Owing to the complexity and complex hierarchical design (heterogeneity) of grid networks, task scheduling in the grid systems is more complicated than in conventional distributed computing systems. Load handling systems may be categorized as clustered (centralized) or distributed (decentralized) and dynamic or static, as well as periodic or non-periodic. Powerful and effective algorithms that have been proposed are critical to improving global grid power performance. Load balancing’s key objective is to have a distributed low-cost scheme that spreads the demand over all processors. As a consequence, load balancing and task fault tolerance in a grid setting will greatly affect grid performance. Due to some reasons, while processing any task, a defective device may trigger some harm. Resources are complex in design, so power load changes with changing grid configuration. The task can move from a highly loaded node to a loaded server. The highly loaded resources will act as a server of the task and the loosely loaded resources will act as a receiver of the task. The scheduling mechanism leads the work to sufficient resources and the tracking mechanism tracks resources. The next phase is scheduling and monitoring. Resource exploration is resource management’s first step. Grid management information is characterized as the process of defining specifications, matching resources with applications, resource allocation, scheduling, and tracking system resources over time so that grid applications can operate as efficiently as possible. The aim of grid computing is to establish the appearance of a simple yet efficient self-managing virtual machine from a wide set of linked heterogeneous networks sharing different resource combinations. Grid computing is adopted in different fields, from university science to government application. Grid technology has arisen as modern, high-performance-oriented, large-scale distributed networking. In the future, all storage systems will use fractal tree indexes. Fractal tree properties include log(N) arrays, one array for each power of two, fractal tree indexes can use 1/100th the power of B-trees, and fractal tree indexes ride the right technology trends. Concepts from fractal theory have been applied to several tasks in data mining and data analysis, such as selectivity estimation, clustering, time series forecasting, correlation detection, and data distribution analysis. Almost all natural objects can be observed as fractals. They are mathematical sets with a high degree of geometrical complexity that can model many natural phenomena. They are crinkly objects that defy conventional measures, such as length, and are most often characterized by their fractal dimension. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate.įractals are of a rough or fragmented geometric shape that can be subdivided into parts, each of which is a reduced copy of the whole. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. The main drawback of this technique is the long computing time. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying.
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