Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio

A Review Of Cloud Manufacturing: Issues And Opportunities

Cloud Manufacturing (CM) is the latest manufacturing paradigm that enables manufacturing to be looked upon as a service industry.The aim is to offer manufacturing as a service so that an individual or organization is willing to manufacture products and utilize this service without having to make capital investment.However,industry adoption of CM paradigm is still limited.This paper compared the current adoption of CM by the industry with the ideal CM environment.The gaps between the two were identified and related research topics were reviewed. This paper also outlined research areas to be pursued to facilitate CM adoption by the manufacturing industry.This will also improve manufacturing resource utilization efficiencies not only within an organization but globally.At the end,the cost benefits will be passed down to end customer

5G-PPP Technology Board:Delivery of 5G Services Indoors - the wireless wire challenge and solutions

The 5G Public Private Partnership (5G PPP) has focused its research and innovation activities mainly on outdoor use cases and supporting the user and its applications while on the move. However, many use cases inherently apply in indoor environments whereas their requirements are not always properly reflected by the requirements eminent for outdoor applications. The best example for indoor applications can be found is the Industry 4.0 vertical, in which most described use cases are occurring in a manufacturing hall. Other environments exhibit similar characteristics such as commercial spaces in offices, shopping malls and commercial buildings. We can find further similar environments in the media & entertainment sector, culture sector with museums and the transportation sector with metro tunnels. Finally in the residential space we can observe a strong trend for wireless connectivity of appliances and devices in the home. Some of these spaces are exhibiting very high requirements among others in terms of device density, high-accuracy localisation, reliability, latency, time sensitivity, coverage and service continuity. The delivery of 5G services to these spaces has to consider the specificities of the indoor environments, in which the radio propagation characteristics are different and in the case of deep indoor scenarios, external radio signals cannot penetrate building construction materials. Furthermore, these spaces are usually “polluted” by existing wireless technologies, causing a multitude of interreference issues with 5G radio technologies. Nevertheless, there exist cases in which the co-existence of 5G new radio and other radio technologies may be sensible, such as for offloading local traffic. In any case the deployment of networks indoors is advised to consider and be planned along existing infrastructure, like powerlines and available shafts for other utilities. Finally indoor environments expose administrative cross-domain issues, and in some cases so called non-public networks, foreseen by 3GPP, could be an attractive deployment model for the owner/tenant of a private space and for the mobile network operators serving the area. Technology-wise there exist a number of solutions for indoor RAN deployment, ranging from small cell architectures, optical wireless/visual light communication, and THz communication utilising reconfigurable intelligent surfaces. For service delivery the concept of multi-access edge computing is well tailored to host virtual network functions needed in the indoor environment, including but not limited to functions supporting localisation, security, load balancing, video optimisation and multi-source streaming. Measurements of key performance indicators in indoor environments indicate that with proper planning and consideration of the environment characteristics, available solutions can deliver on the expectations. Measurements have been conducted regarding throughput and reliability in the mmWave and optical wireless communication cases, electric and magnetic field measurements, round trip latency measurements, as well as high-accuracy positioning in laboratory environment. Overall, the results so far are encouraging and indicate that 5G and beyond networks must advance further in order to meet the demands of future emerging intelligent automation systems in the next 10 years. Highly advanced industrial environments present challenges for 5G specifications, spanning congestion, interference, security and safety concerns, high power consumption, restricted propagation and poor location accuracy within the radio and core backbone communication networks for the massive IoT use cases, especially inside buildings. 6G and beyond 5G deployments for industrial networks will be increasingly denser, heterogeneous and dynamic, posing stricter performance requirements on the network. The large volume of data generated by future connected devices will put a strain on networks. It is therefore fundamental to discriminate the value of information to maximize the utility for the end users with limited network resources

6G—Enabling the New Smart City: A Survey

Smart cities and 6G are technological areas that have the potential to transform the way we live and work in the years to come. Until this transformation comes into place, there is the need, underlined by research and market studies, for a critical reassessment of the entire wireless communication sector for smart cities, which should include the IoT infrastructure, economic factors that could improve their adoption rate, and strategies that enable smart city operations. Therefore, from a technical point of view, a series of stringent issues, such as interoperability, data privacy, security, the digital divide, and implementation issues have to be addressed. Notably, to concentrate the scrutiny on smart cities and the forthcoming influence of 6G, the groundwork laid by the current 5G, with its multifaceted role and inherent limitations within the domain of smart cities, is embraced as a foundational standpoint. This examination culminates in a panoramic exposition, extending beyond the mere delineation of the 6G standard toward the unveiling of the extensive gamut of potential applications that this emergent standard promises to introduce to the smart cities arena. This paper provides an update on the SC ecosystem around the novel paradigm of 6G, aggregating a series of enabling technologies accompanied by the descriptions of their roles and specific employment schemes

Building Programmable Wireless Networks: An Architectural Survey

In recent times, there have been a lot of efforts for improving the ossified Internet architecture in a bid to sustain unstinted growth and innovation. A major reason for the perceived architectural ossification is the lack of ability to program the network as a system. This situation has resulted partly from historical decisions in the original Internet design which emphasized decentralized network operations through co-located data and control planes on each network device. The situation for wireless networks is no different resulting in a lot of complexity and a plethora of largely incompatible wireless technologies. The emergence of "programmable wireless networks", that allow greater flexibility, ease of management and configurability, is a step in the right direction to overcome the aforementioned shortcomings of the wireless networks. In this paper, we provide a broad overview of the architectures proposed in literature for building programmable wireless networks focusing primarily on three popular techniques, i.e., software defined networks, cognitive radio networks, and virtualized networks. This survey is a self-contained tutorial on these techniques and its applications. We also discuss the opportunities and challenges in building next-generation programmable wireless networks and identify open research issues and future research directions.Comment: 19 page

Accessing Wireless Sensor Networks Via Dynamically Reconfigurable Interaction Models

The Wireless Sensor Networks (WSNs) technology is already perceived as fundamental for science across many domains, since it provides a low cost solution for environment monitoring. WSNs representation via the service concept and its inclusion in Web environments, e.g. through Web services, supports particularly their open/standard access and integration. Although such Web enabled WSNs simplify data access, network parameterization and aggregation, the existing interaction models and run-time adaptation mechanisms available to clients are still scarce. Nevertheless, applications increasingly demand richer and more flexible accesses besides the traditional client/server. For instance, applications may require a streaming model in order to avoid sequential data requests, or the asynchronous notification of subscribed data through the publish/subscriber. Moreover, the possibility to automatically switch between such models at runtime allows applications to define flexible context-based data acquisition. To this extent, this paper discusses the relevance of the session and pattern abstractions on the design of a middleware prototype providing richer and dynamically reconfigurable interaction models to Web enabled WSNs

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field

Federated Learning for 6G: Applications, Challenges, and Opportunities

Traditional machine learning is centralized in the cloud (data centers). Recently, the security concern and the availability of abundant data and computation resources in wireless networks are pushing the deployment of learning algorithms towards the network edge. This has led to the emergence of a fast growing area, called federated learning (FL), which integrates two originally decoupled areas: wireless communication and machine learning. In this paper, we provide a comprehensive study on the applications of FL for sixth generation (6G) wireless networks. First, we discuss the key requirements in applying FL for wireless communications. Then, we focus on the motivating application of FL for wireless communications. We identify the main problems, challenges, and provide a comprehensive treatment of implementing FL techniques for wireless communications