Review of Grid-Cloud Distributed Environments: Fusion Fault Tolerance Replication Model and Fragmentation

1. Introduction

Cloud computing (Dhamodaran S, et al. 2023) and grid computing (Pandey, B. K. et al. 2023) are frequently confused and have similar conceptual foundations. Both approaches have the same objective of providing services to consumers via resource sharing across a large user base, and their principles are quite similar. Because they are both network-based and multitasking (Talukdar, V. et al. 2023), users in various places may access one or more program instances to do various tasks. Grid computing uses virtualized computer resources to store massive volumes of data, while cloud computing uses an internet service to provide an application with indirect, rather than direct, access to resources. While cloud computing maintains resources centrally, grid computing distributes resources across grids.

With the aid of a group of networked computers that collaborate to solve an issue, grid computing is a network-based computational paradigm that can manage massive volumes of data. In essence, it is a vast computer network that collaborates to find a solution to a common issue by dividing it into many smaller components known as grids. Because of its distributed design, tasks are presumably planned and handled without consideration for time. As shown in Figure 1, the collection of PCs performs the role of a virtual supercomputer, offering scalable and seamless access to geographically dispersed wide-area computing resources and presenting them as a single, cohesive resource for carrying out massive tasks like data processing.

Figure 1.

Grid computing

Cloud computing is a kind of internet-based computing where an application shares resources to build a big resource pool instead of directly accessing resources. It is a contemporary computing paradigm designed with the purpose of remotely providing quantifiable and scalable IT resources. It is based on network technology. It eliminates the need for large investments in local infrastructure by providing on-demand access to a shared pool of dynamically configured computer resources and higher-level services. The widely dispersed computer resources are controlled centrally. Users do not need to know the precise location of their data in order to use software and program from anywhere. Which translates to “pay only for what you require.” as seen in Figure 2.

Figure 2.

Cloud computing

By distributing resources across several servers in clusters, grid and cloud computing are network-based computing technologies that make effective and efficient use of computer resources (Veeraiah, V. et al. 2023). This makes acquiring hardware and software for application construction easier. By itself, grid computing is a computer approach that pools resources from several fields to accomplish a common goal. In order to store enormous quantities of data, grid computing involves virtualizing computer resources, which is reviewed in this work. Ascending the spectrum, cloud computing involves a program that, instead of directly accessing resources and data, does so indirectly via an internet service. Data are distributed via a centralized or decentralized replication mechanism to a predefined geographically dispersed environment. Fragmentation imputes data into multiple little independent fragments. Replication maintains data transparency in distributed database systems by keeping several copies at each location, contingent on user access or work behavior. In a Grid Computing or Cloud Computing context, a system loses confidence or malfunctions when it does not perform as planned. In addition, fusion fault tolerance in distributed grid-cloud setups is reviewed in this work. The structure of this article is as follows: a review of the literature serves as the foundation for the discussions that follow in the first part. The fragmentation is reviewed in the second part. The replication is reviewed in the third part. Fault Tolerance is covered in Section 4. The study's conclusion is given in the last part.