Reservation for Cloud Composite Service with Concurrent Request Multiplexing PROJECT TITLE : Concurrent Request Multiplexing for Cloud Composite Service Reservation ABSTRACT: It is possible to meet the increasingly varied requirements of customers in the Cloud Computing market by combining a number of atomic services into a composite value-added service. This approach shows promise. Existing studies rarely concentrate their attention on the competitive relationship that exists between users, and as a result, there is no incentive mechanism to effectively provide the request strategy of users. This is because they do not take into account the concurrency of composite service requests that are being made by multiple users. We design a composite service reservation framework in this article that takes into account the multi-user competition and interaction with the cloud provider. Within this framework, user requests can be rationally and efficiently multiplexed. Because of their inherent egotism, users typically have the expectation that they will be able to maximize their own utility in terms of the revenue, payoff, and performance of the composite service. The optimization problem is modeled from the viewpoint of game theory and characterized as a competitive game using this theoretical framework. An equivalent variational inequality problem is used to demonstrate that there is a Nash equilibrium solution for the game that has been formulated. To locate a utility-balanced request strategy, which in theory results in a Nash equilibrium solution, a new algorithm called the iterative proximate algorithm (IPA) has been proposed. In order to test and validate our theoretical analysis, we run a number of simulations of different experiments. The findings of the experiment demonstrate that IPA will eventually arrive at a Nash equilibrium after a sufficient number of iterations. Because the proposed framework gives users the ability to transfer requests from peak periods into non-peak ones, the stable request strategies have the potential to improve the utilities that users enjoy as well as the resource utilization of the cloud provider. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Mobile Edge Computing Services QoS Prediction that is Context-aware and Adaptive Cloud-Based Outsourcing for Large-Scale Non-Negative Matrix Factorization with Privacy Protection