Summary There is a growing need for next‐generation science gateways to increase the accessibility of emerging large‐scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID‐19‐related challenges. Such science gateways that enable access to distributed computing resources for large‐scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio‐technical approach for developing a next‐generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID‐19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence‐based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug‐in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot‐assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks.

Cloud-hosted applications are prone to targeted attacks such as DDoS, advanced persistent threats, Cryptojacking which threaten service availability. Recently, methods for threat information sharing and defense require cooperation and trust between multiple domains/entities. There is a need for mechanisms that establish distributed trust to allow for such a collective defense. In this paper, we present a novel threat intelligence sharing and defense system, namely “DefenseChain,” to allow organizations to have incentive-based and trustworthy cooperation to mitigate the impact of cyber attacks. Our solution approach features a consortium Blockchain platform and an economic model to obtain threat data and select suitable peers to help with attack detection and mitigation. We apply DefenseChain in the financial technology industry for an insurance claim processing use case to demonstrate the effectiveness of DefenseChain in a real-world application setting. Our evaluation experiments with DefenseChain implementation are performed on an Open Cloud testbed with Hyperledger Composer and in a simulation environment. Our results show that the DefenseChain system overall performs better than state-of-the-art decision making schemes in choosing the most appropriate detector and mitigator peers. Lastly, we validate how DefenseChain helps mitigate the threat risk of incidents relating to potential fraudulent insurance claims or cyber attacks.

Data-intensive application workflows benefit by leveraging cloud services to decrease execution times and increase data sharing. Cloud service providers (CSPs) have distinct capabilities and policies, and performance/cost of the cloud services are amongst the prime factors for CSP selection. However, workflow users who need brokering of cloud resources often lack expert guidance to handle the problem of overwhelming choice in CSP selection, and optimization to compensate for service dynamics. In this paper, we address the optimal resource selection problem using a multi-cloud resource broker viz., OnTimeURB that uses knowledge-engineering of user requirements and service capabilities across multiple CSPs. OnTimeURB is powered by integer linear programming and a Naive Bayes classifier to recommend optimal cloud template solutions by weighting performance, agility, cost, and security (PACS) factors. We evaluate the OnTimeURB recommendations with a catalog of bioinformatics application workflows using four CSP resources featuring more than 300 different instance configurations. Our evaluation results show the efficacy of OnTimeURB in creating consistently cost-effective and agile solutions compared to a state-of-the-art k-nearest neighbors (k-NN) approach. We also show that OnTimeURB has 91% success rate improvement in workflow execution times via cloud template recommendations over approaches that do not use knowledge-engineered multi-CSP resource brokering.

3D city-scale models are useful in a number of applications, including education, city planning, navigation systems, artificial intelligence training, and simulations. However, final models need to be immersive and interactive, which requires a mixed reality (XR) environment design that combines e.g., a Cave Automatic Virtual Environment (CAVE) VR system with the Microsoft Hololens2 in a networked and multimodal setting. In this paper, we propose a pipeline to convert a city-scale point cloud into a finalized city-scale textured mesh in which, a number of XR devices can share the same environment and co-exist in a shared space for model interactions. Specifically, we use input point clouds obtained from wide area motion imagery systems or off-the-shelf drones pertaining to Albuquerque, New Mexico, but the pipeline is generalized so that other input can be used. Using four different traditional algorithms and an additional deep learning method, we create meshes for the model interactions. For each mesh produced, we map high-resolution textures onto them, producing a more accurate city, which is then passed into the shared/networked Unity environment. Ten participants provided their assessment of mesh quality and interactivity of the networked environment during exploration of different city reconstructions with the CAVE and laptop device modalities. Results on the perceptual immersive quality of the Point2Mesh deep learning meshes highlights the need for improvements to handle large city scale point clouds.

Current scientific experiments frequently involve control of specialized instruments (e.g., scanning electron microscopes), image data collection from those instruments, and transfer of the data for processing at simulation centers. This process requires a “human-in-the-loop” to perform those tasks manually, which besides requiring a lot of effort and time, could lead to inconsistencies or errors. Thus, it is essential to have an automated system capable of performing remote instrumentation to intelligently control and collect data from the scientific instruments. In this paper, we propose a Remote Instrumentation Science Environment (RISE) for intelligent image analytics that provides the infrastructure to securely capture images, determine process parameters via machine learning, and provide experimental control actions via automation, under the premise of “human-on-the-loop”. The machine learning in RISE aids an iterative discovery process to assist researchers to tune instrument settings to improve the outcomes of experiments. Driven by two scientific use cases of image analytics pipelines, one in material science, and another in biomedical science, we show how RISE automation leverages a cutting-edge integration of cloud computing, on-premise HPC cluster, and a Python programming interface available on a microscope. Using web services, we implement RISE to perform automated image data collection/analysis guided by an intelligent agent to provide real-time feedback control of the microscope using the image analytics outputs. Our evaluation results show the benefits of RISE for researchers to obtain higher image analytics accuracy, save precious time in manually controlling the microscopes, while reducing errors in operating the instruments.

Active defense schemes are becoming critical to secure cloud-based applications in the fields such as healthcare, entertainment, and manufacturing. Active defense mechanisms in cloud platforms need to be robust against targeted attacks (such as Distributed Denial-of-Service (DDoS), malware, and SQL injection) that make servers unresponsive and/or cause data breaches/loss, which in turn can cause high impact especially for healthcare applications. In this paper, we present a novel network-based active defense mechanism viz., “defense by pretense” that uses real-time attack detection and creates cyber deception e.g., by redirecting attacker’s traffic to quarantine machines and sending spoofed responses to attacker for cloud-based healthcare data processing applications. We implement our active defense mechanism by creating a realistic testbed on AWS cloud platform featuring the Observational Health Data Sciences and Informatics (OHDSI) framework for protected health data analytics with electronic health record data (SynPUF) and COVID-19 publications (CORD-19). Our evaluation experiments show the need and effectiveness of our active defense mechanism against targeted resource and data exfiltration attacks. We compare our active defense system against state-of-the-art active defense works, and our results show that our system is cost-effective, scalable and easy to deploy for active defense.

Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity, we present a novel "OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons).

Cloud computing has become a necessary utility for scientific and technical applications. Many diverse web services are published and subscribed using cloud data centers. It has become fairly easy to use services from Cloud Service Providers (CSPs) for computation and data processing. However, even with all their benefits, commercial cloud resources are not economical when large data processing is required. Hence, educators and researchers need guidance to use commercial cloud resources to run large data processing workflow applications within a budget. In this paper, we propose a framework to help users to leverage distributed compute resources to execute data-intensive application workflows, under budget constraints. We demonstrate how our framework can be used by users who may have access to small-scale compute resources in-house, to seamlessly interoperate with public cloud resources.

Unmanned aerial vehicles (UAV) or drone systems equipped with cameras are extensively used in different surveillance scenarios and often require real-time control and high-quality video transmission. However, unstable network situations and various transport protocols may result in impairments during video streaming, which in turn negatively impacts user's quality of experience (QoE). In this paper, we propose a dynamic computation offloading and control framework, named DyCOCo, based on image impairment detection under various available network bandwith conditions. Our DyCOCo framework demo features IoT devices in a testbed setup on the GENI infrastructure. Our demo results show that our DyCOCo approach can efficiently choose the suitable networking protocols and orchestrate both the camera control on the drone, and the computation offloading of the video analytics over limited edge computing/networking resources.

Computer vision applications are increasingly used on mobile Internet-of-Things (IoT) devices such as drones. They provide real-time support in disaster/incident response or crowd protest management scenarios by e.g., counting human/vehicles, or recognizing faces/objects. However, deployment of such applications for real-time video analytics at geo-distributed areas presents new challenges in processing intensive media-rich data to meet users' Quality of Experience (QoE) expectations, due to limited computing power on the devices. In this paper, we present a novel policy-based decision computation offloading scheme that not only facilitates trade-offs in performance vs. cost, but also aids in offloading decision to either an Edge, Cloud or Function-Centric Computing resource architecture for real-time video analytics. To evaluate our offloading scheme, we decompose an existing computer vision pipeline for object/motion detection and object classification into a chain of container-based micro-service functions that communicate via a RESTful API. We evaluate the performance of our scheme on a realistic geo-distributed edge/core cloud testbed using different policies and computing architectures. Results show how our scheme utilizes state-of-the-art computation offloading techniques to Pareto-optimally trade-off performance (i.e., frames-per-second) vs. cost factors (using Amazon Web Services Lambda pricing) during real-time drone video analytics, and thus fosters effective environmental situational awareness.