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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Can I/O Variability Be Reduced on QoS-Less HPC Storage Systems?

For a production high-performance computing (HPC) system, where storage devices are shared between multiple applications and managed in a best effort manner, I/O contention is often a major problem. In this paper, we propose a balanced messaging-based re-routing in conjunction with throttling at the middleware level. This work tackles two key challenges that have not been fully resolved in the past: whether I/O variability can be reduced on a QoS-less HPC storage system, and how to design a runtime scheduling system that can scale up to a large amount of cores. The proposed scheme uses a two-level messaging system to re-route I/O requests to a less congested storage location so that write performance is improved, while limiting the impact on read by throttling re-routing. An analytical model is derived to guide the setup of optimal throttling factor. We thoroughly analyze the virtual messaging layer overhead and explore whether the in-transit buffering is effective in managing I/O variability. Contrary to the intuition, in-transit buffer cannot completely solve the problem. It can reduce the absolute variability but not the relative variability. The proposed scheme is verified against a synthetic benchmark as well as being used by production applications.

Huang, Dan↗
DOE OSTI2016

QoS aware balancing in data centers

One embodiment of the present invention provides a system for balancing loads between two physical resources. During operation, for each physical resource, the system establishes resource-usage models for a plurality of jobs that share the physical resource, identifies quality-of-service (QoS) levels associated with the jobs, and computes a QoS balance point for the physical resource based on the resource-usage models and the QoS ratings. The QoS balance point indicates a rating of QoS, above which substantial resource provisioning is required. In response to detecting a difference between a first QoS balance point associated with a first physical resource and a second QoS balance point associated with a second physical resource, the system identifies one or more jobs to be moved between the first and second physical resources such that the difference between the first and the second QoS balance points is reduced.

Greene, Daniel H.↗
DOE OSTI2024

QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing

Resource constraints on the computing continuum require that we make smart decisions for serving AI-based services at the network edge. AI-based services typically have multiple implementations (e.g., image classification implementations include SqueezeNet, DenseNet, and others) with varying trade-offs (e.g., latency and accuracy). The question then is how should AI-based services be placed across Function-as-a-Service (FaaS) based edge computing systems in order to maximize total Quality-of-Service (QoS). To address this question, we propose a problem that jointly aims to solve (i) edge AI service placement and (ii) request scheduling. These are done across two time-scales (one for placement and one for scheduling). Here we first cast the problem as an integer linear program. We then decompose the problem into separate placement and scheduling subproblems and prove that both are NP-hard. We then propose a novel placement algorithm that places services while considering device-to-device communication across edge clouds to offload requests to one another. Our results show that the proposed placement algorithm is able to outperform a state-of-the-art placement algorithm for AI-based services, and other baseline heuristics, with regard to maximizing total QoS. Additionally, we present a federated learning-based framework, FLIES, to predict the future incoming service requests and their QoS requirements. Our results also show that our FLIES algorithm is able to outperform a standard decentralized learning baseline for predicting incoming requests and show comparable predictive performance when compared to centralized training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Asynchronous Object Storage with QoS for Scientific and Commercial Big Data

This paper presents our design for an asynchronous object storage system intended for use in scientific and commercial big data workloads. Use cases from the target workload do- mains are used to motivate the key abstractions used in the application programming interface (API). The architecture of the Scalable Object Store (SOS), a prototype object stor- age system that supports the API s facilities, is presented. The SOS serves as a vehicle for future research into scalable and resilient big data object storage. We briefly review our research into providing efficient storage servers capable of providing quality of service (QoS) contracts relevant for big data use cases.

Cloud Storage↗
DOE OSTI2016

Adaptive QoS for Data Transfers using Software-Defined Networking

Software-defined networking (SDN) has demonstrated tremendous potential for altering the networking landscape in both academia andindustry. While traffic engineering as network intelligence in classic technologies have received considerable attention from the researchcommunity, traffic engineering in the SDN space, particularly in regards to quality of service (QoS), remains relatively unexplored.While recent efforts have begun to show how SDN might be leverage to meet the complex traffic-shaping needs of modern networks, there is still much that can be done to illustrate its potential, and in particular how it could prevent degradation of service for all QoStraffic. We propose methods to allow other flows to use bandwidth unused by high-priority transfers, while ensuring that bandwidthassigned to those high-priority transfers is available when needed. We present fine-tuned control over bandwidth allocation that achieves an efficient combination of both the differentiated service (DiffServ)-style classification and the integrated service (IntServ)-style perflowconsiderations. We also show how this method may be used over a wide area network (WAN) to optimally adjust bandwidth allocations when high-priority data transfers are not using their allocated bandwidth.

Boley, Josh↗
DOE OSTI2022

Systems and methods for quality of service (QoS) based management of bottlenecks and flows in networks

Techniques based on the Theory of Bottleneck Ordering can reveal the bottleneck structure of a network, and the Theory of Flow ordering can take advantage of the revealed bottleneck structure to manage and configure network flows so as to improve the overall network performance. These two techniques provide insights into the inherent topological properties of a network at least in three areas: (1) identification of the regions of influence of each bottleneck; (2) the order in which bottlenecks (and flows traversing them) may converge to their steady state transmission rates in distributed congestion control algorithms; and (3) the design of optimized traffic engineering policies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Large oscillatory thermal hall effect in kagome metals

The thermal Hall effect recently provided intriguing probes to the ground state of exotic quantum matters. These observations of transverse thermal Hall signals lead to the debate on the fermionic versus bosonic origins of these phenomena. The recent report of quantum oscillations (QOs) in Kitaev spin liquid points to a possible resolution. The Landau level quantization would most likely capture only the fermionic thermal transport effect. However, the QOs in the thermal Hall effect are generally hard to detect. In this work, we report the observation of a large oscillatory thermal Hall effect of correlated Kagome metals. We detect a 180-degree phase change of the oscillation and demonstrate the phase flip as an essential feature for QOs in the thermal transport properties. More importantly, the QOs in the thermal Hall channel are more profound than those in the electrical Hall channel, which strongly violates the Wiedemann–Franz (WF) law for QOs. This result presents the oscillatory thermal Hall effect as a powerful probe to the correlated quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

dCache: from Resilience to Quality of Service

A major goal of future dCache development will be to allow users to define file Quality of Service (QoS) in a more flexible way than currently available. This will mean implementing what might be called a QoS rule engine responsible for registering and managing time-bound QoS transitions for files or storage units. In anticipation of this extension to existing dCache capabilities, the Resilience service, which maintains on-disk replica state, needs to undergo both structural modification and generalization. This paper describes ongoing work to transform Resilience into the new architecture which will eventually support a more broadly defined file QoS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Interpretable Models for Workflow Differentiation in High-Performance Scientific Networks

Scientific workflows in high-performance networks spawn hundreds of interdependent flows that must be managed collectively—yet existing network classifiers treat each flow in isolation, leading to fragmented QoS decisions and missed interflow patterns. We present a novel traffic classification solution that operates at the workflow level, distinguishing entire filetransfer operations from streaming analytics by capturing how concurrent flows interact and burst together. We introduce a workflow identification window (WIW) that ingests raw packet headers from parallel flows into unified tensors, preserving the spatial-temporal patterns that differentiate scientific workflows. This approach achieves 98.7% accuracy using CNN, LSTM, and hybrid architectures, while maintaining 84% accuracy on production traffic collected a week later—demonstrating robustness to temporal drift. By integrating SHAP and GradCAM explainability, we reveal that early-packet timing patterns and cross-flow correlations drive classification decisions, providing operators with interpretable insights. Our system enables coherent workflow-level QoS enforcement and dynamic bandwidth allocation in scientific networks, eliminating manual per-flow configuration while maintaining classification latency at millisecond level.

Giannakou, Anna [LBL, Berkeley]↗
DOE OSTI2019

Evaluating Quality of Service Traffic Classes on the Megafly Network

An emerging trend in High Performance Computing (HPC) systems that use hierarchical topologies (such as dragonfly) is that the applications are increasingly exhibiting high run-to-run performance variability. This poses a significant challenge for application developers, job schedulers, and system maintainers. One approach to address the performance variability is to use newly proposed network topologies such as megafly (or dragonfly+) that offer increased path diversity compared to a traditional fully connected dragonfly. Yet another approach is to use quality of service (QoS) traffic classes that ensure bandwidth guarantees. In this work, we select HPC application workloads that have exhibited performance variability on current 2-D dragonfly systems. We evaluate the baseline performance expectations of these workloads on megafly and 1-D dragonfly network models with comparably similar network configurations. Our results show that the megafly network, despite using fewer virtual channels (VCs) for deadlock avoidance than a dragonfly, performs as well as a fully connected 1-D dragonfly network. We then exploit the fact that megafly networks require fewer VCs to incorporate QoS traffic classes. We use bandwidth capping and traffic differentiation techniques to introduce multiple traffic classes in megafly networks. In some cases, our results show that QoS can completely mitigate application performance variability while causing minimal slowdown to the background network traffic.

Mubarak, Misbah↗
DOE OSTI2015

Performance analysis of data intensive cloud systems based on data management and replication: a survey

As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobiledevices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service Provider must assure that data is efficiently processed and distributed in a way that does not compromise end-users’ Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. In the aforementioned perspective, data replication is used in the cloud for improving the performance (e.g., read and write delay) of applications that access data. Through replication a data intensive application or system can achieve high availability, better fault tolerance, and data recovery. In this paper, we survey data management and replication approaches (from 2007 to 2011) that are developed by both industrial and research communities. The focus of the survey is to discuss and characterize the existing approaches of data replication and management that tackle the resource usage and QoS provisioning with different levels of efficiencies. Moreover, the breakdown of both influential expressions (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported.

Malik, Saif Ur Rehman↗
DOE OSTI2018

Improving the Effectiveness of Burst Buffers for Big Data Processing in HPC Systems with Eley

Here, burst buffer is an effective solution for reducing the data transfer time and the I/O interference in HPC systems. Extending Burst Buffers (BBs) to handle Big Data applications is challenging because BBs must account for the large data inputs of Big Data applications and the Quality-of-Service (QoS) of HPC applications which are considered as first-class citizens in HPC systems. Existing BBs focus on only intermediate data of Big Data applications and incur a high performance degradation of both Big Data and HPC applications. We present Eley , a burst buffer solution that helps to accelerate the performance of Big Data applications while guaranteeing the QoS of HPC applications. To achieve this goal, Eley embraces interference-aware prefetching technique that makes reading data input faster while introducing low interference for HPC applications. Evaluations using a wide range of Big Data and HPC applications demonstrate that Eley improves the performance of Big Data applications by up to 30% compared to existing BBs while maintaining the QoS of HPC applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fermi surface and Berry phase analysis for Dirac nodal line semimetals: Cautionary tale from SrGa 2 and BaGa 2

A Berry phase of odd multiples of 𝜋 inferred from quantum oscillations (QOs) has often been treated as evidence for nontrivial reciprocal space topology. However, disentangling the Berry phase values from the Zeeman effect and the orbital magnetic moment is often challenging. In centrosymmetric compounds, the case is simpler as the orbital magnetic moment contribution is negligible. Although the Zeeman effect can be significant, it is usually overlooked in most studies of QOs in centrosymmetric compounds. Here, we present a detailed study on the nonmagnetic centrosymmetric SrGa 2 and BaGa 2 , which are predicted to be Dirac nodal line semimetals based on density functional theory (DFT) calculations. Evidence of the nontrivial topology is found in magnetotransport measurements. The Fermi surface topology and band structure are carefully studied through a combination of angle-dependent QOs, angle-resolved photoemission spectroscopy (ARPES), and DFT calculations, where the nodal line is observed in the vicinity of the Fermi level. Strong de Haas–van Alphen fundamental oscillations associated with higher harmonics are observed in both compounds, which are well fitted by the Lifshitz-Kosevich (LK) formula. However, even with the inclusion of higher harmonics in the fitting, we found that the Berry phases cannot be unambiguously determined when the Zeeman effect is included. We revisit the LK formula and analyze the phenomena and outcomes that were associated with the Zeeman effect in previous studies. Our experimental results confirm that SrGa 2 and BaGa 2 are Dirac nodal line semimetals. Additionally, we highlight the often overlooked role of spin-damping terms in Berry phase analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Crystal growth and quantum oscillations in the topological chiral semimetal CoSi

Here, we survey the electrical transport properties of single-crystalline, topological chiral semimetal CoSi grown via different methods. High-quality CoSi single crystals were found in the growth from a tellurium solution. The sample's high carrier mobility enables us to observe quantum oscillations (QOs) in its thermoelectrical signals. Our analysis of QOs reveals two spherical Fermi surfaces around the R point in its Brillouin zone corner. The extracted Berry phases of these electron orbits are consistent with the -2 chiral charge as reported in density functional theory (DFT) calculations. A detailed analysis of the QOs reveals that the spin-orbit-coupling-induced band splitting is less than 2 meV near the Fermi level, one order of magnitude smaller than our DFT calculation. We also report a large phonon-drag-induced Nernst effect in CoSi at intermediate temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗