Symmetrical Performance Assessment of Large-Scaled Data Using Meta-heuristic Approach

Abstract

AbstractConsidering the present situation of enhancement of computing resources, the processing as well as flow of large scaled data can be deployed towards several data intensive applications to prioritize the scalability as well as consistency of data even if in virtual platform. In such situation, the technologies can be provisioned with specific paradigms to minimize the cost linked with computing resources. The provision in virtual platform can also be able to manage the specific infrastructure obtaining the security and privacy with the applications. The association of physical components as well as the storage components in this situation can have support on resource abstraction with control while generating applications in virtual platforms. In specific term, it can be understood that the management as well as support in virtual platform should be provisioned with portability as well as interoperability requirements. During the applications in virtual machines, it has also been observed that the specified techniques somehow generate massive heterogeneous large scaled data which sometimes a big challenge as many virtual platforms are typically acquainted with homogeneous linked data along with resources. Considering the performance of virtual machines, it is observed that the heterogeneity of the databases linked with virtual machines can be sequenced and the processes associated with the services can be implemented easily through these virtual machines. Based on all the situations, in this work, the meta-heuristic techniques, i.e., ant colony optimization and firefly approach have been applied to these large-scaled data towards symmetrical performance assessment.KeywordsVirtual machineHeterogeneityMeta-heuristicPheromoneLight absorption coefficient