## 2 Literature Review  
There has been a lot of ongoing research related to green computing (GC) where researchers have been trying to implement green data center using many different technologies. In this section of the paper, we are reviewing some of the work done by other scholars. Authors in [4] are having a closer look at the reasons behind the growth of SBC clusters. Indeed, SBC clusters have reduced footprint, have less energy requirements, and  
Figure 3: IoT architecture including the edge platform. are cheaper which gives computational game changing results. According to the authors, SBC clusters are meant for educational purposes due to their light weight and low-cost. They also make good testbeds for outstanding technologies such as high-core density and ARM based architectures. The authors also identified a set of use cases where SBCs are seen as a strong future especially when more advanced and more powerful. In [5], the authors analyze the performance, the efficiency, the value-for-money, and scalability of new SBC clusters based on Raspberry Pi 3 Model B, Raspberry Pi 3 Model B+, and Odroid C2, which is a competing platform. The authors mentioned that SBCs were not a practical option because of their low compute power. For the analysis purposes in their paper, the authors developed a new SBC clustering technique that is called the Pi Stack. This is a new construction that allows for an increased cluster density and improved power proportionality and control allowing SBC clusters to become useful compute infrastructures. The work done in [6] evaluates the energy efficiency and the performance of single board computer based clusters. The authors carried out extensive testing to analyze the performance of these clusters using popular performance benchmarks. The layout consists of two clusters each containing 20 nodes. They used popular benchmarks for task execution time, memory/storage utilization, network throughput, and energy consumption. They found out that SBC clusters come with a lot of low-cost and low energy consumption advantages, however, the processing power remains significantly limited. Edge computing has also been notably present in the literature recently. The authors in [7] discuss the emergence of the edge computing as a disruptive technology. It is supposed to deliver highly responsive cloud services for mobile computing. The concept of edge computing allows substantial computing and storage resources (cloudlets, micro datacenters, fog nodes) to be placed at the edge of the internet to be closer to mobile devices and sensors. Proximity is a real matter as it affects the end-to-end latency. This problem can be overcome if sufficient efforts are made in this sense. Proximity of cloudlets has a direct effect on delivering highly responsive cloud services, scalability via edge analytics, privacy-policy enforcement, and cloaking cloud outages. Although the edge brings many benefits, it still faces many technical and nontechnical challenges. The technical problems mainly reside in the algorithms needed for the collective control and sharing of cloudlets in distributed computing. As far as the nontechnical side is concerned, the biggest unknown is the possible business models for deploying cloudlets. Many industries, communities, and organizations will have to work hand-in-hand for the success of this deployment. The work done in [8] describes mobile edge computing (MEC) as an emergent architecture consisting of having cloud computing services extended to the edge via exploiting mobile base stations. The authors present a comprehensive survey where they provide the definition of MEC along with relevant research and technological developments in the area. They also describe its advantages, architectures, and different application areas. In addition to that, they give an overview of security and privacy issues related to MEC and their solutions. Many of the challenges and opportunities related to edge computing were mentioned in [9].