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TorqueDB: distributed Querying of Time-Series Data from Edge-local Storage 26th

TorqueDB: Distributed Querying of Time-Series Data from Edge...

引用

26th international Conference on parallel and distributed Computing (Euro-Par)

作者： Garg, Dhruv Shirolkar, Prathik Shukla, Anshu Simmhan, Yogesh Ericsson Res Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9783030576752;9783030576745

The rapid growth in edge computing devices as part of Internet of Things (IoT) allows real-time access to time-series data from 1000's of sensors. Such observations are often queried to optimize the health of the infrastructure. Recently, edge storage systems allow us to retain data on the edge rather than moving them centrally to the cloud. However, such systems do not support flexible querying over the data spread across 10-100's of devices. There is also a lack of distributed time-series databases that can run on the edge devices. Here, we propose TorqueDB, a distributed query engine over time-series data that operates on edge and fog resources. TorqueDB leverages our prior work on ElfStore, a distributed edge-local file store, and InfluxDB, a time-series database, to enable temporal queries to be decomposed and executed across multiple fog and edge devices. Interestingly, we move data into InfluxDB on-demand while retaining the durable data within ElfStore for use by other applications. We also design a cost model that maximizes parallel movement and execution of the queries across resources, and utilizes caching. Our experiments on a real edge, fog and cloud deployment show that TorqueDB performs comparable to InfluxDB on a cloud VM for a smart city query workload, but without the associated monetary costs.

关键词： Edge storage Time-series database distributed querying Internet of Things Cloud computing

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A Real-Time Hardware-in-the-Loop (HIL) Cybersecurity Testbed for Power Electronics Devices and systems in Cyber-Physical Environments

A Real-Time Hardware-in-the-Loop (HIL) Cybersecurity Testbed...

引用

IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Jinchun Choi Deneesh Narayanasamy Bohyun Ahn Seerin Ahmad Jianwu Zeng Taesic Kim Texas A&M University-Kingsville Kingsville TX USA Minnesota State Univeristy Mankato Mankato USA

Cybersecurity of power electronics (PE) is increasingly essential as more systems from renewable energy systems, energy storage systems, and electric vehicle charging stations utilize PE devices to connect them into power grids with complex communication and computation systems for advanced control and situational awareness improvement in smart grid environments. However, cybersecurity research and development (R&D) of networked power electronics (PE) is hampered by the lack of real-time security testbed incorporating real cyber events. This paper proposes a real-time hardware-in-the-loop cybersecurity tested for PE systems in cyber-physical environments. The proposed security testbed consists of: 1) a real-time PE system simulator; 2) a real-time cyber system using real network systems and a server; and 3) penetration testing tools generating real Cyber-attacks as cyber events. Several real cyber-attacks are created and their impacts in a PE system are provided to validate the feasibility of the proposed security testbed.

关键词： Renewable energy sources distributed databases Tools Control systems Real-time systems Smart grids Servers

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Issues and Algorithm of distributed Shared Memory

Issues and Algorithm of Distributed Shared Memory

引用

international Conference on Innovative Computing (ICIC)

作者： Mian Basam Ahmad Sundas Sagheer University of Management and Technology Lahore Pakistan

ISBN: (纸本)9781665400923

Internet of things is a rapidly growing industry, and there are more connected devices than humans. There are unlimited internet of things devices around us. The scope of these devices has increased manifold in recent years in different areas like healthcare, unmanned vehicles, automatic monitoring systems, and smart homes, etc. These connected devices produce lots of data that need to be processed to achieve the goal of automation in daily life. The cloud provides many centralized solutions to process data using powerful resources in real-time, but there could be latency issues due to increased network traffic. So a distributed solution is much needed to meet this growing demand of processing data in parallel. In this paper, we propose a distributed shared memory [18] abstraction for the internet of things that uses a graph-aware partitioning algorithm and uses smart message passing techniques to process data in distributed and parallel fashion [19].

关键词： Manifolds Message passing distributed databases Telecommunication traffic Smart homes Medical services Real-time systems

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distributed Tree-Pattern Matching in Big Data Analytics systems 24th

Distributed Tree-Pattern Matching in Big Data Analytics Syst...

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24th East-European Conference on Advances in databases and Information systems/24th international Conference on Theory and Practice of Digital Libraries/16th Workshop on Business Intelligence and Big Data (ADBIS/TPDL/EDA)

作者： Diestelkamper, Ralf Hersche, Melanie Univ Stuttgart Stuttgart Germany Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9783030548315;9783030548322

Big data analytics systems such as Apache Spark offer built-in support for nested data, which abounds, for instance, as JSON data available online. However, these systems typically have to transform the data to gain access to (deeply) nested data for further processing. This adds complexity to big data analytics pipelines and may result in an unnecessary runtime overhead. Therefore, this paper introduces tree-pattern matching as a first-class operator in big data analytics systems. It reduces the complexity of big data analytics pipelines and accelerates the pipeline processing by up to four times, compared to state-of-the-art pipelines for nested data. The novelty of our operator lies in the distributed and data-parallel processing supported by its underlying tree-pattern matching algorithm. Experiments validate that our operator, implemented in Spark, can improve pipeline complexity and runtime.

关键词： Tree-pattern matching Nested data Big data analytics

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Digital Twin of Overseas Opera Relics: Case Study of Arhat Costumes in the Collection of the Metropolitan Museum of Art

Digital Twin of Overseas Opera Relics: Case Study of Arhat C...

引用

IEEE international symposium on parallel and distributed Processing with Applications and IEEE international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jingyuan Ren Ying Sun Xiaping Shu School of Applied Technology Soochow University Suzhou China School of Art Shandong Agriculture And Engineering University Jinan China School of Art Soochow University Suzhou China

Overseas opera relics are the materialized expression of Chinese opera civilization with a long history. The collection of overseas opera relics information, cataloguing and sorting, and the establishment of a digital database help to enrich and improve the physical data of domestic opera research. As an emerging digital modeling and virtual restoration technology, “digital twin” is of great significance in collecting digital resources of overseas opera relics and realizing digital restoration and global sharing of overseas opera relics. In this paper, after data collection of arhat costumes from the Metropolitan Museum of Art, database establishment, and the textual research and analysis of the shape, pattern and fabric, a “digital twin” of the arhat costumes through 3D modeling was created with the help of “digital twin” technology. which enables the preservation of overseas opera relics with digital technology and promotes the wide dissemination and creative transformation of overseas opera relics.

关键词： Art Shape databases Clothing Data collection Ubiquitous computing Museums

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Remove-Win: a Design Framework for Conflict-free Replicated Data Types

Remove-Win: a Design Framework for Conflict-free Replicated ...

引用

international Conference on parallel and distributed systems (ICPADS)

作者： Yuqi Zhang Hengfeng Wei Yu Huang National Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu Province China

ISBN: (纸本)9781665408790

distributed storage systems employ replication to improve performance and reliability. To provide low latency data access, replicas are often required to accept updates without coordination with each other, and the updates are then propagated asynchronously. This brings the critical challenge of conflict resolution among concurrent updates. Conflict-free Replicated Data Type (CRDT) is a principled approach to addressing this challenge. However, existing CRDT designs are tricky, and hard to be generalized to other data types. A design framework is in great need to guide the systematic design of new CRDTs. To address this challenge, we propose RWF - the Remove-Win design Framewerk for CRDTs. RWF leverages the simple but powerful remove-win strategy to resolve conflicting updates, and provides generic design for a variety of data container types. Two exemplar implementations following RWF are given over the Redis data type store, which demonstrate the effectiveness of RWF. Performance measurements of our implementations further show the efficiency of CRDT designs following RWF.

关键词： Measurement Systematics Conferences distributed databases Containers Reliability Low latency communication

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BSDP: A Novel Balanced Spark Data Partitioner

BSDP: A Novel Balanced Spark Data Partitioner

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international Conference on parallel and distributed systems (ICPADS)

作者： Aibo Song Bowen Peng Jingyi Qiu Yingying Xue Mingyang Du School of Computer Science and Engineering Southeast University Nanjing China Nanjing Foreign Language School Nanjing China

ISBN: (纸本)9781665408790

As a memory-based distributed big data computing framework, Spark has been widely used in big data processing systems. However, during the execution of Spark, due to the imbalance of input data distribution and the shortage of existing data partitioners in Spark, it is easy to cause partition skew problem and reduce the execution efficiency of Spark. Aiming at this problem, this paper proposes a balanced Spark data partitioner called BSDP (Balanced Spark Data Partitioner). By deeply analyzing the partitioning characteristics of Shuffle intermediate data, the Spark Shuffle intermediate data equalization partitioning model is established. The model aims to minimize the partition skew and find a Shuffle intermediate data equalization partitioning strategy. Based on the model, this paper designs and implements a data equalization partitioning algorithm of BSDP. This algorithm transforms the Shuffle intermediate data equalization partitioning problem into a classic List-Scheduling task scheduling problem, effectively realizes the balanced partitioning of Shuffle intermediate data. The experiment verifies that the BSDP can effectively realize the balanced partitioning of the Shuffle intermediate data and improve the execution efficiency of Spark.

关键词： Analytical models Computational modeling distributed databases Transforms Big Data Data models Scheduling

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CASH: A Credit Aware Scheduling for Public Cloud Platforms 21

CASH: A Credit Aware Scheduling for Public Cloud Platforms

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21st IEEE/ACM international symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Sharma, Aakash Dhakshinamurthy, Saravanan Kesidis, George Das, Chita R. Penn State Univ Comp Sci & Engn University Pk PA 16802 USA Facebook Inc Menlo Pk CA USA

ISBN: (纸本)9781728195865

distributed data processing frameworks such as Hadoop, Tez, Spark, and Flink are exclusively used by public cloud tenants for executing large scale data analytics applications in various domains including but not limited to content management, financial sector, healthcare etc. These frameworks slice a job into a number of smaller tasks, which are then executed by a job scheduler on a multi-node compute cluster. While making scheduling decisions, the State-of-art schedulers employed in these frameworks assume hardware resources such as CPU, disk I/O and network I/O to offer a fixed service rate. However, in a public cloud environment, many of these resources are associated with burstable service rates. More specifically, the resources offer a guaranteed baseline service rate with an option to burst above their baseline rate by expending accumulated burst credits. Being unaware about this underlying hardware burstability, schedulers tend to make sub-optimal task placement decisions, thereby adversely affecting the job completion times, leading to higher deployment costs. In this paper, we propose CASH, a burst credit aware scheduler, which is cognizant about the burst credits associated with the individual hardware resources in the public cloud cluster. Through coarse grained task annotations depicting the burst credit demand of individual tasks and dynamically monitoring the credits for the underlying resources, CASH performs optimal task placement decisions. We prototype CASH on YARN, Hadoop, and Tez, and extensively evaluate it using both batch and streaming workloads. Our experimental results with CASH show CPU-credit based instances, like AWS T3, are a viable cost effective alternative when compared to self-managed offerings like Amazon EMR, for running large scale batch workloads. Furthermore, we demonstrate that CASH can accelerate streaming SQL queries on a large Hive database by up to 39.4%, leading to public cloud cost savings by up to 22%.

关键词： Public Cloud Burst Credits Cluster Scheduling parallel Data Processing Cost Savings

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Never Too Late: Tracing and Mitigating Backdoor Attacks in Federated Learning

Never Too Late: Tracing and Mitigating Backdoor Attacks in F...

引用

Reliable distributed systems

作者： Hui Zeng Tongqing Zhou Xinyi Wu Zhiping Cai College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9781665497534

ISBN: (纸本)9781665497541

The privacy-preserving nature of Federated Learning (FL) exposes such a distributed learning paradigm to the planting of backdoors with locally corrupted data. We discover that FL backdoors, under a new on-off multi-shot attack form, are essentially stealthy against existing defenses that are built on model statistics and spectral analysis. First-hand observations of such attacks show that the backdoored models are indistinguishable from normal ones w.r.t. both low-level and high-level representations. We thus emphasize that a critical redemption, if not the only, for the tricky stealthiness is reactive tracing and posterior mitigation. A three-step remedy framework is then proposed by exploring the temporal and inferential correlations of models on a trapped sample from an attack. In particular, we use shift ensemble detection and co-occurrence analysis for adversary identification, and repair the model via malicious ingredients removal under theoretical error guarantee. Extensive experiments on various backdoor settings demonstrate that our framework can achieve accuracy on attack round identification of ∼80% and on attackers of ∼50%, which are ∼28.76% better than existing proactive defenses. Meanwhile, it can successfully eliminate the influence of backdoors with only a 5%∼6% performance drop.

关键词： Analytical models Computer aided instruction Correlation Federated learning Distance learning distributed databases Maintenance engineering

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Investigation on Conducted EMI for Single and parallel Connected Inverters

Investigation on Conducted EMI for Single and Parallel Conne...

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IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Mustafeez ul-Hassan Asif Imran Emon Hongwu Peng Choksi Kushan Fang Luo Stony Brook University Stony Brook NY USA University of Connecticut Storrs CT USA

Owing to increased penetration of sustainable energy sources, multiple converters are run in parallel to improve the power handling capacity in distributed generation systems. paralleling of converters needs to be investigated carefully; and advantages in terms of semiconductor efficiency, power quality and conducted electromagnetic interference (EMI) should be evaluated. It can be ensured that paralleling results in increased efficiency, and better EMI performance by effectively controlling the type of coupling between passive components and interleaving angle amongst carriers. This article, at first, compares common mode (CM) and differential mode (DM) EMI generated by simple two-level voltage source and current source inverter (2L-VSI, 2L-CSI), using sinusoidal pulse width modulation (SPWM). A simple equivalent circuit model for the conducted noise has also been derived for both the topologies. Based on the preliminary results, CSI was down selected for further investigations, and effect of paralleling of CSIs on conducted noise has been studied. In the analysis, coupled inductors with different coupling configurations and coupling coefficients were considered and optimal combination is presented.

关键词： Couplings Voltage source inverters Electromagnetic interference Power quality Pulse width modulation distributed power generation Topology

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