Cloud Computing makes the dream of computing real as a tool and in the form of service. This internetbased ongoing technology which has brought flexibility, capacity and power of processing has realized service-oriented idea and has created a new ecosystem in the computing world with its great power and benefits. Cloud capabilities have been able to move IT industry one step forward. Nowadays, large and famous enterprise have resorted to cloud computing and have transferred their processing and storage to it. Due to popularity and progress of cloud in different organizations, cloud performance evaluation is of special importance and this evaluation can help users make right decisions. In this paper, we provide an overall perspective on cloud evaluation criteria and highlight it with help of simulation. For this purpose, we present different major factors in cloud computing performance and we analyze and evaluate cloud performance in various scenarios considering these factors.

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Increasing resource efficiency and reducing the energy consumption of cloud data centers is critical, especially during the global CORONA virus pandemic.Virtual machines' consolidation using live migration maximizes the hosts' and the reduction of energy consumption. An increase in the host's virtual machines in the consolidation process and the dynamic workload of the virtual machines may cause the overloading in the hosts. One approach to overcome this problem is reducing the hosts' virtual machines. One crucial issue to improve the quality of the consolidation process's quality is determining the best virtual machine for the migration process. Although the selection process has lower computational complexity than other challenges (like placement and overload prediction) in the consolidation process, this issue has received less attention. This article aims to present an efficient algorithm for the selection process. We first considered five main criteria for the selection process: migration time, migration risk, virtual machine connectivity, releasable resources, and penalty for SLA violation. Then, we propose an algorithm based on analytic hierarchy process multi-criteria decision-making technique. Next, to determine the weight of the proposed criteria, we simulate thousands of virtual machines of the PlanetLab workloads. These weights are tunable based on the data center preferences. The results of the suggested approach results show 23% reduction in the hosts' energy consumption, 49% reduction in the number of migrations, and 18% reduction in the SLA violation compared with other techniques. So, using the proposed method may significantly reduce the overall cost of the data centers. K E Y W O R D Sanalytic hierarchy process, cloud computing, dynamic consolidation, green cloud, MADA, virtualization INTRODUCTIONRecently, organizations, research centers, and businesses paid more attention to the advantage of using cloud data centers. These organizations prefer to use the cloud data centers to invest in private data centers and reduce the challenges and cost of their data centers. 1 So, many large companies like Amazon, Google, and Microsoft expand their large cloud data centers, which contain thousands of hosts. One of the main concerns of the data centers is the large amount of energy 1216

The software is often responsible for controlling the behavior of mechanical and electrical components, as well as interactions among these components in cyber-physical systems (CPS). The risks in CPS systems could result in losing tools, features, performance and even life. Therefore, safety analysis for software in these systems is a highly critical and serious issue. In general, safety and reliability approaches play a major role in a risk management process in CPS. In this paper, after reviewing the major techniques of software reliability and safety in CPS, an software fault tree analysis (SFTA)-based approach is presented for analysis of operational use-cases (UC) in a CPS system. In our approach, the events related to use-cases are extracted, and the related SFTA is then obtained using the proposed algorithm. Moreover, a semi-automatic method is presented in this paper to produce software failure mode and effects analysis (SFMEA) from SFTA. The results of our approach are applicable for software safety analysis in a real CPS system, including the control system of Iranian National Observatory telescope. Assessment of the suggested method is performed through numerous safety/reliability criteria and the qualitative/quantitative analysis based on these criteria.