# Green Cloud Software Engineering for Big Data Processing
## 1 Introduction  
Internet of Things (IoT) is the outcome of the emanating third wave of the Internet of Everything. Gartner (2014) [1] predicts that IoT will hit mainstream by 2020 with almost 25 billion smart objects generating data. Traditional data-processing systems will be replaced with powerful big data processing systems and platforms due to the advent of voluminous and complex data generated by IoT [2]. To successfully cope with the exponential growth of IoT generated data and user processing-related demands, scalable and elastic cloud computing technologies provide extensive computational resources for fast, responsive and reliable data processing [3]. Cloud-based data center facilities that house physical networked computers and infrastructure play a crucial role in providing elastic computingresources to create an illusion of infinite resources [4]. Undeniably, such high performance and responsive computing systems consume a lot of energy. Statistically, the global energy consumption of data centers has increased by 56% within a short span of 5 years between 2005 and 2010 [5]. According to Gartner (2007) [6], data centers contribute to 2% of the global CO\({}_{2}\) emissions which is on par with that of the aviation industry. European energy policies and climate targets for the years 2020 and 2030 have energy efficiency as a core priority [7]. Data center energy consumption optimization will undoubtedly help reduce operational costs and its associated carbon footprint [8]. Nowadays, IoT, big data analytics and machine learning play a key role for better power grid infrastructure management, natural disaster-related assessment, as well as more efficient power generation and transmission [9]. Predictive analytics is beneficial for capacity planning and operational efficiency improvement. However, enormous power usage by data center's high performance (due to IoT and big data) processing infrastructures [10] calls for an urgent need to optimize data centers equipped with highly-efficient functionality facilities [11] facilitated through the implementation of intelligent algorithms [12] and IoT-based technologies [13]. Generally, the IT industry focuses on improving system performance through efficient system designs and increased number of components based on Moore's law [14]. Although output per watt is improving, the improvement in the computing system total power consumption is yet to be greatly evident. As a matter of fact, it seems to be increasing with an exponential rate [15]. This trend has thus created a situation where the server energy consumption cost has exceeded the actual hardware cost itself and this is particularly true for large-scale computing infrastructures such as cloud data centers that are greatly impacted by energy-related issues [16; 17]. ICT infrastructures and cloud providers are still looking for energy efficient solutions to address their overwhelming utility bills and carbon footprint [17]. Green Computing focuses on optimizing computing technologies and practices to reduce the negative environmental impact without compromising on performance [18]. Recently, the computing infrastructure industry has shifted their focus to energy efficiency coupled with high level of quality of service (QoS) for customers and quality in sustainability (QiS) [19]. Virtual machine (VM) consolidation aims to reduce resource utilization and energy consumption. It determines a mapping of VMs to physical hosts so that a minimum number of hosts are used [20]. It is one of the green practices where the number of active computing devices are reduced by transitioning inactive servers to 'energy saving' mode [21]. Infrastructure as a Service (IaaS) providers consider numerous metrics to define computing performance to meet service level agreements (SLA) (note a list of metrics could be found in [22; 23]). IBM has provided means to record SLA metrics [24]. A cloud-based big data processing platform for IoT systems requires elastic resources to meet processing demand needs.