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1. Anomaly Detection for Science DMZs Using System Performance Data

   https://doi.org/10.1109/ICNC47757.2020.9049695  

   Gegan, Ross; Mao, Christina; Ghosal, Dipak; Bishop, Matt; Peisert, Sean (February 2020, 2020 International Conference on Computing, Networking and Communications)

   null (Ed.)

   Science DMZs are specialized networks that enable large-scale distributed scientific research, providing efficient and guaranteed performance while transferring large amounts of data at high rates. The high-speed performance of a Science DMZ is made viable via data transfer nodes (DTNs), therefore they are a critical point of failure. DTNs are usually monitored with network intrusion detection systems (NIDS). However, NIDS do not consider system performance data, such as network I/O interrupts and context switches, which can also be useful in revealing anomalous system performance potentially arising due to external network based attacks or insider attacks. In this paper, we demonstrate how system performance metrics can be applied towards securing a DTN in a Science DMZ network. Specifically, we evaluate the effectiveness of system performance data in detecting TCP-SYN flood attacks on a DTN using DBSCAN (a density-based clustering algorithm) for anomaly detection. Our results demonstrate that system interrupts and context switches can be used to successfully detect TCP-SYN floods, suggesting that system performance data could be effective in detecting a variety of attacks not easily detected through network monitoring alone. 

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2. Poster: Minimizing Data Movement Using Distant Futures

   Sly-Delgado, Barry; Thain, Douglas (November 2023, SC23: The International Conference for High Performance Computing, Networking, Storage, and Analysis)

   Scientific workflows execute a series of tasks where each task may consume data as an input and produce data as an output. Within these workflows, tasks often produce intermediate results that may serve as inputs to subsequent tasks within the workflow. These results can vary in size and may need to be transported to another worker node. Data movement can become the primary bottleneck for many scientific workflows thus minimizing the cost of data movement can provide a significant performance benefit for a given workflow. Distant futures enable transfers between worker nodes, eliminating the need for intermediate results to pass through a centralized manager for future tasks invocations. Additionally, asynchronous transfers enable increased concurrency by preventing the blocking of task invocations. This poster shows the performance benefit received from the implementation of distant futures within a workflow that produces numerous intermediate results. 

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3. 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 results in faster, more reliable data throughput, makes cloud resources easier to provision, and the workflows easier to configure for operational use and automation. 

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4. Accelerating Scientific Workflows on HPC Platforms with In Situ Processing

   https://doi.org/10.1109/CCGrid54584.2022.00009  

   Do, Tu Mai; Pottier, Loic; Yildiz, Orcun; Vahi, Karan; Krawczuk, Patrycja; Peterka, Tom; Deelman, Ewa (May 2022, 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid))

   Scientific workflows drive most modern large-scale science breakthroughs by allowing scientists to define their computations as a set of jobs executed in a given order based on their data dependencies. Workflow management systems (WMSs) have become key to automating scientific workflows-executing computational jobs and orchestrating data transfers between those jobs running on complex high-performance computing (HPC) platforms. Traditionally, WMSs use files to communicate between jobs: a job writes out files that are read by other jobs. However, HPC machines face a growing gap between their storage and compute capabilities. To address that concern, the scientific community has adopted a new approach called in situ, which bypasses costly parallel filesystem I/O operations with faster in-memory or in-network communications. When using in situ approaches, communication and computations can be interleaved. In this work, we leverage the Decaf in situ dataflow framework to accelerate task-based scientific workflows managed by the Pegasus WMS, by replacing file communications with faster MPI messaging. We propose a new execution engine that uses Decaf to manage communications within a sub-workflow (i.e., set of jobs) to optimize inter-job communications. We consider two workflows in this study: (i) a synthetic workflow that benchmarks and compares file- and MPI-based communication; and (ii) a realistic bioinformatics workflow that computes mu-tational overlaps in the human genome. Experiments show that in situ communication can improve the bioinformatics workflow execution time by 22% to 30% compared with file communication. Our results motivate further opportunities and challenges for bridging traditional WMSs with in situ frameworks. 

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5. Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model

   https://doi.org/10.1088/2632-2153/ace756  

   Templin, Thomas; Memarzadeh, Milad; Vinci, Walter; Lott, P. Aaron; Akbari Asanjan, Ata; Alexiades Armenakas, Anthony; Rieffel, Eleanor (August 2023, Machine Learning: Science and Technology)

   Abstract Deep generative learning cannot only be used for generating new data with statistical characteristics derived from input data but also for anomaly detection, by separating nominal and anomalous instances based on their reconstruction quality. In this paper, we explore the performance of three unsupervised deep generative models—variational autoencoders (VAEs) with Gaussian, Bernoulli, and Boltzmann priors—in detecting anomalies in multivariate time series of commercial-flight operations. We created two VAE models with discrete latent variables (DVAEs), one with a factorized Bernoulli prior and one with a restricted Boltzmann machine (RBM) with novel positive-phase architecture as prior, because of the demand for discrete-variable models in machine-learning applications and because the integration of quantum devices based on two-level quantum systems requires such models. To the best of our knowledge, our work is the first that applies DVAE models to anomaly-detection tasks in the aerospace field. The DVAE with RBM prior, using a relatively simple—and classically or quantum-mechanically enhanceable—sampling technique for the evolution of the RBM’s negative phase, performed better in detecting anomalies than the Bernoulli DVAE and on par with the Gaussian model, which has a continuous latent space. The transfer of a model to an unseen dataset with the same anomaly but without re-tuning of hyperparameters or re-training noticeably impaired anomaly-detection performance, but performance could be improved by post-training on the new dataset. The RBM model was robust to change of anomaly type and phase of flight during which the anomaly occurred. Our studies demonstrate the competitiveness of a discrete deep generative model with its Gaussian counterpart on anomaly-detection problems. Moreover, the DVAE model with RBM prior can be easily integrated with quantum sampling by outsourcing its generative process to measurements of quantum states obtained from a quantum annealer or gate-model device. 

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