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1. Edge computing framework for distributed smart applications

   https://doi.org/10.1109/UIC-ATC.2017.8397448  

   Liu, Kaikai; Gurudutt, Abhishek; Kamaal, Tejeshwar; Divakara, Chinmayi; Prabhakaran, Praveen (August 2017, The IEEE Smart World Congress 2017 (IEEE SWC 2017))

   The rapid growth in technology and wide use of internet has increased smart applications such as intelligent transportation control system, and Internet of Things, which heavily rely on an efficient and reliable connectivity network. To overcome high bandwidth work load on the network, as well as minimize latency for real-time applications, the computation can be moved from the central cloud to a distributed edge cloud. The edge computing benefits various smart applications that uses distributed network for data analytics and services. Different from the existing cloud management solutions, edge computing needs to move cloud management services towards distributed heterogeneous edge nodes for multi-tenant user applications. However, existing cloud management services do not offer remote deployment of multi-tenant user applications on the cloud of edge nodes. In this paper, we propose a practical edge cloud software framework for deploying multi-tenant distributed smart applications. Having multiple distributed end nodes, auto discovery of all active end nodes is required for deploying multi-tenant user applications. However, existing cloud solutions require either private network or fixed IP address, which is not achievable for the distributed edge nodes. Most of the edge nodes connected through the public internet without fixed IP, and some of them even connect through IEEE 802.15 based sensor networks. We propose to build a software platform to manage the distributed edge nodes as well as support services to deploy and launch isolated, multi-tenant user applications through a lightweight container. We propose an architectural solution to remotely access edge cloud management services through intermittent internet connections. We open sourced our whole set of software solutions, and analyzed the major performance metrics of the edge cloud platform. 

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2. Software-Defined Edge Cloud Framework for Resilient Multitenant Applications

   https://doi.org/10.1155/2019/3947286  

   Liu, Kaikai; Manangi Ravindrarao, Nagthej; Gurudutt, Abhishek; Kamaal, Tejeshwar; Divakara, Chinmayi; Prabhakaran, Praveen (January 2019, Wireless Communications and Mobile Computing)

   Edge application’s distributed nature presents significant challenges for developers in orchestrating and managing the multitenant applications. In this paper, we propose a practical edge cloud software framework for deploying multitenant distributed smart applications. Here we exploit commodity, a low cost embedded board to form distributed edge clusters. The cluster of geo-distributed and wireless edge nodes not only power multitenant IoT applications that are closer to the data source and the user, but also enable developers to remotely deploy and orchestrate application containers over the cloud. Specifically, we propose building a software platform to manage the distributed edge nodes along with support services to deploy and launch isolated and multitenant user applications through a lightweight container. In particular, we propose an architectural solution to improve the resilience of edge cloud services through peer collaborated service migration when the failures happen or when resources are overburdened. We focus on giving the developers a single point control of the infrastructure over the intermittent and lossy wide area networks (WANs) and enabling the remote deployment of multitenant applications. 

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3. Building a Student-to-Workforce Pipeline for 21st Century Cloud Industry Careers

   https://doi.org/10.18260/1-2--36764  

   Billionniere, Elodie; Meyer, Lawrence (July 2021, 2021 ASEE Virtual Annual Conference Proceedings)

   Cloud migration has accelerated and companies around the world are investing their technological future with the cloud. Given the growing industry demand for cloud related skills, Miami Dade College partnered with the industry leader in cloud computing solutions Amazon Web Services (AWS). In conjunction with AWS, we developed a new cloud-based learning curriculum designed to provide an academic gateway for the next generation of computing technicians to meet local and national workforce demands. The recruitment population focused on predominantly minoritized and low-income populations. This certificate-based curriculum is designed as stackable for both the successful completion of a College Credit Certificate and/or an Associate of Science in Networking Technology with a concentration in Enterprise Cloud Computing. The curriculum was developed in alignment with industry needs and utilizes high impact educational practices. The cloud computing programs prepare students to potentially earn an academic credential and globally recognized industry certifications at the entry (college credit certificate) and associate levels. Ensuring students are offered the highest standard of training, faculty members completed professional development in cloud computing, earning industry-recognized credentials to become AWS-accredited instructors. The current cohort of faculty certified to teach in the AWS Academy is the largest group of certified instructors in the academic arena. The inclusion of industry standard certifications from major cloud providers allows for consistency of program evaluation and instructors assessment of student work and course comprehension. The net effect of these certifications is not only earned degrees, but employer ability to validate prospective employee skill and knowledge outside of an academic environment. This paper presents the approach followed in developing in-depth, project-based learning opportunities using cutting-edge technology for the new academic pathway in cloud literacy and the program outcomes. A discussion on the best practices and lessons learned while implementing the first year of the program is included. 

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4. Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge

   https://doi.org/10.1109/PERCOMW.2019.8730577  

   Pettet, Geoffrey; Sahoo, Saroj; Dubey, Abhishek (March 2019, 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops))

   The Internet of Things (IoT) requires distributed, large scale data collection via geographically distributed devices. While IoT devices typically send data to the cloud for processing, this is problematic for bandwidth constrained applications. Fog and edge computing (processing data near where it is gathered, and sending only results to the cloud) has become more popular, as it lowers network overhead and latency. Edge computing often uses devices with low computational capacity, therefore service frameworks and middleware are needed to efficiently compose services. While many frameworks use a top-down perspective, quality of service is an emergent property of the entire system and often requires a bottom up approach. We define services as multi-modal, allowing resource and performance tradeoffs. Different modes can be composed to meet an application's high level goal, which is modeled as a function. We examine a case study for counting vehicle traffic through intersections in Nashville. We apply object detection and tracking to video of the intersection, which must be performed at the edge due to privacy and bandwidth constraints. We explore the hardware and software architectures, and identify the various modes. This paper lays the foundation to formulate the online optimization problem presented by the system which makes tradeoffs between the quantity of services and their quality constrained by available resources. 

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5. Elastically Augmenting the Control-path Throughput in SDN to Deal with Internet DDoS Attacks

   https://doi.org/10.1145/3559759  

   Dai, Yuanjun; Wang, An; Guo, Yang; Chen, Songqing (February 2023, ACM Transactions on Internet Technology)

   Distributed denial of service (DDoS) attacks have been prevalent on the Internet for decades. Albeit various defenses, they keep growing in size, frequency, and duration. The new network paradigm, Software-defined networking (SDN), is also vulnerable to DDoS attacks. SDN uses logically centralized control, bringing the advantages in maintaining a global network view and simplifying programmability. When attacks happen, the control path between the switches and their associated controllers may become congested due to their limited capacity. However, the data plane visibility of SDN provides new opportunities to defend against DDoS attacks in the cloud computing environment. To this end, we conduct measurements to evaluate the throughput of the software control agents on some of the hardware switches when they are under attacks. Then, we design a new mechanism, calledScotch, to enable the network to scale up its capability and handle the DDoS attack traffic. In our design, the congestion works as an indicator to trigger the mitigation mechanism.Scotchelastically scales up the control plane capacity by using an Open vSwitch-based overlay.Scotchtakes advantage of both the high control plane capacity of a large number of vSwitches and the high data plane capacity of commodity physical switches to increase the SDN network scalability and resiliency under abnormal (e.g., DDoS attacks) traffic surges. We have implemented a prototype and experimentally evaluatedScotch. Our experiments in the small-scale lab environment and large-scale GENI testbed demonstrate thatScotchcan elastically scale up the control channel bandwidth upon attacks. 

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