DYNAMIC RESOURCE ALLCATION METHOD BASED ON SYMBIOTIC ORGANISM SEARCH ALGORITHM IN CLOUD COMPUTING

Cloud computing is gaining popularity among small and medium-sized enterprises.The cost of cloud resources plays a significant role for these companies and this is why cloud resource optimization has become a very important issue.Numerous methods have been proposed to optimize cloud computing resources according to actual demand and to reduce the cost of cloud services.Such approaches mostly focus on a single factor (i.e., compute power) optimization, but this can yield unsatisfactory results in real-world cloud workloads which are multi-factor, dynamic and irregular.This article presents a novel approach which uses anomaly detection, machine learning and particle swarm optimization to achieve a cost-optimal cloud resource configuration.Cloud computing offers on-demand availability of computing resources over the Internet.To attract users, cloud providers offer their resources as services at reasonable prices and provide various price models to reflect higher level of quality of service (QoS), which are referred as pricing schemes.k-tims anonymous authentication (k-TAA) is an attractive approach to construct pricing schemes, providing access controllability, user anonymity and public traceability.In k-TAA schemes, authenticated users are permitted to anonymously access services from a provider at most k times.We first formalize the definition and security model for k-TAA-PAYG scheme.Subsequently, we present a concrete construction of k-TAA-PAYG scheme, with the computational complexity as O(1).