A Novel Study on Data Science for Data Security and Data Integrity with Enhanced Heuristic Scheduling in Cloud

Recently, “cloud computing” has become an attractive paradigm for the administration and provisioning of internet-based services. This paradigm is known as “cloud computing.” The promise of this being a reality has been around for a while, but cloud computing has risen to meet the shifting conditions of the IT industry. In this article, we introduce the trust model, the suffrage heuristic, the Heterogeneous Earliest Finish Time (HEFT) scheduling algorithm, and the trust value computation. The trust model has parameters that vary with values of sub parameters and the tasks they perform. These functions are regarded as a standard of durability and cannot be broken. The assumption that better mappings can be made by allocating a resource or machine whose expected time of completion will suffer if that machine is not allocated is the foundation around which a suffrage heuristic will be built. Due to its great performance and stability across a variety of graph topologies, the HEFT technique is a promising solution for the Directed Acyclic Graph (DAG) and associated scheduling problem on the heterogeneous systems. The average schedule length is longer in the suffrage without trust scenario compared to the HEFT without trust, suffrage with trust, and the HEFT with trust scenarios. It is possible that there is no algorithm that, given a scheduling problem, can produce a best solution in polynomial time. Utilising random variables to find solutions to scheduling issues calls for the application of a stochastic approach as well. In addition to swarm algorithms and evolutionary algorithms, these strategies made use of Simulated Annealing (SA). The initial goal of the effort was to use a GA, and then the Firefly Algorithm (FA), to optimise the cost and performance of workflow scheduling. Algorithms like the HEFT and the GA have been proposed in this work. For applications utilising a finite set of heterogeneous processors, this HEFT serves as the underlying algorithm. We can use the GAs to find a global optimum for issues with a large and irregular search space.