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1. OpenStack Network Acceleration Scheme for Datacenter Intelligent Applications

   https://doi.org/10.1109/CLOUD.2018.00146  

   Phan, Linh; Liu, Kaikai (July 2018, 2018 IEEE 11th International Conference on Cloud Computing (CLOUD))

   Cloud virtualization and multi-tenant networking provide Infrastructure as a Service (IaaS) providers a new and innovative way to offer on-demand services to their customers, such as easy provisioning of new applications and better resource efficiency and scalability. However, existing data-intensive intelligent applications require more powerful processors, higher bandwidth and lower-latency networking service. In order to boost the performance of computing and networking services, as well as reduce the overhead of software virtualization, we propose a new data center network design based on OpenStack. Specifically, we map the OpenStack networking services to the hardware switch and utilize hardware-accelerated L2 switch and L3 routing to solve the software limitations, as well as achieve software-like scalability and flexibility. We design our prototype system via the Arista Software-Defined-Networking (SDN) switch and provide an automatic script which abstracts the service layer that decouples OpenStack from the physical network infrastructure, thereby providing vendor-independence. We have evaluated the performance improvement in terms of bandwidth, delay, and system resource utilization using various tools and under various Quality-of-Service (QoS) constraints. Our solution demonstrates improved cloud scaling and network efficiency via only one touch point to control all vendors' devices in the data center. 

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2. Analyzing the Features, Usability, and Performance of Deploying a Containerized Mobile Web Application on Serverless Cloud Platforms

   https://doi.org/10.3390/fi16120475  

   Yang, Jeong; Abraham, Anoop (December 2024, Future Internet)

   Serverless computing services are offered by major cloud service providers such as Google Cloud Platform, Amazon Web Services, and Microsoft Azure. The primary purpose of the services is to offer efficiency and scalability in modern software development and IT operations while reducing overall costs and operational complexity. However, prospective customers often question which serverless service will best meet their organizational and business needs. This study analyzed the features, usability, and performance of three serverless cloud computing platforms: Google Cloud’s Cloud Run, Amazon Web Service’s App Runner, and Microsoft Azure’s Container Apps. The analysis was conducted with a containerized mobile application designed to track real-time bus locations for San Antonio public buses on specific routes and provide estimated arrival times for selected bus stops. The study evaluated various system-related features, including service configuration, pricing, and memory and CPU capacity, along with performance metrics such as container latency, distance matrix API response time, and CPU utilization for each service. The results of the analysis revealed that Google’s Cloud Run demonstrated better performance and usability than AWS’s App Runner and Microsoft Azure’s Container Apps. Cloud Run exhibited lower latency and faster response time for distance matrix queries. These findings provide valuable insights for selecting an appropriate serverless cloud service for similar containerized web applications. 

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3. NSDF-Services: Integrating Networking, Storage, and Computing Services into a Testbed for Democratization of Data Delivery

   https://doi.org/10.1145/3603166.3632136  

   Luettgau, Jakob; Martinez, Heberth; Olaya, Paula; Scorzelli, Giorgio; Tarcea, Glenn; Lofstead, Jay; Kirkpatrick, Christine; Pascucci, Valerio; Taufer, Michela (December 2023, ACM)

   The lack of a readily accessible, tightly integrated data fabric connecting high-speed networking, storage, and computing services remains a critical barrier to the democratization of scientific discovery. To address this challenge, we are building National Science Data Fabric (NSDF), a holistic ecosystem to facilitate domain scientists in their daily research. NSDF comprises networking, storage, and computing services, as well as outreach initiatives. In this paper, we present a testbed integrating three services (i.e., networking, storage, and computing). We evaluate their performance. Specifically, we study the networking services and their throughput and latency with a focus on academic cloud providers; the storage services and their performance with a focus on data movement using file system mappers for both academic and commercial clouds; and computing orchestration services focusing on commercial cloud providers. We discuss NSDF's potential to increase scalability and usability as it decreases time-to-discovery across scientific domains. 

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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. 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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