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Because of widespread distribution of resources in the geographically distributed cloud environment, optimal selection of virtual machines (VMs) is one of the most important challenges for the structure of the network. This is due to the high number of data centers and VMs with different qualities of service parameters. Because of redundancy in the VMs and the high number of service parameters, optimal selection of VMs is an NP-hard problem. Therefore, a method is required, which can suggest the best VMs on the basis of the user's request and on the service-level agreements (SLAs). This study focuses on four important factors in SLAs: cost, response time, availability, and reliability.In this paper, we propose a four-tier structure, Observe, Orient, Decide, and Act, where (1) Observe is responsible for continuous monitoring users' requests and characteristics of data centers and VMs, (2) Orient is responsible for clustering data centers using fuzzy c-means and based of the four quality of services (SLA's factors) and then the selection of the most suitable data center cluster for the VM selection, (3) Decide is responsible for making decision on the most suitable VMs using multiobjective linear programming, and (4) Act is responsible for the execution of the decision. The proposed structure was implemented, and its effectiveness was evaluated through considering the number of SLA violations. KEYWORDSclustering, fuzzy c-means (FCM) algorithm, geographically distributed data centers, multiobjective linear programming (MOLP), service-level agreement (SLA), virtual machine 1820

Recommender systems are designed for offering products to the potential customers. Collaborative Filtering is known as a common way in Recommender systems which offers recommendations made by similar users in the case of entering time and previous transactions. Low accuracy of suggestions due to a database is one of the main concerns about collaborative filtering recommender systems. In this field, numerous researches have been done using associative rules for recommendation systems to improve accuracy but runtime of rule-based recommendation systems is high and cannot be used in the real world. So, many researchers suggest using evolutionary algorithms for finding relative best rules at runtime very fast. The present study investigated the works done for producing associative rules with higher speed and quality. In the first step Apriori-based algorithm will be introduced which is used for recommendation systems and then the Particle Swarm Optimization algorithm will be described and the issues of these 2 work will be discussed. Studying this research could help to know the issues in this research field and produce suggestions which have higher speed and quality. 1-IntroductionOnline business success highly relies on the ability to present personal goods, services, and information items to the potential customers. This result in willigness toward recommender systems. Through statistical methods and knoweldge discovery, these systems present services to the customers [1, 2]. Collaborating filtering system is one recommender system presents recommendation through detecting similar users based on enter date and previous transactions [3]. Collaborating filtering based recommender systems have many challenges such as recommend generation speed, database sparsity, scalability, recommends utility and so on. There have been great attempts for overcoming the collaborating filtering problems and these Attempts resulted in the high quality recommender generation. The present study reviews the previous studies in this area and examines the steps and resulted findings.Recommender systems define the information systems able to analyze the previous behaviors and present the suggestions for the current issues. In other word, recommender systems try to guess the user`s thinking through his similar behavior or other similar users in order to get the best case most appropriately to the user`s taste [1, 2].There are many types of recommender systems such as:• Content-Based [4]: the working method of content filtering, based on item content analysis and trying to understand the discipline among them for generating the recommendation.

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