In recent years, applications of cloud services have been increasingly expanded. Cloud services, are distributed infrastructures which develop the communication and services. Auto scaling is one of the most important features of cloud services which dedicates and retakes the allocated dynamic resource in proportion to the volume of requests. Scaling tries to utilize maximum power of the available resources also to use idle resources, in order to maximize the efficiency or shut down unnecessary resources to reduce the cost of running requests. In this paper, we have suggested an approach based on learning automata auto- scaling, in order to manage and optimize factors like cost, rate of violations of user-level agreements (SLA Violation) as well as stability in the presence of traffic workload. Results of simulation show that proposed approach has been able to optimize cost and rate of SLA violation in order to manage their trade off. Also, it decreases number of operation needed for scaling to increase stability of system compared to the other approaches.

In recent years, using mobile devices has a special place in human life and applicability of these devices leads to increased number of users. Business companies have integrated them with cloud computing technology and have provided mobile cloud in order to improve using mobile devices and overcome the energy consumption of mobile devices. In mobile cloud computing, computations and storages of mobile devices applications are transferred to cloud data centers and mobile devices are used merely as user interface to access services. Therefore, cloud computing will help to reduce energy consumption of mobile devices. In this paper, a new approach is given to reduce energy consumption of based on Learning Automata in mobile cloud computing. Simulation results show that our proposed approach dramatically saves energy consumption through determining the appropriate location for application.

In recent years, the advent of online data storage services has been enabled users to save their data and operational programs in cloud databases. Using an efficient and intelligent management helps to optimize quality of provided services. Also it is possible to increase throughput of services by eliminating repeated data. In following article we have offered a completely dynamic approach to detect and eliminate duplicated data which exist in shared storage resources among virtual machines. Results of simulation show that proposed approach, compared to the similar approaches, will save the storage space substantially by reducing usage of CPU, RAM, also will increase rate of de-duplication data up to 23 %.