Earticle

In recent years, the use of cloud services has been significantly expanded. The providers of software as a service employ multi-tenant architectures to deliver services to their users. In these multi-tenant applications the resource allocation would suffer from over-utilization or under-utilization issues. Considering the significant effects of resource allocation on the service performance and cost, in this paper we have proposed an approach based on genetic algorithm for resource allocation which guarantees service quality through providing adequate resources. The proposed approach also improves system performance, meets the requirements of users and provides maximum resource efficiency. Simulation results show that the proposed approach has better response rate and availability comparing to other approaches, while provides an efficient resource usage.

In recent years, the applications of cloud computing technology has been increased significantly. Multi-tenant property of cloud computing allows data sharing between clients. The multi-tenant data management system breaks the cost of hardware, software and professional services among a large number of tenants and thus reduces per-tenant cost. A multi-tenant database system should provide high performance with small storage space and excellent scalability. One major challenge is to reach a database scheme with high-quality. In this paper, we offer a compatible ADAPTED database schema based on genetic algorithms, and compare it with similar schema. Simulation results show that the proposed approach (GA-ADAPT) besides considering criteria of quality of service, provides higher performance comparing the previous ones in terms of selection time of the important features and the average response time to the tenants.

Regarding the increasingly expanded utility of Cloud storage, the improvement of resources management in the shortest time to respond upon the users’ requests and the geographical constraints is of prime importance to both the Cloud service providers and the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is appraised by Cloud storage systems’ capability for responding to unexpected fault through software or hardware. This paper represents an algorithm based on Learning Automata–oriented approach to fault tolerance data in Cloud storage regarding traffic and query loads dispatched on data centers and learning automata that provides the best possible status for scaling up or down of data nodes. Based on appraisal of traffic on nodes, the node with the highest traffic is chosen for coping among physical nodes. The experimental results indicate that the proposed Learning Automata Fault-Tolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availibility in comparison with other similar approaches.

With the increasing use of cloud services, for both providers and users, saving energy and using needed resources regardless of time and place have got outmost importance. Resource provisioning in the cloud providers has come along availability of workloads in a given time. Most cloud providers conduct inquiries with a limited amount of resources that may be cause to reject the request of customers at the peak of workloads. Cloud federation is an approach to share resources, enhance the scalability and availability. High energy consumption is one of the current challenges for cloud providers; also it should be discussed and researched about providers’ profit as an important issue. We have presented an approach in this paper to reduce power consumption for IaaS Providers, by choosing the most appropriate host and allocating the best virtual machine which leads to satisfy users requests, save energy and reduce the cost of resources. Simulation results show that the proposed approach, compared to the other similar approaches, causes to increase utilization and turns off idle servers to decrease consumed power which followed by an increase in providers’ profit.

In recent years, the popularity of cloud computing technology is widely grown and most organizations want to use this technology in their business processes. But on the other hand, the use of this technology is not easy and many organizations are concerned about storing their sensitive data in their data centers instead of storing them in the cloud storage centers. In the cloud computing environment, trust, as a solution to enhance the security, has attracted the attention of researchers. Trust is one of the most important ways to improve the reliability of cloud computing resources provided in the cloud environment and has an important role in the business environments. Trusting the user to select the appropriate source helps in heterogeneous cloud infrastructure. In this paper, we present the trust model based on standards of appropriate service quality and speed of implementation for cloud resources. Simulation results show that the proposed model compared with similar models, in addition to taking into account measures of the quality of service, selects the most reliable source in a cloud environment by taking into account the speed of things.

In recent years, using mobile devices, in daily life, has found a special place and because of the applicability of these devices are increasing the number of users day by day. 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. Therefore, energy efficiency is a fundamental factor for mobile devices and cloud computing has the potential to save energy and power 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. The 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 significantly saves energy consumption through determining the appropriate location for application.

In recent years, cloud computing is highly embraced and more organizations consider at least some type of cloud strategy and apply theming their business process. Since failure is probable in cloud data centers and access to cloud resources available is fundamental, evaluation and application of different fault-tolerance methods is inevitable. On the other hand, the increasing growth of cloud storage users motivated us to study fault-tolerance techniques, and their strengths and weaknesses. In this paper, after introducing the concept off ault-tolerance in the context of cloud computing, the fault-tolerant techniques are presented, and after introduction of some measures, a comparative analysis is provided.

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. The Scaling tries to utilize maximum power of the available resources also to use idle resources, in order to maximize the efficiency or shutdown unnecessary resources to reduce the cost of running requests. In this paper, we have suggested an approach based on learning automata for resource auto-scaling, in order to manage and optimize factor cost. Results of simulation show that proposed approach has been able to optimize cost compared to the other approaches.

In recent years, the number of users and service providers are increasing in using cloud services so the accessibility and the effective management of the required resources, irrespective of the time and place, seem to be of great importance for both sides. Improving the performance and utilization of the cloud systems are gained by the auto-scaling of the applications; this is because of the fact that, some approaches have been proposed for auto scaling. This paper seeks to checking some value, based on the learning automata, for the scalability of the web applications, which combines virtual machine clusters and the learning automata in order to provide the best possible way for the scaling up and scaling down of the virtual machines. The results of this study indicate how an increased capacity of virtual machine which have been done by the value of thresholds could effect on SLA and overhead of responding.