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Simulative performance analysis of gossip failure detection for scalable distributed systems

Cluster computing 1999年第3期2卷 207-217页

作者： Burns, Mark W. George, Alan D. Wallace, Bradley A. High-performance Computing and Simulation (HCS) Research Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville USA

Three protocols for gossip-based failure detection services in large-scale heterogeneous clusters are analyzed and compared. The basic gossip protocol provides a means by which failures can be detected in large distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications. The hierarchical protocol leverages the underlying network topology to achieve faster failure detection. In addition to studying the effectiveness and efficiency of these two agreement protocols, we propose a third protocol that extends the hierarchical approach by piggybacking gossip information on application-generated messages. The protocols are simulated and evaluated with a fault-injection model for scalable distributed systems comprised of clusters of workstations connected by high-performance networks, such as the CPlant system at Sandia National Laboratories. The model supports permanent and transient node and link failures, with rates specified at simulation time, for processors functioning in a fail-silent fashion. Through high-fidelity, CAD-based modeling and simulation, we demonstrate the strengths and weaknesses of each approach in terms of agreement time, number of gossips, and overall scalability.

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Experimental Analysis of a Gossip-Based Service for Scalable, Distributed Failure Detection and Consensus

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Cluster computing 2003年第3期6卷 237-251页

作者： Sistla, Krishnakanth George, Alan D. Todd, Robert W. High-performance Computing and Simulation (HCS) Research Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville USA

Gossip protocols and services provide a means by which failures can be detected in large, distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications. Extending the gossip protocol such that a system reaches consensus on detected faults can be performed via a flat structure, or it can be hierarchically distributed across cooperating layers of nodes. In this paper, the performance of gossip services employing flat and hierarchical schemes is analyzed on an experimental testbed in terms of consensus time, resource utilization and scalability. performance associated with a hierarchically arranged gossip scheme is analyzed with varying group sizes and is shown to scale well. Resource utilization of the gossip-style failure detection and consensus service is measured in terms of network bandwidth utilization and CPU utilization. Analytical models are developed for resource utilization and performance projections are made for large system sizes.

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis 40

Boosting Meaningful Dependency Mining with Clustering and Co...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Xi Jin, Ruochun Huang, Wanrong Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology Department of Intelligent Data Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350317152

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDM?. In order to scale with large datasets, FDM ? employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ? is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost. © 2024 IEEE.

关键词： Large datasets

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An energy-efficient virtual machine scheduler with I/O collective mechanism in resource virtualisation environments

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International Journal of Networking and Virtual Organisations 2013年第4期13卷 311-326页

作者： Xiao, Peng Liu, Dongbo Department of Computer Science High-Performance Computing Center Hunan Institute of Engineering Hunan Province China

Recently, resource virtualisation has been proven effective for deploying large-scale IT-infrastructures, such as grids and clouds. However, many studies also indicate that the system's energy-efficiency will be reduced when I/O virtualisation is involved. In this paper, we present an energy-efficiency enhanced virtual machine (VM) scheduler with aiming at reducing the energy-efficiency losses caused by I/O virtualisation. The proposed VM scheduler is incorporated with an I/O collective mechanism, which separates I/O-intensive VMs from CPU-intensive ones during the runtime and schedules them in a batch manner, so as to reduce the context-switching costs when scheduling intensive mixed workloads. Extensive experiments are conducted on various platforms by using different benchmarks to investigate the performance of the proposed policy. The experimental results indicate that when the virtualisation platform is in presence of mixed workloads, the proposed scheduler outperforms many existing VM schedulers in term of energy-efficiency. Copyright © 2013 Inderscience Enterprises Ltd.

关键词： Virtual machine

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Gossip-Style Failure Detection and Distributed Consensus for Scalable Heterogeneous Clusters

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Cluster computing 2001年第3期4卷 197-209页

作者： Ranganathan, Sridharan George, Alan D. Todd, Robert W. Chidester, Matthew C. High-performance Computing and Simulation (HCS) Research Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville USA

Gossip protocols provide a means by which failures can be detected in large, distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications. However, in order to be effective with application recovery and reconfiguration, these protocols require mechanisms by which failures can be detected with system-wide consensus in a scalable fashion. This paper presents three new gossip-style protocols supported by a novel algorithm to achieve consensus in scalable, heterogeneous clusters. The round-robin protocol improves on basic randomized gossiping by distributing gossip messages in a deterministic order that optimizes bandwidth consumption. Redundant gossiping is completely eliminated in the binary round-robin protocol, and the round-robin with sequence check protocol is a useful extension that yields efficient detection times without the need for system-specific optimization. The distributed consensus algorithm works with these gossip protocols to achieve agreement among the operable nodes in the cluster on the state of the system featuring either a flat or a layered design. The various protocols are simulated and evaluated in terms of consensus time and scalability using a high-fidelity, fault-injection model for distributed systems comprised of clusters of workstations connected by high-performance networks.

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Generative Methods for Out-of-distribution Prediction and Applications for Threat Detection and Analysis: A Short Review 1st

Generative Methods for Out-of-distribution Prediction and Ap...

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1st International Workshop on Digital Sovereignty in Cyber Security: New Challenges in Future Vision, CyberSec4Europe 2022

作者： Coppolillo, Erica Liguori, Angelica Guarascio, Massimo Pisani, Francesco Sergio Manco, Giuseppe Department of Mathematics and Computer Science University of Calabria Rende Italy Department of Computer Engineering Modeling Electronics an d Systems University of Calabria Rende Italy Institute for High Performance Computing and Networking of Italian National Research Council Rende Italy

ISBN: (纸本)9783031360954

In recent times, Machine Learning has played an important role in developing novel advanced tools for threat detection and mitigation. Intrusion Detection, Misinformation, Malware, and Fraud Detection are just some examples of cybersecurity fields in which Machine Learning techniques are used to reveal the presence of malicious behaviors. However, Out-of-Distribution, i.e., the potential distribution gap between training and test set, can heavily affect the performances of the traditional Machine Learning based methods. Indeed, they could fail in identifying out-of-samples as possible threats, therefore devising robust approaches to cope with this issue is a crucial and relevant challenge to mitigate the risk of undetected attacks. Moreover, a recent emerging line proposes to use generative models to yield synthetic likely examples to feed the learning algorithms. In this work, we first survey recent Machine Learning and Deep Learning based solutions to face both the problems, i.e., outlier detection and generation;then we illustrate the main cybersecurity application scenarios in which these approaches have been adopted successfully. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Learning systems

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Impact of sub-optimal checkpoint intervals on application efficiency in computational clusters 10

Impact of sub-optimal checkpoint intervals on application ef...

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Proceedings of the 19th ACM International Symposium on high performance Distributed computing

作者： Jones, William M. Daly, John T. DeBardeleben, Nathan Department of Computer Science Coastal Carolina University Conway SC United States Center for Exceptional Computing Department of Defense Fort Meade MD United States High Performance Computing Los Alamos National Laboratory Los Alamos NM United States

ISBN: (纸本)9781605589428

As computational clusters rapidly grow in both size and complexity, system reliability and, in particular, application resilience have become increasingly important factors to consider in maintaining efficiency and providing improved computational performance over predecessor systems. One commonly used mechanism for providing application fault tolerance in parallel systems is the use of checkpointing. By making use of a multi-cluster simulator, we study the impact of sub-optimal checkpoint intervals on overall application efficiency. By using a model of a 1926 node cluster and workload statistics from Los Alamos National laboratory to parameterize the simulator, we find that dramatically over-estimating the AMTTI has a fairly minor impact on application efficiency while potentially having a much more severe impact on user-centric performance metrics such a queueing delay. We compare and contrast these results with the trends predicted by an analytical model. Copyright 2010 ACM.

关键词： Fault tolerance

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On implementation of an enhanced and integrated parallel programming web-based toolkit

On implementation of an enhanced and integrated parallel pro...

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ITRE 2005 - 3rd International Conference on Information Technology: Research and Education

作者： Li, Kuan-Ching Yang, Chao-Tung Parallel and Distributed Processing Center Dept. of Computer Science and Information Management Providence University Shalu Taichung 43301 Taiwan High Performance Computing Laboratory Dept. of Computer Science and Information Engineering Tunghai University Taichung City Taichung 40704 Taiwan

ISBN: (纸本)0780389328

The computing power provided by high performance and low cost PC-based clusters and Grid computing platforms are attractive and they are equal or superior to supercomputers and mainframes. In parallel, discussions on how to obtain more computing power from these computing platforms become an interesting issue. The development of applications for these high-performance computing platforms is complicated for several reasons: The complexity of applications themselves, which combines aspects of supercomputing and distributed computing, and by the need to achieve higher performance. This paper describes the design rationale and implementation of a parallel programming web-based toolkit, to ease the parallel programming learning process, with the use of web-based interface. The toolkit has widely been used in MPI parallel programming courses (both in graduate and undergraduate levels) and industry trainings. © 2005 IEEE.

关键词： computer programming

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A framework for faster porting of scientific applications between heterogeneous clouds 1st

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1st International Conference on Infrastructure, Technologies and Applications, SCITA 2017

作者： Ahmed, Waseem Khan, Mohsin Khan, Adeel Ahmed Mehmood, Rashid Algarni, Abdullah Albeshri, Aiiad Katib, Iyad Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India High Performance Computing Center King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9783319941790

The emergence of pay-as-you-use compute clouds has enabled scientists to experiment with the latest processor architectures and accelerators. However, the lack of standardization in cloud computing, more specifically in the interoperability context, makes the task of portability of applications between clouds challenging. Two main tasks that users of multi-vendor clouds will need to perform are porting cost analysis and faster source-to-source translation. Cost analysis is essential to help evaluate the feasibility and cost of portability. And any automation of the source-to-source translation step will help developers perform the translation faster while taking advantage of platform-specific features. This paper presents a framework that assists a developer in performing these two tasks. The first task is achieved using the Maintainability Analyzer module which generates unique funnel shaped patterns that give an insight about the maintainability of an application and its potential for porting. Different scientific applications from various domains, that were developed using different programming paradigms, were evaluated using this module. For the second task, a set of modules use a knowledge repository to perform source-to-source translations while ensuring the maintainability of the generated code. The framework has been tested with different architecture and library combinations with promising results. © 2018, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Cost benefit analysis

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Evaluating the flexibility of dynamic loop scheduling on heterogeneous systems in the presence of fluctuating load using SimGrid

Evaluating the flexibility of dynamic loop scheduling on het...

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2013 IEEE 37th Annual computer Software and Applications Conference, COMPSAC 2013

作者： Sukhija, Nitin Banicescu, Ioana Srivastava, Srishti Ciorba, Florina M. Mississippi State University Department of Computer Science and Engineering Mississippi State MS 39762 United States Technische Universiẗat Dresden Center for Information Services and High Performance Computing 01062 Dresden Germany

ISBN: (纸本)9780769549798

Scientific applications running on heterogeneous computing systems, which often have unpredictable behavior, enhance their performance by employing loop scheduling techniques as methods to avoid load imbalance through an optimized assignment of their parallel loops. With current computing platforms facilitating petascale performance and promising exascale performance towards the end of the present decade, efficient and robust algorithms are required to guarantee optimal performance of parallel applications in the presence of unpredictable perturbations. A number of dynamic loop scheduling (DLS) methods based on probabilistic analyses have been developed to achieve the desired robust performance. In earlier work, two metrics (flexibility and resilience) have been formulated to quantify the robustness of various DLS methods in heterogeneous computing systems with uncertainties. In this work, to ensure robust performance of the scientific applications on current (petascale) and future(exascale) high performance computing systems, a simulation model was designed and integrated into the SimGrid simulation toolkit, thus enabling a comprehensive study of the robustness of the DLS methods which uses results of experimental cases with various combinations of number of processors, problem sizes, and scheduling methods. The DLS methods have been implemented into the simulation model and analyzed for the purpose of exploring their flexibility (robustness against unpredictable variations in the system load), when involved in a range of case scenarios comprised of various distributions characterizing loop iteration execution times and system availability. The simulation results reported are used to compare the robustness of the DLS methods under the various environments considered, using the flexibility metric. © 2013 IEEE.

关键词： Iterative methods

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