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1. Toward a Dynamic Network-Centric Distributed Cloud Platform for Scientific Workflows: A Case Study for Adaptive Weather Sensing

   https://doi.org/10.1109/eScience.2019.00015  

   Lyons, Eric ; Papadimitriou, George ; Wang, Cong ; Thareja, Komal ; Ruth, Paul ; Villalobos, J. J. ; Rodero, Ivan ; Deelman, Ewa ; Zink, Michael ; Mandal, Anirban ( September 2019 , 2019 15th International Conference on eScience (eScience))

   Computational science today depends on complex, data-intensive applications operating on datasets from a variety of scientific instruments. A major challenge is the integration of data into the scientist's workflow. Recent advances in dynamic, networked cloud resources provide the building blocks to construct reconfigurable, end-to-end infrastructure that can increase scientific productivity. However, applications have not adequately taken advantage of these advanced capabilities. In this work, we have developed a novel network-centric platform that enables high-performance, adaptive data flows and coordinated access to distributed cloud resources and data repositories for atmospheric scientists. We demonstrate the effectiveness of our approach by evaluating time-critical, adaptive weather sensing workflows, which utilize advanced networked infrastructure to ingest live weather data from radars and compute data products used for timely response to weather events. The workflows are orchestrated by the Pegasus workflow management system and were chosen because of their diverse resource requirements. We show that our approach results in timely processing of Nowcast workflows under different infrastructure configurations and network conditions. We also show how workflow task clustering choices affect throughput of an ensemble of Nowcast workflows with improved turnaround times. Additionally, we find that using our network-centric platform powered by advanced layer2 networking techniques resultsmore »in faster, more reliable data throughput, makes cloud resources easier to provision, and the workflows easier to configure for operational use and automation.« less
2. FogROS: An Adaptive Framework for Automating Fog Robotics Deployment

   https://doi.org/10.1109/case49439.2021.9551628  

   Chen, Kaiyuan Eric ; Liang, Yafei ; Jha, Nikhil ; Ichnowski, Jeffrey ; Danielczuk, Michael ; Gonzalez, Joseph ; Kubiatowicz, John ; Goldberg, Ken ( August 2021 , 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE))

   As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource for systems that do not contain high computing power onboard. Despite its immense computing capacity, it is often underused by the robotics and automation community due to lack of expertise in cloud computing and cloud-based infrastructure. Fog Robotics balances computing and data between cloud edge devices. We propose a software framework, FogROS, as an extension of the Robot Operating System (ROS), the de-facto standard for creating robot automation applications and components. It allows researchers to deploy components of their software to the cloud with minimal effort, and correspondingly gain access to additional computing cores, GPUs, FPGAs, and TPUs, as well as predeployed software made available by other researchers. FogROS allows a researcher to specify which components of their software will be deployed to the cloud and to what type of computing hardware. We evaluate FogROS on 3 examples: (1) simultaneous localization and mapping (ORB-SLAM2), (2) Dexterity Network (Dex-Net) GPU-based grasp planning, and (3) multi-core motion planning using a 96-core cloud-based server. In all three examples, a component is deployed to the cloud and accelerated with a smallmore »change in system launch configuration, while incurring additional latency of 1.2 s, 0.6 s, and 0.5 s due to network communication, the computation speed is improved by 2.6x, 6.0x and 34.2x, respectively.« less
3. Towards security-as-a-service in multi-access edge

   https://doi.org/10.1145/3318216.3363335  

   Tourani, Reza ; Bos, Austin ; Misra, Satyajayant ; Esposito, Flavio ( January 2019 , SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing)

   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.
4. Hardening the Security of Multi-Access Edge Computing through Bio-Inspired VM Introspection

   https://doi.org/10.3390/bdcc5040052  

   Huseynov H., Saadawi T. ( October 2021 , Big data and cognitive computing)

   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 tomore »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.« less
5. Preliminary Study of Trusted Execution Environments on Heterogeneous Edge Platforms

   https://doi.org/10.1109/SEC.2018.00057  

   Ning, Zhenyu ; Liao, Jinghui ; Zhang, Fengwei ; Shi, Weisong ( October 2018 , In Proceedings of The 1st ACM/IEEE Workshop on Security and Privacy in Edge Computing (EdgeSP'18), in conjunction with The 3rd ACM/IEEE Symposium on Edge Computing (SEC'18))

   The recent edge computing infrastructure introduces a new computing model that works as a complement of the traditional cloud computing. The edge nodes in the infrastructure reduce the network latency of the cloud computing model and increase data privacy by offloading the sensitive computation from the cloud to the edge. Recent research focuses on the applications and performance of the edge computing, but less attention is paid to the security of this new computing paradigm. Inspired by the recent move of hardware vendors that introducing hardware-assisted Trusted Execution Environment (TEE), we believe applying these TEEs on the edge nodes would be a natural choice to secure the computation and sensitive data on these nodes. In this paper, we investigate the typical hardware-assisted TEEs and evaluate the performance of these TEEs to help analyze the feasibility of deploying them on the edge platforms. Our experiments show that the performance overhead introduced by the TEEs is low, which indicates that integrating these TEEs into the edge nodes can efficiently mitigate security loopholes with a low performance overhead.