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Enabling Conflict-free Collaborations with Cloud Storage Services

Enabling Conflict-free Collaborations with Cloud Storage Ser...

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international Conference on parallel and distributed systems (ICPADS)

作者： Minghao Zhao Jian Chen Zhenhua Li School of Software Tsinghua University

ISBN: (纸本)9781665408790

Cloud storage services (e.g., Dropbox) have become pervasive in not only simple file sharing but also advanced collaborative file editing (collaboration for short). Using Dropbox for collaboration is much easier than SVN and Git, thus greatly facilitating common users. In practice, however, many Dropbox users are perplexed by unexpected collaboration conflicts, which severely impair their experiences. Through various benchmark experiments, we unveil the two root causes of collaboration conflicts: 1) Dropbox never locks an edited file during collaboration; 2) Dropbox only guarantees eventual data consistency among the collaborators, significantly aggravating the probability of conflicts. In this paper, we attempt to enable conflict-free collaborations with Dropbox-like cloud storage services. This attempt is empowered by three key findings and measures. First, although the end-to-end sync delay is unpredictable due to eventual consistency, we can always track the latest version of an edited file by actively resorting to the cloud via certain web APIs. Second, although all application-level data is encrypted in Dropbox, we can roughly deduce the sync status from traffic statistics. Third, applying a couple of useful mechanisms (e.g., distributed architecture and data lock) learned from Git, we can effectively and efficiently avoid collaboration conflicts-of course, this requires re-implementing Git mechanisms in cloud storage services with minimum overhead and user interference. Integrating above efforts, we build the ConflictReaper system capable of helping users automatically avoid almost all collaboration conflicts with affordable network and computation overhead.

关键词： Cloud computing Conferences Collaboration distributed databases Interference Computer architecture Peer-to-peer computing

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IEdroid:Detecting Malicious Android Network Behavior Using Incremental Ensemble of Ensembles

IEdroid:Detecting Malicious Android Network Behavior Using I...

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international Conference on parallel and distributed systems (ICPADS)

作者： Cong Liu Anli Yan Zhenxiang Chen Haibo Zhang Qiben Yan Lizhi Peng Chuan Zhao Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan China School of Information Science and Engineering University of Jinan China School of Cyberspace Security (School of Cryptology) Hainan University China University of Otago Dunedin New Zealand Michigan State University USA

ISBN: (纸本)9781665408790

Malware detection has attracted widespread attention due to the growing malware sophistication. Machine learning based methods have been proposed to find traces of malware by analyzing network traffic. However, network traffic exhibits a series of growing and changing states, which makes it challenging to design a detection model that can detect malicious traffic over a long period without the need for costly retraining. In this paper, we present, IEdroid, an Android malicious network behavior detection method that leverages incremental ensembles for model update. Specifically, we train multiple classifiers to form an interim ensemble in distributed cluster environment, and update the interim ensemble by removing and adding classifiers. The generated model is composed of multiple interim ensembles that can adapt to the network traffic. We evaluated the performance of IEdroid using a dataset consisting of 98,565 benign and 41,267 malicious flows. Results show that IEdroid can effectively detect malicious traffic compared with state-of-the-art detection models. The experiment trained IEdroid on datasets incrementally for 10 times without a significant loss on accuracy, precision, recall, and F-Measure, compared with re-training from scratch with full data.

关键词： Training Adaptation models distributed databases Prototypes Telecommunication traffic Machine learning Malware

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distributed State Estimation for Large-Scale systems in the Presence of Data Packet Drops

Distributed State Estimation for Large-Scale Systems in the ...

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IEEE international symposium on Information (IT) in Medicine and Education, ITME

作者： Xiaoyu Fu Xinmin Song School of Information Science and Engineering Shandong Normal University Jinan China

ISBN: (纸本)9781728194202

This article focuses on the distributed state estimation problem for large-scale systems in the presence of data packet drops. The large-scale system is structured in several correlated subsystems in the physical space, and each subsystem only communicates with its neighbors. In particular, the states of different subsystems are measured by different sensors, and the sensor broadcasts measurement information to the subestimator and its neighbors through the lossy communication channel. Thus, subestimators obtain different local information in the presence of data packet drops. In this article, the distributed estimator is designed and the optimal gain is obtained under the minimum mean square error (MMSE) estimation criterion by using local information set. Finally, the effectiveness of the distributed estimator is illustrated by a simulation experiment.

关键词： Atmospheric measurements Simulation distributed databases Packet loss Mean square error methods Particle measurements Loss measurement

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iez: Resource Contention Aware Load Balancing for Large-Scale parallel File systems 33

iez: Resource Contention Aware Load Balancing for Large-Scal...

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Wadhwa, Bharti Paul, Arnab K. Neuwirth, Sarah Wang, Feiyi Oral, Sarp Butt, Ali R. Bernard, Jon Cameron, Kirk W. Virginia Tech Blacksburg VA 24061 USA Heidelberg Univ Heidelberg Germany Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781728112466

parallel I/O performance is crucial to sustaining scientific applications on large-scale High-Performance Computing (HPC) systems. However, I/O load imbalance in the underlying distributed and shared storage systems can significantly reduce overall application performance. There are two conflicting challenges to mitigate this load imbalance: (i) optimizing system-wide data placement to maximize the bandwidth advantages of distributed storage servers, i.e., allocating I/O resources efficiently across applications and job runs;and (ii) optimizing client-centric data movement to minimize I/O load request latency between clients and servers, i.e., allocating I/O resources efficiently in service to a single application and job run. Moreover, existing approaches that require application changes limit wide-spread adoption in commercial or proprietary deployments. We propose iez, an "end-to-end control plane" where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load information for distributed storage server global data placement while our design model leverages trace-based optimization techniques to minimize I/O load request latency between clients and servers. We evaluate our proposed system on an experimental cluster for two common use cases: synthetic I/O benchmark IOR for large sequential writes and a scientific application I/O kernel, HACC-I/O. Results show read and write performance improvements of up to 34% and 32%, respectively, compared to the state of the art.

关键词： Servers distributed databases Load management Benchmark testing Resource management Libraries Metadata

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Pipelining Graph Construction and Agent-based Computation over distributed Memory

Pipelining Graph Construction and Agent-based Computation ov...

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IEEE international Conference on Big Data

作者： Yan Hong Munehiro Fukuda Computing and Software Systems University of Washington Bothell Bothell

ISBN: (纸本)9781665480468

Graph streaming has received substantial attention for the past 10+ years to cope with large-scale graph computation. Two major approaches, one using conventional data-streaming tools and the other accessing graph databases, facilitate continuous analysis of endlessly flowing graphs and query-based incremental construction of huge graphs, respectively. However, some scientific graphs including biological networks need to stay in memory for repetitive but various analyses. Although a cluster system, thus distributed memory can entirely handle a big graph in memory, a challenge is substantial overhead incurred by loading graphs into memory. A solution is hiding such graph-loading and construction overheads with graph computation in a pipelined fashion. We adapted this pipelining approach for agent-based graph computing where thousands of agents traverse a graph for finding its attributes and shape. We used the multi-agent spatial simulation (MASS) library to implement the concept. A huge graph is incrementally constructed in batches, each spawning and walking agents over the corresponding subgraph, and thus all eventually completing a given computation. We coded and ran two MASS benchmark programs: triangle counting and connected components, with which we evaluated our pipelined graph processing. The best performance was obtained once the batch size shrunk enough to fit cache memory, regardless of the number of cluster nodes. For a single node execution of connected components over a 140MB graph, our graph-pipelining implementation performed 7.7 times faster than non-pipelining execution. Its parallel execution with 24 cluster nodes achieved 8.3 times speed-up as compared to the pipelined single-node execution.

关键词： Shape Scalability Loading XML Big Data Size measurement Libraries

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Fractal Intersections and Products via Algorithmic Dimension

Fractal Intersections and Products via Algorithmic Dimension

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作者： Lutz, Neil Department of Computer Science Iowa State University AmesIA50011 United States

Algorithmic fractal dimensions quantify the algorithmic information density of individual points and may be defined in terms of Kolmogorov complexity. This work uses these dimensions to bound the classical Hausdorff and packing dimensions of intersections and Cartesian products of fractals in Euclidean spaces. This approach shows that two prominent, fundamental results about the dimension of Borel or analytic sets also hold for arbitrary sets. © 2021 ACM.

关键词： parallel processing systems

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Towards HPC I/O Performance Prediction Through Large-scale Log Analysis 20

Towards HPC I/O Performance Prediction Through Large-scale L...

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29th international Conference on High-Performance parallel and distributed Computing (HPDC)

作者： Kim, Sunggon Sim, Alex Wu, Kesheng Byna, Suren Son, Yongseok Eom, Hyeonsang Seoul Natl Univ Seoul South Korea Lawrence Berkeley Natl Lab Berkeley CA USA Chung Ang Univ Seoul South Korea

ISBN: (纸本)9781450370523

Large-scale high performance computing (HPC) systems typically consist of many thousands of CPUs and storage units, while used by hundreds to thousands of users at the same time. Applications from these large numbers of users have diverse characteristics, such as varying compute, communication, memory, and I/O intensiveness. A good understanding of the performance characteristics of each user application is important for job scheduling and resource provisioning. Among these performance characteristics, the I/O performance is difficult to predict because the I/O system software is complex, the I/O system is shared among all users, and the I/O operations also heavily rely on networking systems. To improve the prediction of the I/O performance on HPC systems, we propose to integrate information from a number of different system logs and develop a regression-based approach that dynamically selects the most relevant features from the most recent log entries, and automatically select the best regression algorithm for the prediction task. Evaluation results show that our proposed scheme can predict the I/O performance with up to 84% prediction accuracy in the case of the I/O-intensive applications using the logs from CORI supercomputer at NERSC.

关键词： High Performance Computing distributed File System I/O Performance Prediction Log Analysis

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Incremental Graph Processing for On-Line Analytics 33

Incremental Graph Processing for On-Line Analytics

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Sallinen, Scott Pearce, Roger Ripeanu, Matei Univ British Columbia Vancouver BC Canada Lawrence Livermore Natl Lab Livermore CA 94550 USA

ISBN: (纸本)9781728112466

Modern data generation is enormous;we now capture events at increasingly fine granularity, and require processing at rates approaching real-time. For graph analytics, this explosion in data volumes and processing demands has not been matched by improved algorithmic or infrastructure techniques. Instead of exploring solutions to keep up with the velocity of the generated data, most of today's systems focus on analyzing individually built historic snapshots. Modern graph analytics pipelines must evolve to become viable at massive scale, and move away from static, post-processing scenarios to support on-line analysis. This paper presents our progress towards a system that analyzes dynamic incremental graphs, responsive at single-change granularity. We present an algorithmic structure using principles of recursive updates and monotonic convergence, and a set of incremental graph algorithms that can be implemented based on this structure. We also present the required middleware to support graph analytics at fine, event-level granularity. We envision that graph topology changes are processed asynchronously, concurrently, and independently (without shared state), converging an algorithm's state (e.g. single-source shortest path distances, connectivity analysis labeling) to its deterministic answer. The expected long-term impact of this work is to enable a transition away from offfine graph analytics, allowing knowledge to be extracted from networked systems in real-time.

关键词： Heuristic algorithms Real-time systems Topology Convergence Engines distributed databases Network topology

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Data Jockey: Automatic Data Management for HPC Multi-Tiered Storage systems 33

Data Jockey: Automatic Data Management for HPC Multi-Tiered ...

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Shin, Woong Brumgard, Christopher D. Xie, Bing Vazhkudai, Sudharshan S. Ghoshal, Devarshi Oral, Sarp Ramakrishnan, Lavanya Oak Ridge Natl Lab Oak Ridge TN 37830 USA Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781728112466

We present the design and implementation of Data Jockey, a data management system for HPC multi-tiered storage systems. As a centralized data management control plane, Data Jockey automates bulk data movement and placement for scientific workflows and integrates into existing HPC storage infrastructures. Data Jockey simplifies data management by eliminating human effort in programming complex data movements, laying datasets across multiple storage tiers when supporting complex workflows, which in turn increases the usability of multi-tiered storage systems emerging in modern HPC data centers. Specifically, Data Jockey presents a new data management scheme called "goal driven data management" that can automatically infer low-level bulk data movement plans from declarative high-level goal statements that come from the lifetime of iterative runs of scientific workflows. While doing so, Data Jockey aims to minimize data wait times by taking responsibility for datasets that are unused or to be used, and aggressively utilizing the capacity of the upper, higher performant storage tiers. We evaluated a prototype implementation of Data Jockey under a synthetic workload based on a year's worth of Oak Ridge Leadership Computing Facility's (OLCF) operational logs. Our evaluations suggest that Data Jockey leads to higher utilization of the upper storage tiers while minimizing the programming effort of data movement compared to human involved, per-domain ad-hoc data management scripts.

关键词： Task analysis Automation Complexity theory Data analysis Computer architecture distributed databases Programming

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A Role-Based Architecture for Self-Adaptive Cyber-Physical systems 15

A Role-Based Architecture for Self-Adaptive Cyber-Physical S...

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IEEE/ACM 15th international symposium on Software Engineering for Adaptive and Self-Managing systems (SEAMS)

作者： Kluge, Tim Tech Univ Dresden Software Technol Grp Dresden Germany

ISBN: (纸本)9781450379625

Today's computing world features a growing number of cyber-physical systems that require the cooperation of many physical devices. Examples include autonomous cars and co-working robots, which are expected to appropriately adapt to any possible context they find themselves in (e.g. the presence of a nearby human). However, the controlling software continues to be developed using established object-oriented modelling techniques like UML, which do not natively possess a notion of context and thus may introduce accidental complexity. With increasing complexity, the probability of the introduction of software errors rises, which can have fatal consequences in cyber-physical systems. To address this, we envision a model-driven architecture for self-adaptive cyberphysical systems that explicitly models structured context. Entities are modelled as message-passing parallel processes and can play roles in specific contexts, which dynamically alter their behaviour and relationships with other parts of the system. Since the planning of complex adaptations can be cumbersome in real-world scenarios, we envision an intuitive formulation of adaptations as graph rewriting rules on the context model. This paper discusses the current state of research and identifies open research challenges. Based on this, the envisioned architecture as well as an evaluation strategy are presented.

关键词： self-adaptive systems context-sensitivity roles distributed systems decentralized systems graph rewriting

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