Optical fiber deployments in metropolitan areas are critical for information distribution to businesses and large segments of the population. In this paper, we describe a char- acterization study of metropolitan area fiber networks in the US. The goal of our work is to elucidate the key aspects of these infrastructures and to assess how they can be enhanced to support growth in cloud-mobile via expanded connectivity to data centers. We collect maps of 204 metro fiber networks and transcribe these into a geographic information system for analysis and visualization. We report on characteristics including raw miles, geography, proximity to users, correspondence to other infrastructure and PoP/data center proximity. These characteris- tics indicate highly diverse deployments in different metro areas and suggest different strategies for future deployments. Next, we conduct a resource allocation analysis to assess how fiber infrastructure can be deployed in metro areas to reduce the physical distance to data centers over a range of cost scenarios. Our results show that a small number of new connections to data centers can significantly reduce physical distances to users.
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Deployment Characteristics of "The Edge" in Mobile Edge Computing
The envisioned capabilities of mobile edge computing are predicated on a delivery infrastructure with capacity, ubiquity, robustness, and capabilities to serve a country-wide user base. In this paper, we present an empirical study of key aspects of mobile edge
infrastructure toward the goal of understanding their current characteristics and identifying future deployments. We start by analyzing a dataset of over 4M cell tower locations in the US. We evaluate the geographic characteristics of deployments and highlight how
locations correspond to population density in major metropolitan
areas and in rural areas. We also show how deployments have been
arranged along highways throughout the US. Our analysis highlight areas where new deployments would be warranted. Finally, we analyze how cell tower deployments correspond to current major data center locations and assess how micro servers might be
deployed to improve response times and to better serve customers.
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- Award ID(s):
- 1703592
- NSF-PAR ID:
- 10096147
- Date Published:
- Journal Name:
- ACM SIGCOMM Workshop on Mobile Edge Communications
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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With each passing year, the state-of-the-art deep learning neural networks grow larger in size, requiring larger computing and power resources. The high compute resources required by these large networks are alienating the majority of the world population that lives in low-resource settings and lacks the infrastructure to benefit from these advancements in medical AI. Current state-of-the-art medical AI, even with cloud resources, is a bit difficult to deploy in remote areas where we don’t have good internet connectivity. We demonstrate a cost-effective approach to deploying medical AI that could be used in limited resource settings using Edge Tensor Processing Unit (TPU). We trained and optimized a classification model on the Chest X-ray 14 dataset and a segmentation model on the Nerve ultrasound dataset using INT8 Quantization Aware Training. Thereafter, we compiled the optimized models for Edge TPU execution. We find that the inference performance on edge TPUs is 10x faster compared to other embedded devices. The optimized model is 3x and 12x smaller for the classification and segmentation respectively, compared to the full precision model. In summary, we show the potential of Edge TPUs for two medical AI tasks with faster inference times, which could potentially be used in low-resource settings for medical AI-based diagnostics. We finally discuss some potential challenges and limitations of our approach for real-world deployments.more » « less
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null (Ed.)The extreme bandwidth and performance of 5G mobile networks changes the way we develop and utilize digital services. Within a few years, 5G will not only touch technology and applications, but dramatically change the economy, our society and individual life. One of the emerging technologies that enables the evolution to 5G by bringing cloud capabilities near to the end users is Edge Computing or also known as Multi-Access Edge Computing (MEC) that will become pertinent towards the evolution of 5G. This evolution also entails growth in the threat landscape and increase privacy in concerns at different application areas, hence security and privacy plays a central role in the evolution towards 5G. Since MEC application instantiated in the virtualized infrastructure, in this paper we present a distributed application that aims to constantly introspect multiple virtual machines (VMs) in order to detect malicious activities based on their anomalous behavior. Once suspicious processes detected, our IDS in real-time notifies system administrator about the potential threat. Developed software is able to detect keyloggers, rootkits, trojans, process hiding and other intrusion artifacts via agent-less operation, by operating remotely or directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Experimental results of remote VMI on more than 50 different malicious code demonstrate average anomaly detection rate close to 97%. We have established wide testbed environment connecting networks of two universities Kyushu Institute of Technology and The City College of New York through secure GRE tunnel. Conducted experiments on this testbed deliver high response time of the proposed system.more » « less
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The prevailing network security measures are often implemented on proprietary appliances that are deployed at fixed network locations with constant capacity. Such a rigid deployment is sometimes necessary, but undermines the flexibility of security services in meeting the demands of emerging applications, such as augmented/virtual reality, autonomous driving, and 5G for industry 4.0, which are provoked by the evolution of connected and smart devices, their heterogeneity, and integration with cloud and edge computing infrastructures. To loosen these rigid security deployments, in this paper, we propose a data-centric SECurity-as-a-Service (SECaaS) framework for elastic deployment and provisioning of security services at the Multi-Access Edge Computing (MEC) infrastructure. In particular, we discuss three security services that are suitable for edge deployment: (i) an intrusion detection and prevention system (IDPS), (ii) an access control enforcement system (ACE), and (iii) a communication anonymization service (CA). We benchmark the common security microservices along with the design and implementation of a proof of concept communication anonymization application.more » « less
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Objective: Although extreme heat can impact the health of anyone, certain groups are disproportionately affected. In urban settings, cooling centers are intended to reduce heat exposure by providing air-conditioned spaces to the public. We examined the characteristics of populations living near cooling centers and how well they serve areas with high social vulnerability.
Methods: We identified 1402 cooling centers in 81 US cities from publicly available sources and analyzed markers of urban heat and social vulnerability in relation to their locations. Within each city, we developed cooling center access areas, defined as the geographic area within a 0.5-mile walk from a center, and compared sociodemographic characteristics of populations living within versus outside the access areas. We analyzed results by city and geographic region to evaluate climate-relevant regional differences.
Results: Access to cooling centers differed among cities, ranging from 0.01% (Atlanta, Georgia) to 63.2% (Washington, DC) of the population living within an access area. On average, cooling centers were in areas that had higher levels of social vulnerability, as measured by the number of people living in urban heat islands, annual household income below poverty, racial and ethnic minority status, low educational attainment, and high unemployment rate. However, access areas were less inclusive of adult populations aged ≥65 years than among populations aged <65 years.
Conclusion: Given the large percentage of individuals without access to cooling centers and the anticipated increase in frequency and severity of extreme heat events, the current distribution of centers in the urban areas that we examined may be insufficient to protect individuals from the adverse health effects of extreme heat, particularly in the absence of additional measures to reduce risk.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3229556.3229557
