Dynamic Shift from Cloud Computing to Industry 4.0: Eco-Friendly Choice or Climate Change Threat

Cloud computing utilizes thousands of Cloud Data Centres (CDC) and fulfils the demand of end-users dynamically using new technologies and paradigms such as Industry 4.0 and Internet of Things (IoT). With the emergence of Industry 4.0, the quality of cloud service has increased; however, CDC consumes a large amount of energy and produces a huge quantity of carbon footprint, which is one of the major drivers of climate change. This chapter discusses the impacts of cloud developments on climate and quantifies the carbon footprint of cloud computing in a warming world. Further, the dynamic transition from cloud computing to Industry 4.0 is discussed from an eco-friendly/climate change threat perspective. Finally, open research challenges and opportunities for prospective researchers are explored

All you can stream: Investigating the role of user behavior for greenhouse gas intensity of video streaming

The information and communication technology sector reportedly has a relevant impact on the environment. Within this sector, video streaming has been identified as a major driver of CO2-emissions. To make streaming more sustainable, environmentally relevant factors must be identified on both the user and the provider side. Hence, environmental assessments, like life cycle assessments (LCA), need to broaden their perspective from a mere technological to one that includes user decisions and behavior. However, quantitative data on user behavior (e.g. streaming duration, choice of end device and resolution) are often lacking or difficult to integrate in LCA. Additionally, identifying relevant determinants of user behavior, such as the design of streaming platforms or user motivations, may help to design streaming services that keep environmental impact at a passable level. In order to carry out assessments in such a way, interdisciplinary collaboration is necessary. Therefore, this exploratory study combined LCA with an online survey (N= 91, 7 consecutive days of assessment). Based on this dataset the use phase of online video streaming was modeled. Additionally, factors such as sociodemographic, motivational and contextual determinants were measured. Results show that CO2-intensity of video streaming depends on several factors. It is shown that for climate intensity there is a factor 10 between choosing a smart TV and smartphone for video streaming. Furthermore, results show that some factors can be tackled from provider side to reduce overall energy demand at the user side; one of which is setting a low resolution as default.Comment: 7th International Conference on ICT for Sustainability (ICT4S

Streaming Media’s Environmental Impact

This group of articles, which arose from a panel planned for the 2020 annual meeting of members of the Society for Cinema and Media Studies, draws attention to an unpopular but inescapable issue: the adverse environmental effects of streaming media. Four of these brief interventions focus on streaming media's carbon footprint, estimated by some to be 1 percent of global greenhouse gas emissions (The Shift Project 2019). This startling figure is rising at a calamitous rate as more people around the world stream more media at higher bandwidth---now exacerbated by the COVID-19 pandemic. Another factor in streaming media's environmental impact is even less welcome: the deleterious effects of higher levels of electromagnetic frequencies that media corporations' turn to fifth-generation (5G) wireless technology would exacerbate. These effects are well documented yet almost universally ignored. Despite all these findings, the notion abides that digital media are immaterial. Laura U. Marks introduces the research challenges involved in calculating the carbon footprint of streaming media and suggests actions consumers and media makers can take to mitigate this environmental threat. Joseph Clark discusses the implications of digitizing huge amounts of archival film and connects material histories of news film production, distribution, and preservation or disposal to contemporary issues of digital storage, streaming, and energy use, using the newsreel archive as a case study. Jason Livingston's contribution expands on his droll and disturbing video lecture, which presents a speculative app for mobile phones that tracks streaming, correlates it to energy use and CO~2~ emissions, and suggests methods to mitigate usage. Denise Oleksijczuk introduces scientific research on the health and environmental impacts of high levels of electromagnetic frequencies and suggests ways, including creative practice, to break through the resistance to these findings among telecommunications companies, governments, and the public. Lucas Hilderbrand focuses on best practices in teaching: how to educate our students about these impacts, and how teachers can resist increasing pressures to use streaming-based pedagogical media. Many communities around the world already rely on low-tech media, of necessity, and are often extremely innovative in their use (Marks 2017). However, network and media corporations are aggressively marketing devices and streaming platforms in both "developed" and "developing" regions (Cisco 2020). Many of the latter regions depend on fossil fuels and cannot afford to prioritize renewable energy and efficient systems. Thus streaming media's carbon footprint is not just a First World problem

Saving Energy in QoS Networked Data Centers

One of the major challenges that cloud providers face is minimizing power consumption of their data centers. To this point, majority of current research focuses on energy efficient management of resources in the Infrastructure as a Service model using virtualization and through virtual machine consolidation. However, current virtualized data centers are not designed for supporting communication–computing intensive real-time applications, such as, info-mobility applications, real-time video co-decoding. In fact, imposing hard-limits on the overall per-job delay forces the overall networked computing infrastructure to adapt quickly its resource utilization to the (possibly, unpredictable and abrupt) time fluctuations of the offered workload. Jointly, a promising approach for making networked data centers more energy-efficient is the use of traffic engineering-based method to dynamically adapt the number of active servers to match the current workload. Therefore, it is desirable to develop a flexible and robust resource allocation algorithm that automatically adapts to time-varying workload and pays close attention to the consumed energy in computing and communication in virtualized networked data centers (VNetDCs). In this thesis, we propose three new dynamic and adaptive energy-aware algorithms scheduling policies that model and manage VNetDCs. Our focuses are to propose i) admission control of the offered input traffic; ii) balanced control and dispatching of the admitted workload; iii) dynamic reconfiguration and consolidation of the Dynamic Voltage and Frequency Scaling (DVFS)-enabled Virtual Machines (VMs) instantiated onto the parallel computing platform; and, iv) rate control of the traffic injected into the TCP/IP mobile connection. Necessary and sufficient conditions for the feasibility and optimality of the proposed schedulers are also provided in closed-form. Specifically, the first approach, called VNetDC, the optimal minimum-energy scheduler for the joint adaptive load balancing and provisioning of the computing-plus-communication resources. VNetDC platforms have been considered which operate under hard real-time constraints. VNetDC has capability to adapt to the time-varying statistical features of the offered workload without requiring any a priori assumption and/or knowledge about the statistics of the processed data. Green- NetDC is the second scheduling policy that is a flexible and robust resource allocation algorithm that automatically adapts to time-varying workload and pays close attention to the consumed energy in computing and communication in VNetDCs. GreenNetDC not only ensures users the Quality of Service (through Service Level Agreements) but also achieves maximum energy saving and attains green cloud computing goals in a fully distributed fashion by utilizing the DVFS-based CPU frequencies. Finally, the last algorithm tested an efficient dynamic resource provisioning scheduler which applied in Networked Data Centers (NetDCs). This method is connected to (possibly, mobile) clients through TCP/IP-based vehicular backbones The salient features of this algorithm is that: i) It is adaptive and admits distributed scalable implementation; ii) It is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rate of the traffic delivered to the client, instantaneous goodput and total processing delay; and, iii) It explicitly accounts for the dynamic interaction between computing and networking resources, in order to maximize the resulting energy efficiency. Actual performance of the proposed scheduler in the presence of :i) client mobility; ii)wireless fading; iii)reconfiguration and two-thresholds consolidation costs of the underlying networked computing platform; and, iv)abrupt changes of the transport quality of the available TCP/IP mobile connection, is numerically tested and compared against the corresponding ones of some state-of-the-art static schedulers, under both synthetically generated and measured real-world workload traces