balancing. Each agent represents a local grid resource that uses predictive device output data with incremental heuristic algorithms to maintain local load balance throughout different servers. [PERSON] and [PERSON] (2006) [15] presented a layered balancing algorithm focused on tree representation. This model turns every grid design into a four-level, specific tree. It generates a two-level sub-tree for each site. This tree's leaves reflect a site's computational components, and the root represents a site-related virtual node. These sub-trees, referring to cluster locations, are clustered together to create a three-level sub-tree. Finally, such sub-trees are clustered together, creating a four-level tree called the generic load balancing model. [PERSON] (2009) [16] offered a hybrid network load policy integrating static or dynamic task scheduling architectures. The static load balancing policy is used to choose an appropriate and convenient node set. As a node shows potential failure to continue supplying services, the complex load balancing strategy can decide whether or not the node involved is successful in providing load distribution. In a short period, the device can receive a new substitute node to preserve system efficiency. [PERSON] et al. (2012) [17] recommended a load structured Min-Min algorithm, mainly implemented to reduce start making span and increase resource utilization in the heterogeneous network. It is introduced in two steps. The Min-Min method is adopted in the first phase to schedule tasks. In the second stage, activities in crowded resources are postponed to effectively use underused resources. The authors in [18] introduced an augmenting hierarchical load balancing. The possibility of deviation of mean system load from cluster load is calculated as well as checked for containment within a specified range from 0 to 1. The best tools are allocated to jobs by matching the predicted work computing capacity with cluster average computer power. The authors provided a grouping-based schedule of data storage for fine-grained work. It groups fine-grained jobs to form coarse-grained jobs based on resource processing capability and grouped job processing requirements. The analysts round about an asset and calculate the MIPS product and granularity time used to estimate the overall number of jobs that could be completed within the same specified timeframe. Then they select the resource with the required amount of waiting jobs [19]. [PERSON] (2012) [20] addressed numerous fault recovery processes, including checkpointing, replication, and rescheduling. Having to take checkpoints is the process of regularly saving the status of a permanent storage process. This makes a process that fails to restart from the point of last saving its state or checkpoint on another resource. Replication involves maintaining a sufficient amount of replicas or copies of parallel systems on various resources such that at least one copy succeeds. The rescheduling procedure finds various resources to reschedule failures. The authors in [21] built a grid fault-tolerant scheduling method to plan backups and minimize job response time. In this approach, directed acyclic graphs model jobs. They schedule jobs with delays to avoid execution failures even with the appearance of processor faults. Initially, a communication framework is designed that defines when contact between such a backup and successor backups is needed. Then any processor malfunction will initiate the backup. This minimizes reply time and expense. The authors in [22] introduced a fault tolerance checkpointing mechanism.