Publications

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Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia's Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia's ElectroMagnetic Plasma In Realistic Environments ( EMPIRE ) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia's HPC users.

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Network congestion in high-speed interconnects is a major source of application runtime performance variation. Recent years have witnessed a surge of interest from both academia and industry in the development of novel approaches for congestion control at the network level and in application placement, mapping, and scheduling at the system-level. However, these studies are based on proxy applications and benchmarks that are not representative of field-congestion characteristics of high-speed interconnects. To address this gap, we present (a) an end-to-end framework for monitoring and analysis to support long-term field-congestion characterization studies, and (b) an empirical study of network congestion in petascale systems across two different interconnect technologies: (i) Cray Gemini, which uses a 3-D torus topology, and (ii) Cray Aries, which uses the DragonFly topology.

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Monitoring of High Performance Computing (HPC) platforms is critical to successful operations, can provide insights into performance-impacting conditions, and can inform methodologies for improving science throughput. However, monitoring systems are not generally considered core capabilities in system requirements specifications nor in vendor development strategies. In this paper we present work performed at a number of large-scale HPC sites towards developing monitoring capabilities that fill current gaps in ease of problem identification and root cause discovery. We also present our collective views, based on the experiences presented, on needs and requirements for enabling development by vendors or users of effective sharable end-to-end monitoring capabilities.

We present techniques for characterizing bandwidth and congestion characteristics of supercomputer High-Speed Networks (HSN). By utilizing a link-level perspective, we gain generality over analyses which are tied to specific topologies. We illustrate these techniques using five months of a Blue Waters production dataset consisting of network utilization and congestion counters. We find that: i) execution time of the communicationheavy applications is highly correlated to network stalls observed in the network topology and increase in application runtime can be as high as 1.7x with nominal increase in stalls, ii) heterogeneity in the available link bandwidth in the network can lead to backpressure and congestion even when the network is not underprovisioned, and (iii) links connected to I/O nodes are no more likely to observe congestion during operational hours than any other link in the system.

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The growth of High Performance Computer (HPC) systems increases the complexity with respect to understanding resource utilization, system management, and performance issues. While raw performance data is increasingly exposed at the component level, the usefulness of the data is dependent on the ability to do meaningful analysis on actionable timescales. However, current system monitoring infrastructures largely focus on data collection, with analysis performed off-system in post-processing mode. This increases the time required to provide analysis and feedback to a variety of consumers. In this work, we enhance the architecture of a monitoring system used on large-scale computational platforms, to integrate streaming analysis capabilities at arbitrary locations within its data collection, transport, and aggregation facilities. We leverage the flexible communication topology of the monitoring system to enable placement of transformations based on overhead concerns, while still enabling low-latency exposure on node. Our design internally supports and exposes the raw and transformed data uniformly for both node level and off-system consumers. We show the viability of our implementation for a case with production-relevance: run-time determination of the relative per-node files system demands.

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In high-performance computing systems, application performance and throughput are dependent on a complex interplay of hardware and software subsystems and variable workloads with competing resource demands. Data-driven insights into the potentially widespread scope and propagationof impact of events, such as faults and contention for shared resources, can be used to drive more effective use of resources, for improved root cause diagnosis, and for predicting performance impacts. We present work developing integrated capabilities for holistic monitoring and analysis to understand and characterize propagation of performance-degrading events. These characterizations can be used to determine and invoke mitigating responses by system administrators, applications, and system software.

The presentation documented the technical approach of the team and summary of the results with sufficient detail to demonstrate both the value and the completion of the milestone. A separate SAND report was also generated with more detail to supplement the presentation.

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A detailed understanding of HPC applications’ resource needs and their complex interactions with each other and HPC platform resources are critical to achieving scalability and performance. Such understanding has been difficult to achieve because typical application profiling tools do not capture the behaviors of codes under the potentially wide spectrum of actual production conditions and because typical monitoring tools do not capture system resource usage information with high enough fidelity to gain sufficient insight into application performance and demands. In this paper we present both system and application profiling results based on data obtained through synchronized system wide monitoring on a production HPC cluster at Sandia National Laboratories (SNL). We demonstrate analytic and visualization techniques that we are using to characterize application and system resource usage under production conditions for better understanding of application resource needs. Our goals are to improve application performance (through understanding application-to-resource mapping and system throughput) and to ensure that future system capabilities match their intended workloads.

Sandia has invested heavily in scientifc/engineering application development and in the research, development, and deployment of large scale HPC platforms to support the com- putational needs of these applications. As application developers continually expand the capabilities of their software and spend more time on performance tuning of applications for these platforms, HPC platform resources are at a premium as they are a heavily shared resource serving the varied needs of many users. To ensure that the HPC platform resources are being used efciently and perform as designed, it is necessary to obtain reliable data on resource utilization that will allow us to investigate the occurrence, severity, and causes of performance-afecting contention between applications. The work presented in this paper was an initial step to determine if resource contention can be understood and minimized through monitoring, modeling, planning and infrastructure. This paper describes the set of metric defnitions, identifed in this research, that can be used as meaningful and poten- tially actionable indicators of performance-afecting contention between applications. These metrics were verifed using the observed slowdown of IOR, IMB, and CTH in operating scenarios that forced contention. This paper also describes system/application monitoring activities that are critical to distilling vast amounts of data into quantities that hold the key to understanding for an application's performance under production conditions and that will ultimately aid in Sandia's eforts to succeed in extreme-scale computing.

Issues of High Performance Computer (HPC) system diagnosis, automated system management, and resource-aware computing, are all dependent on high fidelity, system wide, persistent monitoring. Development and deployment of an effective persistent system wide monitoring service at large-scale presents a number of challenges, particularly when collecting data at the granularities needed to resolve features of interest and obtain early indication of significant events on the system. In this paper we provide experiences from our developments on and two-year deployment of our Lightweight Distributed Metric Service (LDMS) monitoring system on NCSA's 27,648 node Blue Waters system. We present monitoring related challenges and issues and their effects on the major functional components of general monitoring infrastructures and deployments: Data Sampling, Data Aggregation, Data Storage, Analysis Support, Operations, and Data Stewardship. Based on these experiences, we providerecommendations for effective development and deployment of HPC monitoring systems.

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Shared networks create unique challenges in obtaining con-sistent performance across jobs for large systems when not using exclusive system-wide allocations. In order to provide good system utilization, resource managers allocate system space to multiple jobs. These multiple independent node al-locations can interfere with each other through their shared network. This work provides a method of observing and measuring the impact of network contention due to interfer-ence from other jobs through a continually running bench-mark application and the use of network performance coun-Ters. This is the first work to measure network interfer-ence using specially designed benchmarks and network per-formance counters.

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A detailed understanding of HPC application's resource needs and their complex interactions with each other and HPC platform resources is critical to achieving scalability and performance. Such understanding has been difficult to achieve because typical application profiling tools do not capture the behaviors of codes under the potentially wide spectrum of actual production conditions and because typical monitoring tools do not capture system resource usage information with high enough fidelity to gain sufficient insight into application performance and demands. In this paper we present both system and application profiling results based on data obtained through synchronized system wide monitoring on a production HPC cluster at Sandia National Laboratories (SNL). We demonstrate analytic and visualization techniques that we are using to characterize application and system resource usage under production conditions for better understanding of application resource needs. Our goals are to improve application performance (through understanding application-to-resource mapping and system throughput) and to ensure that future system capabilities match their intended workloads.

Disentangling significant and important log messages from those that are routine and unimportant can be a difficult task. Further, on a new system, understanding correlations between significant and possibly new types of messages and conditions that cause them can require significant effort and time. The initial standup of a machine can provide opportunities for investigating the parameter space of events and operations and thus for gaining insight into the events of interest. In particular, failure inducement and investigation of corner case conditions can provide knowledge of system behavior for significant issues that will enable easier diagnosis and mitigation of such issues for when they may actually occur during the platform lifetime. In this work, we describe the testing process and monitoring results from a testbed system in preparation for the ACES Trinity system. We describe how events in the initial standup including changes in configuration and software and corner case testing has provided insights that can inform future monitoring and operating conditions, both of our test systems and the eventual large-scale Trinity system.

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This work demonstrates the integration of monitoring, analysis, and feedback to perform application-to-resource mapping that adapts to both static architecture features and dynamic resource state. In particular, we present a framework for mapping MPI tasks to compute resources based on run-time analysis of system-wide network data, architecture-specific routing algorithms, and application communication patterns. We address several challenges. Within each node, we collect local utilization data. We consolidate that information to form a global view of system performance, accounting for system-wide factors including competing applications. We provide an interface for applications to query the global information. Then we exploit the system information to change the mapping of tasks to nodes so that system bottlenecks are avoided. We demonstrate the benefit of this monitoring and feedback by remapping MPI tasks based on route-length, bandwidth, and credit-stalls metrics for a parallel sparse matrix-vector multiplication kernel. In the best case, remapping based on dynamic network information in a congested environment recovered 48.9% of the time lost to congestion, reducing matrix-vector multiplication time by 7.8%. Our experiments focus on the Cray XE/XK platform, but the integration concepts are generally applicable to any platform for which applicable metrics and route knowledge can be obtained.

As computer systems grow in both size and complexity, the need for applications and run-time systems to adjust to their dynamic environment also grows. The goal of the RAAMP LDRD was to combine static architecture information and real-time system state with algorithms to conserve power, reduce communication costs, and avoid network contention. We devel- oped new data collection and aggregation tools to extract static hardware information (e.g., node/core hierarchy, network routing) as well as real-time performance data (e.g., CPU uti- lization, power consumption, memory bandwidth saturation, percentage of used bandwidth, number of network stalls). We created application interfaces that allowed this data to be used easily by algorithms. Finally, we demonstrated the benefit of integrating system and application information for two use cases. The first used real-time power consumption and memory bandwidth saturation data to throttle concurrency to save power without increasing application execution time. The second used static or real-time network traffic information to reduce or avoid network congestion by remapping MPI tasks to allocated processors. Results from our work are summarized in this report; more details are available in our publications [2, 6, 14, 16, 22, 29, 38, 44, 51, 54].

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Understanding how resources of High Performance Compute platforms are utilized by applications both individually and as a composite is key to application and platform performance. Typical system monitoring tools do not provide sufficient fidelity while application profiling tools do not capture the complex interplay between applications competing for shared resources. To gain new insights, monitoring tools must run continuously, system wide, at frequencies appropriate to the metrics of interest while having minimal impact on application performance. We introduce the Lightweight Distributed Metric Service for scalable, lightweight monitoring of large scale computing systems and applications. We describe issues and constraints guiding deployment in Sandia National Laboratories' capacity computing environment and on the National Center for Supercomputing Applications' Blue Waters platform including motivations, metrics of choice, and requirements relating to the scale and specialized nature of Blue Waters. We address monitoring overhead and impact on application performance and provide illustrative profiling results.

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This report documents thirteen of Sandia's contributions to the Computational Systems and Software Environment (CSSE) within the Advanced Simulation and Computing (ASC) program between fiscal years 2009 and 2012. It describes their impact on ASC applications. Most contributions are implemented in lower software levels allowing for application improvement without source code changes. Improvements are identified in such areas as reduced run time, characterizing power usage, and Input/Output (I/O). Other experiments are more forward looking, demonstrating potential bottlenecks using mini-application versions of the legacy codes and simulating their network activity on Exascale-class hardware. The purpose of this report is to prove that the team has completed milestone 4467-Demonstration of a Legacy Application's Path to Exascale. Cielo is expected to be the last capability system on which existing ASC codes can run without significant modifications. This assertion will be tested to determine where the breaking point is for an existing highly scalable application. The goal is to stretch the performance boundaries of the application by applying recent CSSE RD in areas such as resilience, power, I/O, visualization services, SMARTMAP, lightweight LWKs, virtualization, simulation, and feedback loops. Dedicated system time reservations and/or CCC allocations will be used to quantify the impact of system-level changes to extend the life and performance of the ASC code base. Finally, a simulation of anticipated exascale-class hardware will be performed using SST to supplement the calculations. Determine where the breaking point is for an existing highly scalable application: Chapter 15 presented the CSSE work that sought to identify the breaking point in two ASC legacy applications-Charon and CTH. Their mini-app versions were also employed to complete the task. There is no single breaking point as more than one issue was found with the two codes. The results were that applications can expect to encounter performance issues related to the computing environment, system software, and algorithms. Careful profiling of runtime performance will be needed to identify the source of an issue, in strong combination with knowledge of system software and application source code.

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This report provides documentation for the completion of the Sandia Level II milestone 'Develop feedback system for intelligent dynamic resource allocation to improve application performance'. This milestone demonstrates the use of a scalable data collection analysis and feedback system that enables insight into how an application is utilizing the hardware resources of a high performance computing (HPC) platform in a lightweight fashion. Further we demonstrate utilizing the same mechanisms used for transporting data for remote analysis and visualization to provide low latency run-time feedback to applications. The ultimate goal of this body of work is performance optimization in the face of the ever increasing size and complexity of HPC systems.

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The rate of failures in HPC systems continues to increase as the number of components comprising the systems increases. System logs are one of the valuable information sources that can be used to analyze system failures and their root causes. However, system log files are usually too large and complex to analyze manually. There are some existing log clustering tools that seek to help analysts in exploring these logs, however they fail to satisfy our needs with respect to scalability, usability and quality of results. Thus, we have developed a log clustering tool to better address these needs. In this paper we present our novel approach and initial experimental results. © Springer-Verlag 2011.

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This document describes how to obtain, install, use, and enjoy a better life with OVIS version 3.2. The OVIS project targets scalable, real-time analysis of very large data sets. We characterize the behaviors of elements and aggregations of elements (e.g., across space and time) in data sets in order to detect meaningful conditions and anomalous behaviors. We are particularly interested in determining anomalous behaviors that can be used as advance indicators of significant events of which notification can be made or upon which action can be taken or invoked. The OVIS open source tool (BSD license) is available for download at ovis.ca.sandia.gov. While we intend for it to support a variety of application domains, the OVIS tool was initially developed for, and continues to be primarily tuned for, the investigation of High Performance Compute (HPC) cluster system health. In this application it is intended to be both a system administrator tool for monitoring and a system engineer tool for exploring the system state in depth. OVIS 3.2 provides a variety of statistical tools for examining the behavior of elements in a cluster (e.g., nodes, racks) and associated resources (e.g., storage appliances and network switches). It provides an interactive 3-D physical view in which the cluster elements can be colored by raw or derived element values (e.g., temperatures, memory errors). The visual display allows the user to easily determine abnormal or outlier behaviors. Additionally, it provides search capabilities for certain scheduler logs. The OVIS capabilities were designed to be highly interactive - for example, the job search may drive an analysis which in turn may drive the user generation of a derived value which would then be examined on the physical display. The OVIS project envisions the capabilities of its tools applied to compute cluster monitoring. In the future, integration with the scheduler or resource manager will be included in a release to enable intelligent resource utilization. For example, nodes that are deemed less healthy (i.e., nodes that exhibit outlier behavior with respect to some set of variables shown to be correlated with future failure) can be discovered and assigned to shorter duration or less important jobs. Further, HPC applications with fault-tolerant capabilities would respond to changes in resource health and other OVIS notifications as needed, rather than undertaking preventative measures (e.g. checkpointing) at regular intervals unnecessarily.

Effective failure prediction and mitigation strategies in high-performance computing systems could provide huge gains in resilience of tightly coupled large-scale scientific codes. These gains would come from prediction-directed process migration and resource servicing, intelligent resource allocation, and checkpointing driven by failure predictors rather than at regular intervals based on nominal mean time to failure. Given probabilistic associations of outlier behavior in hardware-related metrics with eventual failure in hardware, system software, and/or applications, this paper explores approaches for quantifying the effects of prediction and mitigation strategies and demonstrates these using actual production system data. We describe contextrelevant methodologies for determining the accuracy and cost-benefit of predictors. © 2010 IEEE.

As HPC systems grow in size and complexity, diagnosing problems and understanding system behavior, including failure modes, becomes increasingly difficult and time consuming. At Sandia National Laboratories we have developed a tool, OVIS, to facilitate large scale HPC system understanding. OVIS incorporates an intuitive graphical user interface, an extensive and extendable data analysis suite, and a 3-D visualization engine that allows visual inspection of both raw and derived data on a geometrically correct representation of a HPC system. This talk will cover system instrumentation, data collection (including log files and the complications of meaningful parsing), analysis, visualization of both raw and derived information, and how data can be combined to increase system understanding and efficiency.

Accurate failure prediction in conjunction with efficient process migration facilities including some Cloud constructs can enable failure avoidance in large-scale high performance computing (HPC) platforms. In this work we demonstrate a prototype system that incorporates our probabilistic failure prediction system with virtualization mechanisms and techniques to provide a whole system approach to failure avoidance. This work utilizes a failure scenario based on a real-world HPC case study. © 2010 IEEE.

Improved resource utilization and fault tolerance of large-scale HPC systems can be achieved through fine grained, intelligent, and dynamic resource (re)allocation. We explore components and enabling technologies applicable to creating a system to provide this capability: specifically 1) Scalable fine-grained monitoring and analysis to inform resource allocation decisions, 2) Virtualization to enable dynamic reconfiguration, 3) Resource management for the combined physical and virtual resources and 4) Orchestration of the allocation, evaluation, and balancing of resources in a dynamic environment. We discuss both general and HPC-centric issues that impact the design of such a system. Finally, we present our prototype system, giving both design details and examples of its application in real-world scenarios.

This report summarizes the current statistical analysis capability of OVIS and how it works in conjunction with the OVIS data readers and interpolators. It also documents how to extend these capabilities. OVIS is a tool for parallel statistical analysis of sensor data to improve system reliability. Parallelism is achieved using a distributed data model: many sensors on similar components (metaphorically sheep) insert measurements into a series of databases on computers reserved for analyzing the measurements (metaphorically shepherds). Each shepherd node then processes the sheep data stored locally and the results are aggregated across all shepherds. OVIS uses the Visualization Tool Kit (VTK) statistics algorithm class hierarchy to perform analysis of each process's data but avoids VTK's model aggregation stage which uses the Message Passing Interface (MPI); this is because if a single process in an MPI job fails, the entire job will fail. Instead, OVIS uses asynchronous database replication to aggregate statistical models. OVIS has several additional features beyond those present in VTK that, first, accommodate its particular data format and, second, improve the memory and speed of the statistical analyses. First, because many statistical algorithms are multivariate in nature and sensor data is typically univariate, interpolation of data is required to provide simultaneous observations of metrics. Note that in this report, we will refer to a single value obtained from a sensor as a measurement while a collection of multiple sensor values simultaneously present in the system is an observation. A base class for interpolation is provided that abstracts the operation of converting multiple sensor measurements into simultaneous observations. A concrete implementation is provided that performs piecewise constant temporal interpolation of multiple metrics across a single component. Secondly, because calculations may summarize data too large to fit in memory OVIS analyses batches of observations at a time and aggregates these intermediate intra-process models as it goes before storing the final model for inter-process aggregation via database replication. This reduces the memory footprint of the analysis, interpolation, and the database client and server query processing. This also interleaves processing with the disk I/O required to fetch data from the database - also improving speed. This report documents how OVIS performs analyses and how to create additional analysis components that fetch measurements from the database, perform interpolation, or perform operations on streamed observations (such as model updates or assessments). The rest of this section outlines the OVIS analysis algorithm and is followed by sections specific to each subtask. Note that we are limiting our discussion for now to the creation of a model from a set of measurements, and not including the assessment of observations using a model. The same framework can be used for assessment but that use case is not detailed in this report.

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The ability to predict impending failures (hardware or software) on large scale high performance compute (HPC) platforms, augmented by checkpoint mechanisms could drastically increase the scalability of applications and efficiency of platforms. In this paper we present our findings and methodologies employed to date in our search for reliable, advance indicators of failures on a 288 node, 4608 core, Opteron based cluster in production use at Sandia National Laboratories. In support of this effort we have deployed OVIS, a Sandia-developed scalable HPC monitoring, analysis, and visualization tool designed for this purpose. We demonstrate that for a particular error case, statistical analysis using OVIS would enable advanced warning of cluster problems on timescales that would enable application and system administrator response in advance of errors, subsequent system error log reporting, and job failures. This is significant as the utility of detecting such indicators depends on how far in advance of failure they can be recognized and how reliable they are. Copyright 2009 ACM.

Using the cloud computing paradigm, a host of companies promise to make huge compute resources available to users on a pay-as-you-go basis. These resources can be configured on the fly to provide the hardware and operating system of choice to the customer on a large scale. While the current target market for these resources in the commercial space is web development/hosting, this model has the lure of savings of ownership, operation, and maintenance costs, and thus sounds like an attractive solution for people who currently invest millions to hundreds of millions of dollars annually on High Performance Computing (HPC) platforms in order to support large-scale scientific simulation codes. Given the current interconnect bandwidth and topologies utilized in these commercial offerings, however, the only current viable market in HPC would be small-memoryfootprint embarrassingly parallel or loosely coupled applications, which inherently require little to no inter-processor communication. While providing the appropriate resources (bandwidth, latency, memory, etc.) for the HPC community would increase the potential to enable HPC in cloud environments, this would not address the need for scalability and reliability, crucial to HPC applications. Providing for these needs is particularly difficult in commercial cloud offerings where the number of virtual resources can far outstrip the number of physical resources, the resources are shared among many users, and the resources may be heterogeneous. Advanced resource monitoring, analysis, and configuration tools can help address these issues, since they bring the ability to dynamically provide and respond to information about the platform and application state and would enable more appropriate, efficient, and flexible use of the resources key to enabling HPC. Additionally such tools could be of benefit to non-HPC cloud providers, users, and applications by providing more efficient resource utilization in general. © 2009 IEEE.

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This document describes how to obtain, install, use, and enjoy a better life with OVIS version 2.0. The OVIS project targets scalable, real-time analysis of very large data sets. We characterize the behaviors of elements and aggregations of elements (e.g., across space and time) in data sets in order to detect anomalous behaviors. We are particularly interested in determining anomalous behaviors that can be used as advance indicators of significant events of which notification can be made or upon which action can be taken or invoked. The OVIS open source tool (BSD license) is available for download at ovis.ca.sandia.gov. While we intend for it to support a variety of application domains, the OVIS tool was initially developed for, and continues to be primarily tuned for, the investigation of High Performance Compute (HPC) cluster system health. In this application it is intended to be both a system administrator tool for monitoring and a system engineer tool for exploring the system state in depth. OVIS 2.0 provides a variety of statistical tools for examining the behavior of elements in a cluster (e.g., nodes, racks) and associated resources (e.g., storage appliances and network switches). It calculates and reports model values and outliers relative to those models. Additionally, it provides an interactive 3D physical view in which the cluster elements can be colored by raw element values (e.g., temperatures, memory errors) or by the comparison of those values to a given model. The analysis tools and the visual display allow the user to easily determine abnormal or outlier behaviors. The OVIS project envisions the OVIS tool, when applied to compute cluster monitoring, to be used in conjunction with the scheduler or resource manager in order to enable intelligent resource utilization. For example, nodes that are deemed less healthy, that is, nodes that exhibit outlier behavior in some variable, or set of variables, that has shown to be correlated with future failure, can be discovered and assigned to shorter duration or less important jobs. Further, applications with fault-tolerant capabilities can invoke those mechanisms on demand, based upon notification of a node exhibiting impending failure conditions, rather than performing such mechanisms (e.g. checkpointing) at regular intervals unnecessarily.

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The Feature Characterization Library (FCLib) is a software library that simplifies the process of interrogating, analyzing, and understanding complex data sets generated by finite element applications. This document provides an overview of the library, a description of both the design philosophy and implementation of the library, and examples of how the library can be utilized to extract understanding from raw datasets.

We report on the work done in the late-start LDRDUsing Emulation and Simulation toUnderstand the Large-Scale Behavior of the Internet. We describe the creation of a researchplatform that emulates many thousands of machines to be used for the study of large-scale inter-net behavior. We describe a proof-of-concept simple attack we performed in this environment.We describe the successful capture of a Storm bot and, from the study of the bot and furtherliterature search, establish large-scale aspects we seek to understand via emulation of Storm onour research platform in possible follow-on work. Finally, we discuss possible future work.3

Resource utilization in High Performance Compute clusters can be improved by increased awareness of system state information. Sophisticated run-time characterization of system state in increasingly large clusters requires a scalable fault-tolerant RAS framework. In this paper we describe the architecture of OVIS-2 and how it meets these requirements. We describe some of the sophisticated statistical analysis, 3-D visualization, and use cases for these. Using this framework and associated tools allows the engineer to explore the behaviors and complex interactions of low level system elements while simultaneously giving the system administrator their desired level of detail with respect to ongoing system and component health. ©2008 IEEE.

The current trend in high performance computing is to aggregate ever larger numbers of processing and interconnection elements in order to achieve desired levels of computational power, This, however, also comes with a decrease in the Mean Time To Interrupt because the elements comprising these systems are not becoming significantly more robust. There is substantial evidence that the Mean Time To Interrupt vs. number of processor elements involved is quite similar over a large number of platforms. In this paper we present a system that uses hardware level monitoring coupled with statistical analysis and modeling to select processing system elements based on where they lie in the statistical distribution of similar elements. These characterizations can be used by the scheduler/resource manager to deliver a close to optimal set of processing elements given the available pool and the reliability requirements of the application. © 2008 IEEE.

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Traditional cluster monitoring approaches consider nodes in singleton, using manufacturer-specified extreme limits as thresholds for failure ''prediction''. We have developed a tool, OVIS, for monitoring and analysis of large computational platforms which, instead, uses a statistical approach to characterize single device behaviors from those of a large number of statistically similar devices. Baseline capabilities of OVIS include the visual display of deterministic information about state variables (e.g., temperature, CPU utilization, fan speed) and their aggregate statistics. Visual consideration of the cluster as a comparative ensemble, rather than as singleton nodes, is an easy and useful method for tuning cluster configuration and determining effects of real-time changes.

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Traditional cluster monitoring approaches consider nodes in singleton, using manufacturer-specified extreme limits as thresholds for failure "prediction". We have developed a tool, OVIS, for monitoring and analysis of large computational platforms which, instead, uses a statistical approach to characterize single device behaviors from those of a large number of statistically similar devices. Baseline capabilities of OVIS include the visual display of deterministic information about state variables (e.g., temperature, CPU utilization, fan speed) and their aggregate statistics. Visual consideration of the cluster as a comparative ensemble, rather than as singleton nodes, is an easy and useful method for tuning cluster configuration and determining effects of real-time changes. Additionally, OVIS incorporates a novel Bayesian inference scheme to dynamically infer models for the normal behavior of a system and to determine bounds on the probability of values evinced in the system. Individual node values that are unlikely given the current applicable model are flagged as aberrant. This can be a much earlier indicator of problems than waiting for the crossing of some threshold that is necessarily set high to preclude too many false alarms. We present OVIS and discuss its applications in cluster configuration and environmental tuning and to abnormality and problem discovery in our production clusters. © 2006 IEEE.

Effective monitoring of large computational clusters demands the analysis of a vast amount of raw data from a large number of machines. The fundamental interactions of the system are not, however, well-defined, making it difficult to draw meaningful conclusions from this data, even if one were able to efficiently handle and process it. In this paper we show that computational clusters, because they are comprised of a large number of identical machines, behave in a statistically meaningful fashion. We therefore can employ normal statistical methods to derive information about individual systems and their environment and to detect problems sooner than with traditional mechanisms. We discuss design details necessary to use these methods on a large system in a timely and low-impact fashion.

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