Abstract

The term "cloud computing" refers to a concept of resource sharing that enables ubiquitous, convenient, and on-demand network access to a shared pool of configurable computer resources that are provided by commercial providers in accordance with certain service level agreements. The use of cloud computing is significant for a number of reasons, including data analysis and storage. A cloud broker acts as a go-between for their customers and the various service providers. Requests can be made by the client to the internet broker. The cloud broker is responsible for matching the client's request with the various offerings that are made available by the service's provider. The problem of cloud brokerage can be formulated as a multi-objective optimization challenge, with the following three goals in mind: decreasing the amount of time it takes to respond to requests from customers; limiting the amount of energy that is consumed; and maximizing the amount of money that is made by the cloud broker. To overcome this problem using various optimization techniques can be compared. The performance of the cloud brokerage system is compared with that the multi-objective particle swarms optimization, genetic algorithm, and random search algorithm and ant colony optimization

Description