Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

The fast increment in the number of IoT (Internet of Things) devices is accelerating the research on new solutions to make cloud services scalable. In this context, the novel concept of fog computing as well as the combined fog-to-cloud computing paradigm is becoming essential to decentralize the cloud, while bringing the services closer to the end-system. This paper surveys on the application layer communication protocols to fulfill the IoT communication requirements, and their potential for implementation in fog-and cloud-based IoT systems. To this end, the paper first briefly presents potential protocol candidates, including request-reply and publish-subscribe protocols. After that, the paper surveys these protocols based on their main characteristics, as well as the main performance issues, including latency, energy consumption and network throughput. These findings are thereafter used to place the protocols in each segment of the system (IoT, fog, cloud), and thus opens up the discussion on their choice, interoperability and wider system integration. The survey is expected to be useful to system architects and protocol designers when choosing the communication protocols in an integrated IoT-to-fog-to-cloud system architecture. Continuous innovations in hardware, software and connection solutions in the last decade have lead to the expansion of the Internet of Things (IoT) with the number of connected devices growing by the day [1] [2]. The huge amount of data generated by these devices require to find a proper system architecture able to both process and store all the data. While cloud-based architectures are being currently used for that purpose, the new fog computing paradigm is envisioned to scale and optimize the IoT infrastructures [3]. Examples of the cloud-based IoT solutions have been proposed in [4], [5], [6] and a detailed analysis of properties for IoT cloud providers has been conducted in [7]. These studies have shown that cloud computing has the potential to satisfy many IoT requirements, such as monitoring of services, powerful processing of sensor data streams and visualization tasks. On the other hand, fog-based solutions are suited to address real-time processing, fast data response, and latency issues, thus extending the cloud capabilities closer to the edge of the network [8]. Among many factors that will determine the performance in a combined IoT, fog and cloud computing paradigm, the application layer communication, which in turn depends on the selected communication protocols, is one of the main ones.Despite the popularity and wide spread usage of HTTP, the currently used protocols in various domains of IoT, fog and cloud domains are de-facto fragmented with many different solutions. This is due to the different requirements and areas that IoT needs to cover, combining the functionalities of sensors, actuators and computing power with security, connectivity and a myriad of other features. As a result, there is no common agreement on the reference architecture or adopted standards of co...

Cloud Computing is a model of service delivery and access where dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a new horizon of opportunity for enterprises. It introduces new operating and business models that allow customers to pay for the resources they effectively use, instead of making heavy upfront investments. The biggest challenge in Cloud Computing is the lack of a de facto standard or single architectural method, which can meet the requirements of an enterprise cloud approach. In this paper, we explore the architectural features of Cloud Computing and classify them according to the requirements of end-users, enterprises that use the cloud as a platform, and cloud providers themselves. We show that several architectural features will play a major role in the adoption of the Cloud Computing paradigm as a mainstream commodity in the enterprise world. This paper also provides key guidelines to software architects and Cloud Computing application developers for creating future architectures.

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-The recent advances in the cloud services technology are fueling a plethora of information technology innovation, including networking, storage and computing. Today, various flavors have evolved of Internet of Things (IoT), cloud computing and the so-called fog computing, -a concept referred to capabilities of edge-devices and user's clients to compute, store and exchange data among each other and with the cloud. Though the evolution was not easily foreseeable to happen at such a rapid pace, each piece of it today facilitates and enables the deployment of what we commonly refer to as a smart scenario, including smart cities, smart transportation and smart homes. As most of the cloud, fog and network services today run simultaneously in each scenario, we observe that we are at the dawn of what maybe the next big step in the cloud computing and networking evolution, whereby services might be executed at the network edge, both in parallel and in a coordinated fashion, as well as supported by the unstoppable technology evolution. As edge devices become richer in functionality and smarter, -embedding capacities such as storage or processing, as well as embedding new functionalities, such as decision making, data collection and forwarding, sharing, etc, a real need is emerging for coordinated management of fog-to-cloud (F2C) computing systems. This paper introduces a layered fog-to-cloud (F2C) architecture, its benefits and strengths as well as the arising open and research challenges, making the case for the real need for their coordinated management. Our architecture, the illustrative use case presented and a comparative performance analysis, albeit conceptual, all clearly show the way forward towards a new IoT scenario with a set of existing and unforeseen services provided on a highly distributed and dynamic compute, storage and networking resources, bringing together heterogeneous and commodity edge devices, emerging fogs as well as conventional clouds. Keywords-Cloud computing, fog computing, fog-to-cloud, Internet of Things (IoT) I. INTRODUCTION: THE SCENARIOThe most recent developments in the information and communications technologies area have started to make a profound impact, through massive connectivity of humans and computers, as well as a massive proliferation of edge devices carried by humans (i.e., smart phones, and those associated with all the surroundings -the Internet of Things). These two major commodities not only have facilitated the true "anywhere, anyhow, anytime" users' connectivity, but also the data collection, further enabling the deployment of new value-added services. Today, the scenarios of smart cities, smart transportation and smart homes are no more domain of research of distant future, but are becoming the new "normal". Several references can be found in the literature that already showed the notable effect these concepts can bring to the business market [1]. For a rapid business and technological success to happen, however, two inherent features need to be addressed in t...

Network Function Virtualization (NFV) is a new paradigm, enabling service innovation through virtualization of traditional network functions located flexibly in the network in form of Virtual Network Functions (VNFs). Since VNFs can only be placed onto servers located in networked data centers, which is the NFV's salient feature, the traffic directed to these data center areas has significant impact on network load balancing. Network load balancing can be even more critical for an ordered sequence of VNFs, also known as Service Function Chains (SFCs), a common cloud and network service approach today. To balance the network load, VNF's can be placed in a smaller cluster of servers in the network thus minimizing the distance to the data center. The optimization of the placement of these clusters is a challenge as also other factors need to be considered, such as the resource utilization. To address this issue, we study the problem of VNF placement with replications, and especially the potential of VNFs replications to help load balance the network. We design and compare three optimization methods, including Linear Programing (LP) model, Genetic Algorithm (GA) and Random Fit Placement Algorithm (RFPA) for the allocation and replication of VNFs. Our results show that the optimum placement and replication can significantly improve load balancing, for which we also propose a GA heuristics applicable to larger networks.

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of animal welfare in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of smart computing and sensing is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare.

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