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Assessing Data Usefulness for Failure Analysis in Anonymized System Logs 17

Assessing Data Usefulness for Failure Analysis in Anonymized...

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17th Annual international symposium on parallel and distributed Computing (ISPDC)

作者： Ghiasvand, Siavash Ciorba, Florina M. Tech Univ Dresden Dresden Germany Univ Basel Basel Switzerland

ISBN: (纸本)9781538653302

System logs are a valuable source of information for the analysis and understanding of systems behavior for the purpose of improving their performance. Such logs contain various types of information, including sensitive information. Information deemed sensitive can either directly be extracted from system log entries by correlation of several log entries, or can be inferred from the combination of the (non-sensitive) information contained within system logs with other logs and/or additional datasets. The analysis of system logs containing sensitive information compromises data privacy. Therefore, various anonymization techniques, such as generalization and suppression have been employed, over the years, by data and computing centers to protect the privacy of their users, their data, and the system as a whole. Privacy-preserving data resulting from anonymization via generalization and suppression may lead to significantly decreased data usefulness, thus, hindering the intended analysis for understanding the system behavior. Maintaining a balance between data usefulness and privacy preservation, therefore, remains an open and important challenge. Irreversible encoding of system logs using collision-resistant hashing algorithms, such as SHAKE-128, is a novel approach previously introduced by the authors to mitigate data privacy concerns. The present work describes a study of the applicability of the encoding approach from earlier work on the system logs of a production high performance computing system. Moreover, a metric is introduced to assess the data usefulness of the anonymized system logs to detect and identify the failures encountered in the system.

关键词： Data privacy Encoding Encryption Measurement distributed databases High performance computing Privacy"

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Policy-Based Adaptation of a Byzantine Fault Tolerant distributed Graph Database 37

Policy-Based Adaptation of a Byzantine Fault Tolerant Distri...

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37th IEEE international symposium on Reliable distributed systems (SRDS)

作者： Bravo, Manuel Rodrigues, Luis Neiheiser, Ray Rech, Luciana ULisboa IST INESC ID Lisbon Portugal Univ Fed Santa Catarina DAS INE Florianopolis SC Brazil

ISBN: (纸本)9781538683019

Modern fault-tolerant distributed architectures can be configured to tolerate a wide-range of faults. For instance, Fireplug is a distributed BFT graph database, based on n-version programming, that can be configured to tolerate crash or Byzantine faults, uncorrelated faults in individual machines, correlated faults that affect all replicas running a given software version, or correlated faults that affect an entire datacenter. Interestingly, in such a system, fault handling heavily depends on the type of faults the system is configured to tolerate. To hardwire all possible behaviours in the fault-handling code is inflexible and may even be impractical. In this paper, we explore a different alternative that consists in specifying not only the system configuration, but also the fault-handling behaviour, and how the system adapts to changes in the workload, in a policy language, that is processed externally to the managed system. We show that, using this approach, a single simplified codebase of the managed system can be used effectively to address a wide range of dependability constraints.

关键词： Protocols databases Fault tolerance Fault tolerant systems Software Measurement Adaptive systems

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HPDC 2018 - proceedings of The 27th international symposium on High-Performance parallel and distributed Computing Posters/Doctoral Consortium

HPDC 2018 - Proceedings of The 27th International Symposium ...

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27th ACM international symposium on High-Performance parallel and distributed Computing, HPDC 2018

ISBN: (纸本)9781450358996

The proceedings contain 10 papers. The topics discussed include: an intelligent framework to parallelize hadoop phases;performance evaluation of a toolkit for sparse tensor decomposition;Hermes: a heterogeneous-aware multi-tiered distributed I/O buffering system;profiling distributed systems in lightweight virtualized environments with logs and resource metrics;making profit with albatross: a runtime system for heterogeneous high-performance-computing clusters;lightweight authentication of fault-tolerant topic-channel queries in distributed systems;are existing knowledge transfer techniques effective to support deep learning on edge devices?;exploring the capabilities of mobile devices supporting deep learning;and locality-driven MRC construction and cache allocation.

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proceedings - 2018 IEEE 32nd international parallel and distributed Processing symposium, IPDPS 2018

Proceedings - 2018 IEEE 32nd International Parallel and Dist...

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32nd IEEE international parallel and distributed Processing symposium, IPDPS 2018

ISBN: (纸本)9781538643686

The proceedings contain 113 papers. The topics discussed include: optimizing parallel graph connectivity computation via subgraph sampling;a parallel algorithm for Bayesian network inference using arithmetic circuits;cataloging the visible universe through Bayesian inference at petascale;efficient, parallel at-scale correlation analysis for atom probe tomography on hybrid architectures;a fast and massively-parallel inverse solver for multiple-scattering tomographic image reconstruction;real-time massively distributed multi-object adaptive optics simulations for the European extremely large telescope;performance isolation of data-intensive scale-out applications in a multi-tenant cloud;scalable data resilience for in-memory data staging;and performance and scalability of lightweight multi-kernel based operating systems.

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24th international Conference on Database systems for Advanced Applications, DASFAA 2019

24th International Conference on Database Systems for Advanc...

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24th international Conference on Database systems for Advanced Applications, DASFAA 2019

ISBN: (纸本)9783030185756

The proceedings contain 183 papers. The special focus in this conference is on Database systems for Advanced Applications. The topics include: Multi-view spectral clustering via multi-view weighted consensus and matrix-decomposition based discretization;SIRCS: Slope-intercept-residual compression by correlation sequencing for multi-stream high variation data;fast quorum-based log replication and replay for fast databases;PDCS: A privacy-preserving distinct counting scheme for mobile sensing;reinforced reliable worker selection for spatial crowdsensing networks;SeqST-ResNet: A sequential spatial temporal resnet for task prediction in spatial crowdsourcing;towards robust arbitrarily oriented subspace clustering;truthful crowdsensed data trading based on reverse auction and blockchain;selective matrix factorization for multi-relational data fusion;a frequency scaling based performance indicator framework for big data systems;selectivity estimation on set containment search;typicality-based across-time mapping of entity sets in document archives;unsupervised entity alignment using attribute triples and relation triples;combining meta-graph and attention for recommendation over heterogenous information network;efficient search of the most cohesive co-located community in attributed networks;a weighted word embedding model for text classification;bipartite network embedding via effective integration of explicit and implicit relations;enhancing network embedding with implicit clustering;MDAL: Multi-task dual attention LSTM model for semi-supervised network embedding;net2Text: An edge labelling language model for personalized review generation;a time-series sockpuppet detection method for dynamic social relationships;understanding information diffusion via heterogeneous information network embeddings;distributed parallel Structural Hole Detection on Big Graphs;DynGraphGAN: Dynamic graph embedding via generative adversarial networks;evaluating mixed patterns on large data gr

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SEECSSim - A parallel and distributed Simulation Framework for Mobile Devices 18

SEECSSim - A Parallel and Distributed Simulation Framework f...

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22nd IEEE/ACM international symposium on distributed Simulation and Real Time Applications (DS-RT)

作者： Maqbool, Fahad Malik, Asad Waqar Mahmood, Imran D'Angelo, Gabriele Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Dept Comp Islamabad Pakistan Univ Bologna Dept Comp Sci & Engn DISI Bologna Italy

ISBN: (纸本)9781538650486

On battery-operated devices, energy and power consumption are main concerns. With the recent advancement of technology, mobile devices can be integrated with traditional systems for running complex computations. In fact, mobile devices can easily become part of computational networks and share their computational and memory resources. Despite this, traditional simulation frameworks are not designed to perform well on heterogeneous networks. This is mainly due to the limited computational resources that are available on mobile devices. In this paper, we propose SEECSSim (SEECSSim is derived from School of Electrical Engineering and Computer Science (SEECS)) that is a simulation framework specifically designed for mobile devices. SEECSSim includes state-of-theart distributed synchronization algorithms that are implemented to run on mobile or embedded devices. To benchmark the proposed framework, the well-known PHOLD model is used and performance results are reported in terms of execution time, CPU usage, memory and energy consumption.

关键词： PDES Time Warp Conservative Optimistic Mobile Devices

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Lightweight Authentication for Fault-Tolerant Topic-Channels in Ad Hoc distributed systems 18

Lightweight Authentication for Fault-Tolerant Topic-Channels...

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27th ACM international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Behrens, Hans Walter Candan, K. Selcuk Arizona State Univ Tempe AZ 85281 USA

ISBN: (纸本)9781450358996

Many distributed systems assume participants are both performant and secure, characteristics offered by many cloud-based systems. However, scaling distributed techniques down to highly resource- or power-constrained contexts may require alternative approaches. One such context is the deployment of ad hoc distributed systems in insecure or uncontrolled areas, for example during disaster response activities. Providing reliable and secure service is exacerbated by the computational and power constraints imposed on these devices. In this work, we first introduce the concept of on-demand topic-channels. Then, we describe three message authentication protocols which provide secure, authenticated communication between participants and a coordinator, while also providing resilience from adversarial or accidental disruption. We leverage homomorphic hashing primitives to trade message secrecy against communication and computational costs. Finally, we assess these protocols, and show that our hash-based protocols provide significant efficiency improvements over traditional encryption-based approaches.

关键词： distributed systems homomorphic hashing messaging protocols

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15th international symposium on Applied Reconfigurable Computing, ARC 2019

15th International Symposium on Applied Reconfigurable Compu...

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15th international symposium on Applied Reconfigurable Computing, ARC 2019

ISBN: (纸本)9783030172268

The proceedings contain 28 papers. The special focus in this conference is on Applied Reconfigurable Computing. The topics include: Proof-Carrying Hardware Versus the Stealthy Malicious LUT Hardware Trojan;secure Local Configuration of Intellectual Property Without a Trusted Third Party;HiFlipVX: An Open Source High-Level Synthesis FPGA Library for Image Processing;Real-Time FPGA Implementation of Connected Component Labelling for a 4K Video Stream;A Scalable FPGA-Based Architecture for Depth Estimation in SLAM;Evaluating LULESH Kernels on OpenCL FPGA;The TaPaSCo Open-Source Toolflow for the Automated Composition of Task-Based parallel Reconfigurable Computing systems;Graph-Based Code Restructuring Targeting HLS for FPGAs;UltraSynth: Integration of a CGRA into a Control Engineering Environment;exploiting Reconfigurable Vector Processing for Energy-Efficient Computation in 3D-Stacked Memories;Optimizing CNN-Based Hyperspectral Image Classification on FPGAs;Automatic Toolflow for VCGRA Generation to Enable CGRA Evaluation for Arithmetic Algorithms;reM: A Reconfigurable Multipotent Cell for New distributed Reconfigurable Architectures;Update or Invalidate: Influence of Coherence Protocols on Configurable HW Accelerators;hybrid Prototyping for Manycore Design and Validation;Evaluation of FPGA Partitioning Schemes for Time and Space Sharing of Heterogeneous Tasks;Third Party CAD Tools for FPGA Design—A Survey of the Current Landscape;Filter-Wise Pruning Approach to FPGA Implementation of Fully Convolutional Network for Semantic Segmentation;Exploring Data Size to Run Convolutional Neural Networks in Low Density FPGAs;Faster Convolutional Neural Networks in Low Density FPGAs Using Block Pruning;Supporting Columnar In-memory Formats on FPGA: The Hardware Design of Fletcher for Apache Arrow;A Novel Encoder for TDCs.

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Improving parallelism in Data-Intensive Workflows with distributed databases

Improving Parallelism in Data-Intensive Workflows with Distr...

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IEEE international Conference on Services Computing (IEEE SCC) Part of the IEEE World Congress on Services

作者： Watanabe, Elaine Naomi Braghetto, Kelly Rosa Univ Sao Paulo Inst Math & Stat Dept Comp Sci Rua Matao 1010 BR-05508090 Sao Paulo SP Brazil

ISBN: (纸本)9781538672501

The efficient execution of data-intensive workflows relies on strategies to enable parallel data processing, such as partitioning and replicating data across distributed resources. The maximum degree of parallelism a workflow can reach during its execution is usually defined at design time. However, designing workflow models capable to provide an efficient use of distributed computing platforms is not a simple task and requires specialized expertise. Furthermore, since Workflow Management systems see workflow activities as black-boxes, they are not able to automatically explore data parallelism in the workflow execution. To address this problem, in this work we propose a novel method to automatically improve data parallelism in workflows based on annotations that characterize how activities access and consume data. For an annotated workflow model, the method defines a model transformation and a database setup (including data sharding, replication, and indexing) to support data parallelism in a distributed environment. To evaluate this approach, we implemented and tested two workflows that process up to 20.5 million data objects from real-world datasets. We executed each model in 21 different scenarios in a cluster on a public cloud, using a centralized relational database and a distributed NoSQL database. The automatic parallelization created by the proposed method reduced the execution times of these workflows up to 66.6%, without increasing the monetary costs of their execution.

关键词： parallel processing distributed databases Data models Integrated circuits Data collection"

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Profiling distributed systems in Lightweight Virtualized Environments with Logs and Resource Metrics 18

Profiling Distributed Systems in Lightweight Virtualized Env...

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27th ACM international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Pi, Aidi Chen, Wei Zhou, Xiaobo Univ Colorado Colorado Springs Colorado Springs CO 80918 USA

ISBN: (纸本)9781450358996

Understanding and troubleshooting distributed systems in the cloud is considered a very difficult problem because the execution of a single user request is distributed to multiple machines. Further, the multi-tenancy nature of cloud environments further introduces interference that causes performance issues. Most existing troubleshooting tools either focus on log analysis or intrusive tracing methods, leaving resource usage monitoring unexplored. We propose and implement LRTrace, a non-intrusive tracing and feedback control tool for distributed applications in lightweight virtualized environments. LRTrace profiles both log messages and actual resource consumptions of an application at runtime in a fine-grained manner, which is made possible by lightweight container-based virtualization. By correlating these two kinds of information, LRTrace provides users the ability to build the relationship between changes in resource consumption and application events. Furthermore, LRTrace allows users to define and implement their own feedback control plug-ins to manage the cluster in a semiautomatic manner. In system evaluation, we run Spark and MapReduce applications in a multi-tenant cluster and show that LRTrace can diagnose performance issues caused by either interference or bugs, or both. It also helps users to understand the workflows of data-parallel applications.

关键词： Troubleshooting Lightweight virtualization Logs Resource metrics Data-parallel applications

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