A Novel Algorithm for Optimizing Selection of Cloud Instance Types in MultiCloud Environment With the development of cloud computing, the cloud market is becoming more and more complicated. There are many cloud providers and different cloud instance types, which brings users some confusion when they select cloud instance types. In order to solve the cloud instance type selection problem in multicloud environment, a Cloud Instance Type Selection Algorithm based on Genetic Algorithm (CITSAGA) is proposed. CITSAGA mainly includes twodimensional encoding with the constraint between adjacent genes, selection operation adopting the elite retention strategy and the roulette strategy, crossover operation using the first fit strategy, and mutation operation with mutation bounds. We perform some experiments to prove the effectiveness of the proposed CITSAGA.

An Intelligent Resource Selection Framework for Cloud Computing Environments Cloud computing provides computing resources as utility services. It offers clients with several usage scenarios. These scenarios fulfill the services requested by the clients with a lower cost as compared with deploying them locally. This paper introduces an intelligent resource selection framework that provides the client with the best set of resources in terms of computing cost and the fulfillment of clientu0027s requests. It is assumed that the requested resources can be provided to the client from multiple data centers. Additionally, cloud resources are allocated in units determined according to clientu0027s request. The client submits his resource requirements in terms of units of computing power, storage, networking, software xe2x80xa6 etc. Then, the proposed framework finds the best set of resources that satisfies the clientu0027s request. Moreover, it takes into consideration the interdependencies among resources that lead to mutually allocating certain resources together. Additionally, it considers the overhead incurred in acquiring and using the requested resources. This problem is formulated as a constrained optimization problem and has been tackled in this paper using genetic algorithms. Simulation studies show that the proposed framework provides a nearoptimal solution for the studied cases.