Authentication Model for Cloud Service Clients
In the last few years, Cloud computing has become essential in the research, industry and business. On the other hands, Cloud computing is based on virtualization over IT resources where virtual storage and computing services are provided. Therefore, trusting of both cloud providers and consumers is considered a critical factor to enhance reliability and security of the cloud environment. Although there are many research studies concern about establishing trust for cloud service providers. In this project, a trust Model has been proposed to determine the trust for cloud consumers. The main component of the proposed trust model is the trust metric stage. The function of this stage is to define the trust percentage for each consumer. Four enhanced techniques have been used and implemented in this stage: Particle Swarm Optimization, Multiple Regression, Analytic Hierarchical Process and MR-PSO.
As Iam the team lead of the project, I have done most of the part and remaining was done by other 2 members. I have developed the website with the help of our project guide and also done the project documentation.
Front-end : Core Java, J2EE(Servlets,Jsp)
Back-end : My SQL
Operating System : Windows 10
IDE : Eclipse
A Particle Swarm Optimization with Levy Flight for Service Caching and Task Offloading in Edge - Cloud Computing
Edge-cloud computing is an efficient approach to address the high latency issue in mobile cloud computing for service provisioning, by placing several computing resources close to end devices. To improve the user satisfaction and the resource efficiency, this project focuses on the task Offloading and service caching problem for providing services by Edge-cloud computing. This project formulates the problem as a constrained discrete optimization problem and proposes a hybrid heuristic method based on Particle Swarm Optimization(PSO) to solve the problem in polynomial time. The proposed method, LMPSO, exploit PSO to solve the service caching problem. To avoid PSO trapping into local optimization, LMPSO adds levy flight movement for particle updating to improve the diversity of particle.
As Iam the team lead of the project, I have done most of the part and remaining was done by other 2 members. I have developed the website with the help of our project guide and also done the project documentation.
Front-end : J2EE(Servlets,Jsp)
Back-end : My SQL
Operating System : Windows 10
IDE : Eclipse