Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems最新文献

The role of event-time order in data streaming analysis 事件时间顺序在数据流分析中的作用

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems Pub Date : 2020-07-13 DOI: 10.1145/3401025.3404088

Vincenzo Gulisano, Dimitris Palyvos-Giannas, Bastian Havers, M. Papatriantafilou

{"title":"The role of event-time order in data streaming analysis","authors":"Vincenzo Gulisano, Dimitris Palyvos-Giannas, Bastian Havers, M. Papatriantafilou","doi":"10.1145/3401025.3404088","DOIUrl":"https://doi.org/10.1145/3401025.3404088","url":null,"abstract":"The data streaming paradigm was introduced around the year 2000 to overcome the limitations of traditional store-then-process paradigms found in relational databases (DBs). Opposite to DBs' \"first-the-data-then-the-query\" approach, data streaming applications build on the \"first-the-query-then-the-data\" alternative. More concretely, data streaming applications do not rely on storage to initially persist data and later query it, but rather build on continuous single-pass analysis in which incoming streams of data are processed on the fly and result in continuous streams of outputs. In contrast with traditional batch processing, data streaming applications require the user to reason about an additional dimension in the data: event-time. Numerous models have been proposed in the literature to reason about event-time, each with different guarantees and trade-offs. Since it is not always clear which of these models is appropriate for a particular application, this tutorial studies the relevant concepts and compares the available options. This study can be highly relevant for people working with data streaming applications, both researchers and industrial practitioners.","PeriodicalId":207154,"journal":{"name":"Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128352579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

hSPICE hSPICE

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems Pub Date : 2020-07-13 DOI: 10.1145/3401025.3401742

Ahmad Slo, Sukanya Bhowmik, K. Rothermel

{"title":"hSPICE","authors":"Ahmad Slo, Sukanya Bhowmik, K. Rothermel","doi":"10.1145/3401025.3401742","DOIUrl":"https://doi.org/10.1145/3401025.3401742","url":null,"abstract":"In complex event processing (CEP), load shedding is performed to maintain a given latency bound during overload situations when there is a limitation on resources. However, shedding load implies degradation in the quality of results (QoR). Therefore, it is crucial to perform load shedding in a way that has the lowest impact on QoR. Researchers, in the CEP domain, propose to drop either events or partial matches (PMs) in overload cases. They assign utilities to events or PMs by considering either the importance of events or the importance of PMs but not both together. In this paper, we propose a load shedding approach for CEP systems that combines these approaches by assigning a utility to an event by considering both the event importance and the importance of PMs. We adopt a probabilistic model that uses the type and position of an event in a window and the state of a PM to assign a utility to an event corresponding to each PM. We, also, propose an approach to predict a utility threshold that is used to drop the required amount of events to maintain a given latency bound. By extensive evaluations on two real-world datasets and several representative queries, we show that, in the majority of cases, our load shedding approach outperforms state-of-the-art load shedding approaches, w.r.t. QoR.","PeriodicalId":207154,"journal":{"name":"Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123353317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

The Kaiju project: enabling event-driven observability Kaiju项目:启用事件驱动的可观察性

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems Pub Date : 2020-07-13 DOI: 10.1145/3401025.3401740

Mario Scrocca, Riccardo Tommasini, Alessandro Margara, Emanuele Della Valle, S. Sakr

{"title":"The Kaiju project: enabling event-driven observability","authors":"Mario Scrocca, Riccardo Tommasini, Alessandro Margara, Emanuele Della Valle, S. Sakr","doi":"10.1145/3401025.3401740","DOIUrl":"https://doi.org/10.1145/3401025.3401740","url":null,"abstract":"Microservices architectures are getting momentum. Even small and medium-size companies are migrating towards cloud-based distributed solutions supported by lightweight virtualization techniques, containers, and orchestration systems. In this context, understanding the system behavior at runtime is critical to promptly react to errors. Unfortunately, traditional monitoring techniques are not adequate for such complex and dynamic environments. Therefore, a new challenge, namely observability, emerged from precise industrial needs: expose and make sense of the system behavior at runtime. In this paper, we investigate observability as a research problem. We discuss the benefits of events as a unified abstraction for metrics, logs, and trace data, and the advantages of employing event stream processing techniques and tools in this context. We show that an event-based approach enables understanding the system behavior in near real-time more effectively than state-of-the-art solutions in the field. We implement our model in the Kaiju system and we validate it against a realistic deployment supported by a software company.","PeriodicalId":207154,"journal":{"name":"Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123570871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

TinTiN: Travelling in time (if necessary) to deal with out-of-order data in streaming aggregation 丁丁:及时旅行(如果必要的话)来处理流聚合中的乱序数据

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems Pub Date : 2020-07-13 DOI: 10.1145/3401025.3401769

Joris van Rooij, Vincenzo Gulisano, M. Papatriantafilou

{"title":"TinTiN: Travelling in time (if necessary) to deal with out-of-order data in streaming aggregation","authors":"Joris van Rooij, Vincenzo Gulisano, M. Papatriantafilou","doi":"10.1145/3401025.3401769","DOIUrl":"https://doi.org/10.1145/3401025.3401769","url":null,"abstract":"Cyber-Physical Systems (CPS) rely on data stream processing for high-throughput, low-latency analysis with correctness and accuracy guarantees (building on deterministic execution) for monitoring, safety or security applications. The trade-offs in processing performance and results' accuracy are nonetheless application-dependent. While some applications need strict deterministic execution, others can value fast (but possibly approximated) answers. Despite the existing literature on how to relax and trade strict determinism for efficiency or deadlines, we lack a formal characterization of levels of determinism, needed by industries to assess whether or not such trade-offs are acceptable. To bridge the gap, we introduce the notion of D-bounded eventual determinism, where D is the maximum out-of-order delay of the input data. We design and implement TinTiN, a streaming middleware that can be used in combination with user-defined streaming applications, to provably enforce D-bounded eventual determinism. We evaluate TinTiN with a real-world streaming application for Advanced Metering Infrastructure (AMI) monitoring, showing it provides an order of magnitude improvement in processing performance, while minimizing delays in output generation, compared to a state-of-the-art strictly deterministic solution that waits for time proportional to D, for each input tuple, before generating output that depends on it.","PeriodicalId":207154,"journal":{"name":"Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems","volume":"381 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115852830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pre-processing and data validation in IoT data streams 物联网数据流的预处理和数据验证

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems Pub Date : 2020-07-13 DOI: 10.1145/3401025.3406443

Philsy Baban

{"title":"Pre-processing and data validation in IoT data streams","authors":"Philsy Baban","doi":"10.1145/3401025.3406443","DOIUrl":"https://doi.org/10.1145/3401025.3406443","url":null,"abstract":"In the last few years, distributed stream processing engines have been on the rise due to their crucial impacts on real-time data processing with guaranteed low latency in several application domains such as financial markets, surveillance systems, manufacturing, smart cities, etc. Stream processing engines are run-time libraries to process data streams without knowing the lower level streaming mechanics. Apache Storm, Apache Flink, Apache Spark, Kafka Streams and Hazelcast Jet are some of the popular stream processing engines. Nowadays, critical systems like energy systems, are interconnected and automated. As a result, these systems are vulnerable to cyber-attacks. In real-world applications, the sensing values come from sensor devices contains missing values, redundant data, data outliers, manipulated data, data failures, etc. Therefore, our system must be resilient to these conditions. In this paper, we present an approach to check if there is any above mentioned conditions by pre-processing data streams using a stream processing engine like Apache Flink which will be updated as a library in future. Then, the pre-processed streams are forwarded to other stream processing engines like Apache Kafka for real stream processing. As a result, data validation, data consistency and integrity for a resilient system can be accomplished before initiating the actual stream processing.","PeriodicalId":207154,"journal":{"name":"Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134353249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A survey of adaptive sampling and filtering algorithms for the internet of things 物联网自适应采样滤波算法综述

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems Pub Date : 2020-07-13 DOI: 10.1145/3401025.3403777

Dimitrios Giouroukis, Alexander Dadiani, J. Traub, Steffen Zeuch, V. Markl

{"title":"A survey of adaptive sampling and filtering algorithms for the internet of things","authors":"Dimitrios Giouroukis, Alexander Dadiani, J. Traub, Steffen Zeuch, V. Markl","doi":"10.1145/3401025.3403777","DOIUrl":"https://doi.org/10.1145/3401025.3403777","url":null,"abstract":"The Internet of Things (IoT) represents one of the fastest emerging trends in the area of information and communication technology. The main challenge in the IoT is the timely gathering of data streams from potentially millions of sensors. In particular, those sensors are widely distributed, constantly in transit, highly heterogeneous, and unreliable. To gather data in such a dynamic environment efficiently, two techniques have emerged over the last decade: adaptive sampling and adaptive filtering. These techniques dynamically reconfigure rates and filter thresholds to trade-off data quality against resource utilization. In this paper, we survey representative, state-of-the-art algorithms to address scalability challenges in real-time and distributed sensor systems. To this end, we cover publications from top peer-reviewed venues for a period larger than 12 years. For each algorithm, we point out advantages, disadvantages, assumptions, and limitations. Furthermore, we outline current research challenges, future research directions, and aim to support readers in their decision process when designing extremely distributed sensor systems.","PeriodicalId":207154,"journal":{"name":"Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115483806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Trade-off analysis of thermal-constrained scheduling strategies in multi-core systems 多核系统热约束调度策略的权衡分析

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems Pub Date : 2020-07-13 DOI: 10.1145/3401025.3406442

Muhammad Usama Sardar

引用次数: 0

Optimized parallel implementation of sequential clustering-based event detection 基于顺序聚类的事件检测的优化并行实现

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems Pub Date : 2020-07-13 DOI: 10.1145/3401025.3401760

Gengtao Xu, Jing Qin, Runqi Tian

引用次数: 2

Real-time detection of smart meter events with odysseus 实时检测智能仪表事件与奥德修斯

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems Pub Date : 2020-07-13 DOI: 10.1145/3401025.3401757

Michael Brand, T. Brandt, M. Grawunder

引用次数: 0

Blockchain consensus unraveled: virtues and limitations 区块链共识的瓦解:优点和局限性

Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems Pub Date : 2020-07-13 DOI: 10.1145/3401025.3404099

Suyash Gupta, Jelle Hellings, Sajjad Rahnama, Mohammad Sadoghi

{"title":"Blockchain consensus unraveled: virtues and limitations","authors":"Suyash Gupta, Jelle Hellings, Sajjad Rahnama, Mohammad Sadoghi","doi":"10.1145/3401025.3404099","DOIUrl":"https://doi.org/10.1145/3401025.3404099","url":null,"abstract":"Since the introduction of Bitcoin---the first wide-spread application driven by blockchains---the interest of the public and private sector in blockchains has skyrocketed. At the core of this interest are the ways in which blockchains can be used to improve data management, e.g., by enabling federated data management via decentralization, resilience against failure and malicious actors via replication and consensus, and strong data provenance via a secured immutable ledger. In practice, high-performance blockchains for data management are usually built in permissioned environments in which the participants are vetted and can be identified. In this setting, blockchains are typically powered by Byzantine fault-tolerant consensus protocols. These consensus protocols are used to provide full replication among all honest blockchain participants by enforcing an unique order of processing incoming requests among the participants. In this tutorial, we take an in-depth look at Byzantine fault-tolerant consensus. First, we take a look at the theory behind replicated computing and consensus. Then, we delve into how common consensus protocols operate. Finally, we take a look at current developments and briefly look at our vision moving forward.","PeriodicalId":207154,"journal":{"name":"Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132136590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10