Cloud computing as a new and modern technology has become a focus of attention and a platform for future services. Cloud computing services have been categorized into three main groups of services, which are infrastructure, platform, and software. Each service has its own unique characteristic while all services have also some common characteristics and challenges. Such characteristics create a complicated combination of features to serve selection of cloud solutions. This paper presents a new feature based taxonomy which facilitates the process of cloud software product selection. More precisely, Hierarchical Cloud Taxonomy Engine is the proposed method to help developers and consumers choose their appropriate product according to their needs and based on real capabilities of different cloud computing products. Moreover, this paper proposes a new evaluation mechanism which sorts the cloud products according to the customer's needs and requirements. Besides, this paper presents a classification model for cloud features and subsequently presents a comparison between some business and research cloud products as a case study. Results of comparisons show that our proposed techniques facilitate the process of cloud product selection.
These days by a high increase in the amount of computation and big data gathering and analysis, everybody needs more resources. Buying more computational and storage resources are so expensive. However, cloud computing solved this problem by providing a "pay as you go" plans therefor, users will only pay for resources that they used. However, using this technology has its challenges. One of them is resource management, which is focusing on the methodologies of dedicating resources to the users with the minimum of waste. In this paper, we propose a novel energy-aware resource management technique, using the concepts of both joint VM and container consolidation approach and deep Q-Learning algorithm for green computing in cloud data centers in order to minimize the waste of resources, migration rate, and energy.
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