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Message from the HCW Steering Committee Chair

Message from the HCW Steering Committee Chair

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Behrooz Shiraz Washington State University Pullman Washington USA

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

These are the proceedings of the “29th Heterogeneity in Computing Workshop,” also known as HCW 2020. A few years ago, the title of the workshop was changed from the original title of “Heterogeneous Computing Workshop” to reflect the breadth of the impact of heterogeneity, as well as to stress that the focus of the workshop is on the management and exploitation of heterogeneity. All of this is, of course, taken in the context of the parent conference, the international parallel and distributed Processing symposium (IPDPS), and so explores heterogeneity in parallel and distributed computing systems.

关键词： Conferences distributed processing Heterogeneous networks distributed computing Task analysis Program processors Stress

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Large scale querying and processing for property graphs PhD symposium 22

Large scale querying and processing for property graphs PhD ...

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22nd international Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, DOLAP 2020

作者： Ragab, Mohamed Data Systems Group University of Tartu Tartu Estonia

Recently, large scale graph data management, querying and processing have experienced a renaissance in several timely application domains (e.g., social networks, bibliographical networks and knowledge graphs). However, these applications still introduce new challenges with large-scale graph processing. Therefore, recently, we have witnessed a remarkable growth in the prevalence of work on graph processing in both academia and industry. Querying and processing large graphs is an interesting and challenging task. Recently, several centralized/distributed large-scale graph processing frameworks have been developed. However, they mainly focus on batch graph analytics. On the other hand, the state-of-the-art graph databases can't sustain for distributed efficient querying for large graphs with complex queries. In particular, online large scale graph querying engines are still limited. In this paper, we present a research plan shipped with the state-of-the-art techniques for large-scale property graph querying and processing. We present our goals and initial results for querying and processing large property graphs based on the emerging and promising Apache Spark framework, a defacto standard platform for big data processing. In principle, the design of this research plan is revolving around two main goals. The first goal focuses on designing an adequate and efficient graph-based storage backend that can be integrated with the Apache Spark framework. The second goal focuses on developing various Graph-aware optimization techniques (e.g., graph indexing, graph materialized views), and extending the default relational Spark Catalyst optimizer with Graph-aware cost-based optimizations. Achieving these contributions can significantly enhance the performance of executing graph queries on top of Apache Spark. © Copyright 2020 for this paper held by its author(s).

关键词： Graph databases

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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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LADD: A Length-Adaptive Approach to Detecting Taxi Anomalous Detours 25

LADD: A Length-Adaptive Approach to Detecting Taxi Anomalous...

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25th IEEE international Conference on parallel and distributed systems (IEEE ICPADS)

作者： Lu, Xuansheng Cheng, Weiyu Shen, Yanyan Zhu, Yanmin Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728125831

This paper focuses on the practical problem of detecting taxi anomalous detours. In this paper, we propose a length-adaptive detection approach called LADD to detect if a taxi conducted an anomalous detour by investigating the taxi journey's trajectory. We employ a similarity-based detection strategy, which takes trajectory length into consideration. Our approach can deal with both long and short trajectories by applying weighted similarity on trajectories and using trajectory concatenation or clipping. For quantitative performance comparison, we propose a performance metric, and compare our approach with the state-of-the-art methods on a real-world dataset. The results verify the superior performance of LADD on taxi detour detection.

关键词： Big data processing spatial databases detour detecting GPS trajectories intelligent transportation systems

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Semantically Correct and Intent Defined Commutativity in distributed systems

Semantically Correct and Intent Defined Commutativity in Dis...

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international symposium on parallel and distributed Computing

作者： Courtney Robinson Department of Computing & Information Systems University of Greenwich London UK

ISBN: (数字)9781728189468

ISBN: (纸本)9781728189475

This paper reviews the problem of correctness in the context of scaleable distributed systems. It takes the concept of a strongly eventually consistent (SEC) data structure based on the work done on conflict free replicated data types and examines the theoretical basis of a practical application to generalised database management systems. It reasons that the condition required for a database management system to implement a monotonic semilattice and remain generic is not possible. It further proposes a framework which extends the SEC condition to capture enough information to make such a system practical to implement. Finally, it evaluates such an implementation using a modified storage engine for Apache Ignite demonstrating not just its practicality but its scaleability in Ignite and other database management systems like it. The key contributions of this work are Semantic and intent defined conflict free replicated data types, an enriched CRDT which enables generalised database systems to meet the SEC criteria for a monotonic semilattice object and Semantic Intent Configuration Language, SICL, a language and framework for distributed computing.

关键词： Semantics Software Database systems Data structures distributed databases Software algorithms Convergence

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d'Artagnan: A Trusted NoSQL Database on Untrusted Clouds 38

d'Artagnan: A Trusted NoSQL Database on Untrusted Clouds

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IEEE 38th international symposium on Reliable distributed systems (SRDS)

作者： Pontes, Rogerio Maia, Francisco Vilaca, Ricardo Machado, Nuno INESC TEC HASLab High Assurance Software Lab Porto Portugal Univ Minho Braga Portugal Teradata San Diego CA USA INESC TEC Porto Portugal

ISBN: (纸本)9781728142227

Privacy sensitive applications that store confidential information such as personal identifiable data or medical records have strict security concerns. These concerns hinder the adoption of the cloud. With cloud providers under the constant threat of malicious attacks, a single successful breach is sufficient to exploit any valuable information and disclose sensitive data. Existing privacy-aware databases mitigate some of these concerns, but sill leak critical information that can potently compromise the entire system's security. This paper proposes d'Artagnan, the first privacy-aware multi-cloud NoSQL database framework that renders database leaks worthless. The framework stores data as encrypted secrets in multiple clouds such that i) a single data breach cannot break the database's confidentiality and ii) queries are processed on the server-side without leaking any sensitive information. d'Artagnan is evaluated with industry-standard benchmark on marketleading cloud providers.

关键词： Multi party Privacy aware databases Secure query processing

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Approaches to Speed up Data Processing in Relational databases 13

Approaches to Speed up Data Processing in Relational Databas...

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13th international symposium on Intelligent systems (INTELS)

作者： Shichkina, Yulia St Petersburg Electrotech Univ LETI Prof Popova Str5 St Petersburg 197376 Russia

The increasing of data volumes and the tightening of requirements by the time of data processing actualize the problem of finding methods for optimizing data structures and queries in databases. This article presents a set of methods that can help speed up the processing of data. These include the method of restructuring a completed database, a method for obtaining a parallel query plan, and methods for optimizing queries. The presented methods can be used both in a complex and independently of each other. At the end of the article, are shown the results of the experiments, which were carried out on a test database for approbation of optimization methods for a parallel query plan. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 13th international symposium "Intelligent systems" (INTELS'18).

关键词： database query parallel computing information graph optimization

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Operational Equivalence of distributed Hypothesis Testing and Identification systems

Operational Equivalence of Distributed Hypothesis Testing an...

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IEEE international symposium on Information Theory (ISIT)

作者： Minh Thanh Vu Oechtering, Tobias J. Skoglund, Mikael KTH Royal Inst Technol Div Informat Sci & Engn Stockholm Sweden

ISBN: (纸本)9781538692912

In this paper we revisit the connections of the distributed hypothesis testing against independence (HT) problem with the Wyner-Ahlswede-Korner (WAK) problem and the identification systems (ID). We show that the strong converse for the WAK problem is equivalent to the strong converse for the HT problem via constructive and nonconstructive transformations of codes. As another consequence of the transformation we provide a new exponentially strong converse equivalence statement. Applying the same idea, we prove a new result that the epsilon-identification capacity of the ID problem is equal to the maximum epsilon-exponent of type II of error in the HT problem when both side compression is allowed.

关键词： Testing Convergence Transforms databases Encoding Decoding Information science

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ESCAPe: Elastic Caching For Big Data systems 38

ESCAPe: Elastic Caching For Big Data Systems

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IEEE 38th international symposium on Reliable distributed systems (SRDS)

作者： Priovolos, Thanasis Maroulis, Stathis Kalogeraki, Vana Athens Univ Econ & Business Dept Infomat Athens Greece

ISBN: (纸本)9781728142227

In recent years, in-memory cache systems have been commonly utilized to help maintain low application response times, compared to traditional relational databases, where the data items are stored in disk drives. Although cache memory systems offer improved performance, running everything inmemory might not be cost-effective. In this paper, we present ESCAPe, an elastic high throughput and low latency key-value in-memory cache system. Unlike existing schemes, ESCAPe offers an elastic mechanism that proactively adds or removes nodes to scale-down or scale-up to meet fluctuating application demands, and incorporates a dynamic redistribution scheme that prioritizes the distribution of the keys at the nodes, while keeping the overhead cost as low as possible. We have evaluated our approach in a real cluster, using ESCAPe as the memcache system for Web Applications using different workload traces and comparing our approach with state of the art schemes. Our results illustrate that ESCAPe is able to select the most useful items to keep in memory, significantly reducing the end-to-end latency experienced by the applications.

关键词： Memory Cache Elasticity Redis distributed systems

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