Motivations and challenges for stream processing in edge computing

The 2030 Agenda for Sustainable Development of the United Nations General Assembly defines 17 development goals to be met for a sustainable future. Goals such as Industry, Innovation and Infrastructure and Sustainable Cities and Communities depend on digital systems. As a matter of fact, billions of Euros are invested into digital transformation within the European Union, and many researchers are actively working to push state-of-the-art boundaries for techniques/tools able to extract value and insights from the large amounts of raw data sensed in digital systems. Edge computing aims at supporting such data-to-value transformation. In digital systems that traditionally rely on central data gathering, edge computing proposes to push the analysis towards the devices and data sources, thus leveraging the large cumulative computational power found in modern distributed systems. Some of the ideas promoted in edge computing are not new, though. Continuous and distributed data analysis paradigms such as stream processing have argued about the need for smart distributed analysis for basically 20 years. Starting from this observation, this talk covers a set of standing challenges for smart, distributed, and continuous stream processing in edge computing, with real-world examples and use-cases from smart grids and vehicular networks

TinTiN: Travelling in time (if necessary) to deal with out-of-order data in streaming aggregation

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\u27 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

Data stream processing meets the Advanced Metering Infrastructure: possibilities, challenges and applications

Distribution of electricity is changing.Energy production is increasingly distributed, weather dependent and located in the distribution network, close to consumers.Energy consumption is increasing throughout society and the electrification of transportation is driving distribution networks closer to the limits.Operating the networks closer to their limits also increases the risk for faults.Continuous monitoring of the distribution network closest to the customers is needed in order to mitigate this risk.The Advanced Metering Infrastructure introduced smart meters throughout the distribution network.Data stream processing is a computing paradigm that offers low latency results from analysis on large volumes of the data.This thesis investigates the possibilities and challenges for continuous monitoring that are created when the Advanced Metering Infrastructure and data stream processing meet.The challenges that are addressed in the thesis are efficient processing of unordered (also called out-of-order) data and efficient usage of the computational resources present in the Advanced Metering Infrastructure.Contributions towards more efficient processing of out-of-order data are made with eChIDNA and TinTiN. Both are systems that utilize knowledge about smart meter data to directly produce results where possible and storing only data that is relevant for late data in order to produce updated results when such late data arrives. eChIDNA is integrated in the streaming query itself, while TinTiN is a streaming middleware that can be applied to streaming queries in order to make them resilient against out-of-order data.Eventual determinism is defined in order to formally investigate the deterministic properties of output produced by such systems.Contributions towards efficient usage of the computational resources of the Advanced Metering Infrastructure are made with the application LoCoVolt.LoCoVolt implements a monitoring algorithm that can run on equipment that is localized in the communication infrastructure of the Advanced Metering Infrastructure and can take advantage of the overlap between the communication and distribution networks.All contributions are evaluated on hardware that is available in current AMI systems, using large scale data obtained from a real production AMI

Distributed and Communication-Efficient Continuous Data Processing in Vehicular Cyber-Physical Systems

Processing the data produced by modern connected vehicles is of increasing interest for vehicle manufacturers to gain knowledge and develop novel functions and applications for the future of mobility.Connected vehicles form Vehicular Cyber-Physical Systems (VCPSs) that continuously sense increasingly large data volumes from high-bandwidth sensors such as LiDARs (an array of laser-based distance sensors that create a 3D map of the surroundings).The straightforward attempt of gathering all raw data from a VCPS to a central location for analysis often fails due to limits imposed by the infrastructure on the communication and storage capacities. In this Licentiate thesis, I present the results from my research that investigates techniques aiming at reducing the data volumes that need to be transmitted from vehicles through online compression and adaptive selection of participating vehicles. As explained in this work, the key to reducing the communication volume is in pushing parts of the necessary processing onto the vehicles\u27 on-board computers, thereby favorably leveraging the available distributed processing infrastructure in a VCPS.The findings highlight that existing analysis workflows can be sped up significantly while reducing their data volume footprint and incurring only modest accuracy decreases. At the same time, the adaptive selection of vehicles for analyses proves to provide a sufficiently large subset of vehicles that have compliant data for further analyses, while balancing the time needed for selection and the induced computational load

Maximizing determinism in stream processing under latency constraints

The problem of coping with the demands of determinism and meeting latency constraints is challenging in distributed data stream processing systems that have to process high volume data streams that arrive from different unsynchronized input sources. In order to deterministically process the streaming data, they need mechanisms that synchronize the order in which tuples are processed by the operators. On the other hand, achieving real-time response in such a system requires careful tradeoff between determinism and low latency performance. We build on a recently proposed approach to handle data exchange and synchronization in stream processing, namely ScaleGate, which comes with guarantees for determinism and an efficient lock-free implementation, enabling high scalability. Considering the challenge and trade-offs implied by real-time constraints, we propose a system which comprises (a) a novel data structure called Slack-ScaleGate (SSG), along with its algorithmic implementation; SSG enables us to guarantee the deterministic processing of tuples as long as they are able to meet their latency constraints, and (b) a method to dynamically tune the maximum amount of time that a tuple can wait in the SSG data-structure, relaxing the determinism guarantees when needed, in order to satisfy the latency constraints. Our detailed experimental evaluation using a traffic monitoring application deployed in the city of Dublin, illustrates the working and benefits of our approach

Hardware-Aware Algorithm Designs for Efficient Parallel and Distributed Processing

The introduction and widespread adoption of the Internet of Things, together with emerging new industrial applications, bring new requirements in data processing. Specifically, the need for timely processing of data that arrives at high rates creates a challenge for the traditional cloud computing paradigm, where data collected at various sources is sent to the cloud for processing. As an approach to this challenge, processing algorithms and infrastructure are distributed from the cloud to multiple tiers of computing, closer to the sources of data. This creates a wide range of devices for algorithms to be deployed on and software designs to adapt to.In this thesis, we investigate how hardware-aware algorithm designs on a variety of platforms lead to algorithm implementations that efficiently utilize the underlying resources. We design, implement and evaluate new techniques for representative applications that involve the whole spectrum of devices, from resource-constrained sensors in the field, to highly parallel servers. At each tier of processing capability, we identify key architectural features that are relevant for applications and propose designs that make use of these features to achieve high-rate, timely and energy-efficient processing.In the first part of the thesis, we focus on high-end servers and utilize two main approaches to achieve high throughput processing: vectorization and thread parallelism. We employ vectorization for the case of pattern matching algorithms used in security applications. We show that re-thinking the design of algorithms to better utilize the resources available in the platforms they are deployed on, such as vector processing units, can bring significant speedups in processing throughout. We then show how thread-aware data distribution and proper inter-thread synchronization allow scalability, especially for the problem of high-rate network traffic monitoring. We design a parallelization scheme for sketch-based algorithms that summarize traffic information, which allows them to handle incoming data at high rates and be able to answer queries on that data efficiently, without overheads.In the second part of the thesis, we target the intermediate tier of computing devices and focus on the typical examples of hardware that is found there. We show how single-board computers with embedded accelerators can be used to handle the computationally heavy part of applications and showcase it specifically for pattern matching for security-related processing. We further identify key hardware features that affect the performance of pattern matching algorithms on such devices, present a co-evaluation framework to compare algorithms, and design a new algorithm that efficiently utilizes the hardware features.In the last part of the thesis, we shift the focus to the low-power, resource-constrained tier of processing devices. We target wireless sensor networks and study distributed data processing algorithms where the processing happens on the same devices that generate the data. Specifically, we focus on a continuous monitoring algorithm (geometric monitoring) that aims to minimize communication between nodes. By deploying that algorithm in action, under realistic environments, we demonstrate that the interplay between the network protocol and the application plays an important role in this layer of devices. Based on that observation, we co-design a continuous monitoring application with a modern network stack and augment it further with an in-network aggregation technique. In this way, we show that awareness of the underlying network stack is important to realize the full potential of the continuous monitoring algorithm.The techniques and solutions presented in this thesis contribute to better utilization of hardware characteristics, across a wide spectrum of platforms. We employ these techniques on problems that are representative examples of current and upcoming applications and contribute with an outlook of emerging possibilities that can build on the results of the thesis