Informatica

To provide better overall performance, identity (ID)-based signcryption (IBSC) has been constructed by combining ID-based signature (IBS) and ID-based encryption (IBE) in a secure manner. Undoubtedly, the IBSC fulfills the authentication and the confidentiality by signature and encryption, respectively. All the previously proposed IBSC schemes are inseparable in the sense that the two-layer sign-then-encrypt procedure must be performed only by the same entity. However, the entities, such as wireless sensors and smart cards, are resource-constrained and become time consuming in executing the two-layer sign-then-encrypt procedure. Nowadays, the usage of mobile cloud computing is gaining expanding interest which provides scalable and virtualized services over the Internet or wireless networks while users with resource-constrained devices can enjoy the advantages of mobile cloud computing environments. Hence, we aim to reduce the computational cost for resource-constrained devices by employing a third party. In this article, we present the first separable ID-based signcryption (SIBSC) scheme in which the signing and encrypting layers are performed by the device and a third party, respectively. Under the computation Diffie–Hellman (CDH) and bilinear Diffie–Hellman (BDH) assumptions, we demonstrate that the proposed SIBSC scheme offers the provable security of authentication and confidentiality while retaining communication performance.

While users increasingly use such large multimedia data, more people use the cloud computing technology. It is necessary to manage large data in an efficient way, and to consider transmission efficiency for multimedia data of different quality. To this end, an important thing is to ensure efficient distribution of important resources (CPU, network and storage) which constitute cloud computing, and variable distribution algorithms are required therefor. This study proposes a method of designing a scheme for applying MapReduce of the FP-Growth algorithm which is one of data mining methods based on the Hadoop platform at the stage of IaaS (Infrastructure As a Service) including CPU, networking and storages. The method is then for allocating resources with the scheme.

This study proposes a model for supporting the decision making process of the cloud policy for the deployment of virtual machines in cloud environments. We explore two configurations, the static case in which virtual machines are generated according to the cloud orchestration, and the dynamic case in which virtual machines are reactively adapted according to the job submissions, using migration, for optimizing performance time metrics. We integrate both solutions in the same simulator for measuring the performance of various combinations of virtual machines, jobs and hosts in terms of the average execution and total simulation time. We conclude that the dynamic configuration is prosperus as it offers optimized job execution performance.