Abstract
In recent decades, there exists a wide increase in traffic of clouds due to the enormous increase of media content. The popularity of cloud is gaining its attention due to its ease of use and flexible model but suffers from poor resource2 management and its minimal extendibility of service portfolio. However, with recent advancement, the services are effectively managed, and its discovery is made further possible. To handle larger amounts of multimedia contents in a standalone cloud, deployment of wide operable systems is yet required that handles the data effectively with increasing demands of the user. A resource allocation framework is designed in this paper that uses a gray wolf optimization (GWO) architecture to effectively learn the operation of resource allocation in an optimal manner. For optimal service provisioning and scalability, the cloud at times communicates with each other based on the resource allocated by the deep neural network, and then the resources are shared. Such a scenario forms the multi-cloud computing, and the resource management using the deep neural network ensures trivial solutions on poor scalability. The deep neural network acts as a model for controlling the routing capabilities based on the input data rate and the storage space available in the multi-clouds. The deep neural network operates in such a way that it reduces the delay in processing and storage of data to cloud that ensures flexible operations across the cloud. The entire operation is divided into two modules: the first module operates on data processing and routing operations, and the second module acts as a control plane using the deep neural network that ensures optimal allocation of resources based on the data obtained and processed in the first module. These two models ensure better delivery of data to the cloud with proper allocation and storage of resources in the multi-cloud environment. The simulation is conducted using Java (netbeans) platform, and it is evaluated further using CloudSim toolkit. The results are experimented on various performance metrics that includes time delay and cost of resource allocation on multi-cloud.
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Sangeetha, S.B., Sabitha, R., Dhiyanesh, B., Kiruthiga, G., Yuvaraj, N., Raja, R.A. (2022). Resource Management Framework Using Deep Neural Networks in Multi-Cloud Environment. In: Nagarajan, R., Raj, P., Thirunavukarasu, R. (eds) Operationalizing Multi-Cloud Environments. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-74402-1_5
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