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Continuous distributed monitoring in the evolved packet core 19

Continuous distributed monitoring in the evolved packet core

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13th acm international conference on distributed and event-based systems, DEBS 2019

作者： Duvignau, Romaric Papatriantafilou, Marina Peratinos, Konstantinos Nordström, Eric Nyman, Patrik Chalmers University of Technology Gothenburg Sweden Ericsson intern Gothenburg Sweden Ericsson Gothenburg Sweden

ISBN: (纸本)9781450367943

For performance analysis, optimization and anomaly detection, there is strong need to monitor industrial systems, which, along modern datacenter infrastructures, feature a high level of decentralization. Continuous distributed Monitoring (CDM) corresponds to the task of continuously keeping track of statistics from different distributed nodes. We review the feasibility of implementing state-of-the-art CDM algorithms in a large-scale, distributed, performance-critical production system. the study is on the Evolved Packet Core (EPC), an inherently distributed component of the 4G architecture, for processing mobile broadband data. In this work, we propose adjustments to classical models that are needed to account for communication and computation delays and the hierarchical architecture present in production systems. We further demonstrate efficient CDM implementations in the EPC, and analyze trade-offs of accuracy versus savings in communication, as well as availability of the monitoring system. © 2019 acm.

关键词： Anomaly detection

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Kurma: Secure Geo-distributed Multi-Cloud Storage Gateways 19

Kurma: Secure Geo-Distributed Multi-Cloud Storage Gateways

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12th acm international conference on systems and Storage (SYSTOR)

作者： Chen, Ming Zadok, Erez SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9781450367493

Cloud storage is highly available, scalable, and cost-efficient. Yet, many cannot store data in cloud due to security concerns and legacy infrastructure such as network-attached storage ( NAS). We describe Kurma, a cloud storage gateway system that allows NAS-based programs to seamlessly and securely access cloud storage. To share files among distant clients, Kurma maintains a unified file-system namespace by replicating metadata across geo-distributed gateways. Kurma stores only encrypted data blocks in clouds, keeps file-system and security metadata on-premises, and can verify data integrity and freshness without any trusted third party. Kurma uses multiple clouds to prevent cloud outage and vendor lock-in. Kurma's performance is 52-91% that of a local NFS server while providing geo-replication, confidentiality, integrity, and high availability.

关键词： Multi-cloud cloud storage gateways storage security

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Uncertainty-Aware event Analytics over distributed Settings 19

Uncertainty-Aware Event Analytics over Distributed Settings

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13th acm international conference on distributed and event-based systems, DEBS 2019

作者： Giatrakos, Nikos Artikis, Alexander Deligiannakis, Antonios Garofalakis, Minos Athena Research and Innovation Center Technical University of Crete Greece University of Piraeus NCSR Demokritos Greece

ISBN: (纸本)9781450367943

In complex event processing (CEP), simple derived event tuples are combined in pattern matching procedures to derive complex events (CEs) of interest. Big Data applications analyze event streams online and extract CEs to support decision making procedures. At massive scale, such applications operate over distributed networks of sites where efficient CEP requires reducing communication as much as possible. Besides, events often encompass various types of uncertainty. therefore, massively distributed Big event Data applications in a world of uncertain events call for communication-efficient, uncertainty-aware CEP solutions, which is the focus of this work. As a proof-of-concept for the applicability of our techniques, we show how we bridge the gap between two recent CEP prototypes which use the same CEP engine and each extend it towards only one of the dimensions of distribution and uncertainty. © 2019 acm.

关键词： Pattern matching

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Search feasibility in distributed MS-proteomics big data 21

Search feasibility in distributed MS-proteomics big data

引用

proceedings of the 12th acm conference on Bioinformatics, Computational Biology, and Health Informatics

作者： Umair Mohammad Fahad Saeed Florida International University

ISBN: (纸本)9781450384506

Making large-scale Mass Spectrometry (MS) data FAIR (Findable, Accessible, Interoperable, Reusable) and democratizing access for the omics research community requires advance access and reuse mechanisms. In this work, we proposed a novel distributed data access infrastructure and developed a simulation test-bed to show the feasibility of this solution. In contrast to existing centralized approaches, participating nodes are relied upon to execute the search algorithm and search based on the comparison of raw spectra is supported as opposed to simple meta-data based searches. Simulation results using networking, stochastic modelling, and queuing theory, illustrated that search times were reduced by up-to 600 times for up-to a total of fifty billion spectra. Proteomics is vital because of the importance proteins to life and their role in state-of-the-art medicine such as custom drug delivery and cancer treatment. MS-based proteomics involves the fragmentation of proteins into peptide ions to generate raw MS spectra. Traditionally, scientists have relied on meta-data based searches of centralized repositories followed by complex database searches and protein sequencing. though useful, this technique may result in missed datasets because of poor meta-data or sheer amount of effort and computational time needed. Recently, direct raw spectra search has been proposed with the development of centralized tools such as PeptideAtlas. However, PeptideAtlas hosts 13,000 spectra whereas systems supporting billions of spectra are needed. Let us assume users can submit one or more query spectra for search to a central controller. In the proposed novel distributed paradigm, the controller will forward the queries to several nodes hosting a total of multiple MS/MS datasets, where each of the nodes will run the search algorithm against against each spectrum in their local MS/MS dataset, and send the results as URLs/pointers and associated scores back to the controller. the controller w

关键词： modelling and simulation distributed infrastructure networked database proteomics mass spectrometry big omics data spectral search

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A Database Collusion Detection and Measurement based on Bloom Filter 12

A Database Collusion Detection and Measurement Based on Bloo...

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12th international conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC 2020

作者： Ge, Chonghui Di, Yunlong Sun, Yuxin Zhu, Yongjin Xie, Linfeng Zhang, Yingzhou Jiang Su Frontier Electric Technology Co. Ltd. Nanjing China College of Computer Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728184487

In the era of rapid development of information technology brings about the protection of digital products. In order to solve the problem of traditional anti-collusion fingerprint code is difficult to detect multi-user collusion attacks, this paper proposes a method of detecting collusive users by storing the embedded attribute values of database as user characteristics. In order to further measure the collusion rate of user data, the characteristic weight value of user is included in the leaking database. the position of any embedded digital watermarking technology is taken as the feature, and the features of high efficiency and small storage of the bloom filter-is used to extract the features in the database at high speed. this paper uses a low distortion database watermarking algorithm to embed the watermark. the experimental results show that the embedded database has little distortion and can extract watermark accurately. In the case of collusion attack, this method can accurately detect collusion users and estimate the collusion rate. © 2020 IEEE.

关键词： Database systems

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AI & eBPF based performance anomaly detection system 19

AI & eBPF based performance anomaly detection system

引用

12th acm international conference on systems and Storage (SYSTOR)

作者： Ben-Yair, Ido Rogovoy, Pavel Zaidenberg, Nezer Open Univ Israel Raanana Israel Coll Management Rishon Leziyyon Israel

ISBN: (纸本)9781450367493

We describe means to run eBPF on a production environment for systems inspection. We examine the inspected system outputs in order to train and generate a model for the host. We model the specific application and network traffic usage on the site based on the data collected by eBPF. Our system generates alerts when an anomaly in performance is detected on a specific host. these warnings can be used to discover the root cause for performance problems, cyber-security issues and warn in advance about potential performance peaks.

关键词： eBPF Anomaly Detection Performance

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Self-adaptive data stream processing in geo-distributed computing environments 19

Self-adaptive data stream processing in geo-distributed comp...

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13th acm international conference on distributed and event-based systems, DEBS 2019

作者： Russo, Gabriele Russo University of Rome Tor Vergata Rome Italy

ISBN: (纸本)9781450367943

An ever increasing number of services requires real-time analysis of collected data streams. Emerging Fog/Edge computing platforms are appealing for such latency-sensitive applications, encouraging the deployment of Data Stream Processing (DSP) systems in geo-distributed environments. However, the highly dynamic nature of these infrastructures poses challenges on how to satisfy the Quality of Service requirements of both the application and the infrastructure providers. In this doctoral work we investigate how DSP systems can face the dynamicity of workloads and computing environments by self-adapting their deployment and behavior at run-time. Targeting geo-distributed infrastructures, we specifically search for decentralized solutions, and propose a framework for organizing adaptation using a hierarchical control approach. Focusing on application elasticity, we equip the framework with decentralized policies based on reinforcement learning. We extend our solution to consider multi-level elasticity, and heterogeneous computing resources. In the ongoing research work, we aim to face the challenges associated with mobility of users and computing resources, exploring complementary adaptation mechanisms. © 2019 Author.

关键词： Reinforcement learning

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Evaluation of TDM-based Integrated Access and Backhaul Schemes for 5G and beyond at mmWave Band 12

Evaluation of TDM-based Integrated Access and Backhaul Schem...

引用

12th international conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC 2020

作者： Yin, Bobo Wang, Ning Fan, Yajun Sun, Xiaofang He, Danping Liu, Wenhao School of Information Engineering Zhengzhou University Zhengzhou China State Key Lab of Rail Traffic Control Safety Beijing Jiaotong University Beijing China Wireless Product Operation ZTE Corporation Xi'an China

ISBN: (纸本)9781728184487

Integrated access and backhaul (IAB) has been introduced in 3GPP Release 16 as a promising enabling technology for densely deployed 5G small cell networks at millimeter wave (mmWave) band. In this paper, we study MIMO-IAB systems adopting TDM operation in the mmWave band for both single and multi-user scenarios. throughput expressions of the MIMO-IAB system under different antenna array configurations are derived. Practical modeling of a single-panel antenna array is discussed and the model is used in the performance evaluation of the MIMO-IAB system. Simulation results demonstrate that the TDM-based MIMO-IAB systems outperform the existing non-IAB architecture in terms of throughput when proper time allocation and power allocation coefficients are selected. It is further observed that the performance of the IAB technology largely depends on how well the inter-beam interference is managed. © 2020 IEEE.

关键词： Millimeter waves

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FlowGraph: distributed temporal pattern detection over dynamically evolving graphs 19

FlowGraph: Distributed temporal pattern detection over dynam...

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13th acm international conference on distributed and event-based systems (DEBS)

作者： Chaudhry, Hassan Nazeer Politecn Milan Milan Italy

ISBN: (纸本)9781450367943

Temporally evolving graphs are an indispensable requisite of modern-day big data processing pipelines. Existing graph processing systems mostly focus on static graphs and lack the essential support for pattern detection and event processing in graph-shaped data. On the other hand, stream processing systems support event and pattern detection, but they are inadequate for graph processing. this work lies at the intersection of the graph and stream processing domains with the following objectives: (i) It introduces the syntax of a language for the detection of temporal patterns in large-scale graphs. (ii) It presents a novel data structure called distributed label store (DLS) to efficiently store graph computation results and discover temporal patterns within them. the proposed system, called FlowGraph, unifies graph-shaped data with stream processing by observing graph changes as a stream flowing into the system. It provides an API to handle temporal patterns that predicate on the results of graph computations with traditional graph computations.

关键词： Temporal Graphs streams Pattern detection Dynamic Graphs event Processing

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Data Repair for distributed, event-based IoT Applications 19

Data Repair for Distributed, Event-based IoT Applications

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13th acm international conference on distributed and event-based systems (DEBS)

作者： Lin, Wei-Tsung Bakir, Fatih Krintz, Chandra Wolski, Rich Mock, Markus Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Univ Appl Sci Dept Comp Sci Landshut Germany

ISBN: (纸本)9781450367943

Motivated by the growth of Internet of things (IoT) technologies and the volumes and velocity of data that they can and will produce, we investigate automated data repair for event-driven, IoT applications. IoT devices are heterogeneous in their hardware architectures, software, size, cost, capacity, network capabilities, power requirements, etc. they must execute in a wide range of operating environments where failures and degradations of service due to hardware malfunction, software bugs, network partitions, etc. cannot be immediately remediated. Further, many of these failure modes cause corruption in the data that these devices produce and in the computations "downstream" that depend on this data. To "repair" corrupted data from its origin through its computational dependencies in a distributed IoT setting, we explore SANS-SOUCI - a system for automatically tracking causal data dependencies and re-initiating dependent computations in event-driven IoT deployment frameworks. SANS-SOUCI presupposes an event-driven programming model based on cloud functions, which we extend for portable execution across IoT tiers (device, edge, cloud). We add fast, persistent, append-only storage and versioning for efficient data robustness and durability. SANS-SOUCI records events and their causal dependencies using a distributed event log and repairs applications dynamically, across tiers via replay. We evaluate SANS-SOUCI using a portable, open source, distributed IoT platform, example applications, and microbenchmarks. We find that SANS-SOUCI is able to perform repair for both software (function) and sensor produced data corruption with very low overhead.

关键词： replay IoT serverless cloud functions

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