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Auflistung nach Autor:in "Zacharatou, Eleni Tzirita"

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  • Textdokument
    Towards Resilient Data Management for the Internet of Moving Things
    (BTW 2021, 2021) Paz, Elena Beatriz Ouro; Zacharatou, Eleni Tzirita; Markl, Volker
    Mobile devices have become ubiquitous; smartphones, tablets and wearables are essential commodities for many people. The ubiquity of mobile devices combined with their ever increasing capabilities, open new possibilities for Internet-of-Things (IoT) applications where mobile devices act as both data generators as well as processing nodes. However, deploying a stream processing system (SPS) over mobile devices is particularly challenging as mobile devices change their position within the network very frequently and are notoriously prone to transient disconnections. To deal with faults arising from disconnections and mobility, existing fault tolerance strategies in SPS are either checkpointing-based or replication-based. Checkpointing-based strategies are too heavyweight for mobile devices, as they save and broadcast state periodically, even when there are no failures. On the other hand, replication-based strategies cannot provide fault tolerance at the level of the data source, as the data source itself cannot be always replicated. Finally, existing systems exclude mobile devices from data processing upon a disconnection even when the duration of the disconnection is very short, thus failing to exploit the computing capabilities of the offline devices. This paper proposes a buffering-based reactive fault tolerance strategy to handle transient disconnections of mobile devices that both generate and process data, even in cases where the devices move through the network during the disconnection. The main components of our strategy are: (a) a circular buffer that stores the data which are generated and processed locally during a device disconnection, (b) a query-aware buffer replacement policy, and (c) a query restart process that ensures the correct forwarding of the buffered data upon re-connection, taking into account the new network topology. We integrate our fault tolerance strategy with NebulaStream, a novel stream processing system specifically designed for the IoT. We evaluate our strategy using a custom benchmark based on real data, exhibiting reduction in data loss and query runtime compared to the baseline NebulaStream.
  • Konferenzbeitrag
    Workload Prediction for IoT Data Management Systems
    (BTW 2023, 2023) Burrell, David; Chatziliadis, Xenofon; Zacharatou, Eleni Tzirita; Zeuch, Steffen; Markl, Volker
    The Internet of Things (IoT) is an emerging technology that allows numerous devices, potentially spread over a large geographical area, to collect and collectively process data from high-speed data streams.To that end, specialized IoT data management systems (IoTDMSs) have emerged.One challenge in those systems is the collection of different metrics from devices in a central location for analysis. This analysis allows IoTDMSs to maintain an overview of the workload on different devices and to optimize their processing. However, as an IoT network comprises of many heterogeneous devices with low computation resources and limited bandwidth, collecting and sending workload metrics can cause increased latency in data processing tasks across the network.In this ongoing work, we present an approach to avoid unnecessary transmission of workload metrics by predicting CPU, memory, and network usage using machine learning (ML).Specifically, we demonstrate the performance of two ML models, linear regression and Long Short-Term Memory (LSTM) neural network, and show the features that we explored to train these models.This work is part of an ongoing research to develop a monitoring tool for our new IoTDMS named NebulaStream.