A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.Comment: 31 pages, 13 figure

A selective approach for energy-aware video content adaptation decision-taking engine in android based smartphone

Rapid advancement of technology and their increasing affordability have transformed mobile devices from a means of communication to tools for socialization, entertainment, work and learning. However, advancement of battery technology and capacity is slow compared to energy need. Viewing content with high quality of experience will consume high power. In limited available energy, normal content adaptation system will decrease the content quality, hence reducing quality of experience. However, there is a need for optimizing content quality of experience (QoE) in a limited available energy. With modification and improvement, content adaptation may solve this issue. The key objective of this research is to propose a framework for energy-aware video content adaptation system to enable video delivery over the Internet. To optimise the QoE while viewing streaming video on a limited available smartphone energy, an algorithm for energy-aware video content adaptation decision-taking engine named EnVADE is proposed. The EnVADE algorithm uses selective mechanism. Selective mechanism means the video segmented into scenes and adaptation process is done based on the selected scenes. Thus, QoE can be improved. To evaluate EnVADE algorithm in term of energy efficiency, an experimental evaluation has been done. Subjective evaluation by selected respondents are also has been made using Absolute Category Rating method as recommended by ITU to evaluate EnVADE algorithm in term of QoE. In both evaluation, comparison with other methods has been made. The results show that the proposed solution is able to increase the viewing time of about 14% compared to MPEG-DASH which is an official international standard and widely used streaming method. In term of QoE subjective test, EnVADE algorithm score surpasses the score of other video streaming method. Therefore, EnVADE framework and algorithm has proven its capability as an alternative technique to stream video content with higher QoE and lower energy consumption

Cloud computing: survey on energy efficiency

International audienceCloud computing is today’s most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such great significance comes with the support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. Their share in power consumption generates between 1.1% and 1.5% of the total electricity use worldwide and is projected to rise even more. Such alarming numbers demand rethinking the energy efficiency of such infrastructures. However, before making any changes to infrastructure, an analysis of the current status is required. In this article, we perform a comprehensive analysis of an infrastructure supporting the cloud computing paradigm with regards to energy efficiency. First, we define a systematic approach for analyzing the energy efficiency of most important data center domains, including server and network equipment, as well as cloud management systems and appliances consisting of a software utilized by end users. Second, we utilize this approach for analyzing available scientific and industrial literature on state-of-the-art practices in data centers and their equipment. Finally, we extract existing challenges and highlight future research directions

Optimizing energy-efficiency for multi-core packet processing systems in a compiler framework

Network applications become increasingly computation-intensive and the amount of traffic soars unprecedentedly nowadays. Multi-core and multi-threaded techniques are thus widely employed in packet processing system to meet the changing requirement. However, the processing power cannot be fully utilized without a suitable programming environment. The compilation procedure is decisive for the quality of the code. It can largely determine the overall system performance in terms of packet throughput, individual packet latency, core utilization and energy efficiency. The thesis investigated compilation issues in networking domain first, particularly on energy consumption. And as a cornerstone for any compiler optimizations, a code analysis module for collecting program dependency is presented and incorporated into a compiler framework. With that dependency information, a strategy based on graph bi-partitioning and mapping is proposed to search for an optimal configuration in a parallel-pipeline fashion. The energy-aware extension is specifically effective in enhancing the energy-efficiency of the whole system. Finally, a generic evaluation framework for simulating the performance and energy consumption of a packet processing system is given. It accepts flexible architectural configuration and is capable of performingarbitrary code mapping. The simulation time is extremely short compared to full-fledged simulators. A set of our optimization results is gathered using the framework